eval

Prompt:
Implement the Python class `Interactions` described below.

Class description:
Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all thoses interactions 5 following types of interactions B : local basis transformation matrix (unitary) L : LOS case R : Reflection T : Transmission D : Diffraction

Method signatures and docstrings:
- def __init__(self, slab={}): object constructor
- def add(self, li): add a list of interactions Parameters ---------- li : list list of interactions
- def addi(self, i): add interactions into Interactions class Parameters ---------- i : Inter object
- def eval(self, fGHz=np.array([2.4])): evaluate all the interactions Parameters ---------- fGHz : np.array() Notes ----- self.I : np.shape(self.I) = (self.nf,self.nimax,2,2) with self.nf : number of frequences self.nimax : the total number of interactions ( of all rays) self.sout : distance from one interaction to the next one self.si0 : distance from the previous interaction to the one self.alpha : alpha as described in JFL Thesis self.gamma : !! gamma**2 !!! (squared included) as described

Implement the Python class `Interactions` described below.

Class description:
Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all thoses interactions 5 following types of interactions B : local basis transformation matrix (unitary) L : LOS case R : Reflection T : Transmission D : Diffraction

Method signatures and docstrings:
- def __init__(self, slab={}): object constructor
- def add(self, li): add a list of interactions Parameters ---------- li : list list of interactions
- def addi(self, i): add interactions into Interactions class Parameters ---------- i : Inter object
- def eval(self, fGHz=np.array([2.4])): evaluate all the interactions Parameters ---------- fGHz : np.array() Notes ----- self.I : np.shape(self.I) = (self.nf,self.nimax,2,2) with self.nf : number of frequences self.nimax : the total number of interactions ( of all rays) self.sout : distance from one interaction to the next one self.si0 : distance from the previous interaction to the one self.alpha : alpha as described in JFL Thesis self.gamma : !! gamma**2 !!! (squared included) as described

<|skeleton|>
class Interactions:
    """Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all thoses interactions 5 following types of interactions B : local basis transformation matrix (unitary) L : LOS case R : Reflection T : Transmission D : Diffraction"""

    def __init__(self, slab={}):
        """object constructor"""
        <|body_0|>

    def add(self, li):
        """add a list of interactions Parameters ---------- li : list list of interactions"""
        <|body_1|>

    def addi(self, i):
        """add interactions into Interactions class Parameters ---------- i : Inter object"""
        <|body_2|>

    def eval(self, fGHz=np.array([2.4])):
        """evaluate all the interactions Parameters ---------- fGHz : np.array() Notes ----- self.I : np.shape(self.I) = (self.nf,self.nimax,2,2) with self.nf : number of frequences self.nimax : the total number of interactions ( of all rays) self.sout : distance from one interaction to the next one self.si0 : distance from the previous interaction to the one self.alpha : alpha as described in JFL Thesis self.gamma : !! gamma**2 !!! (squared included) as described"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        Inter.__init__(self, slab=slab)
        self['B'] = []
        self['L'] = []
        self[''] = []
        self['T'] = []
        self['D'] = []
        self.evaluated = False
        self.nimax = 0
<|end_body_0|>

<|body_start_1|>
        for i in li:
            if i.idx != []:
                self.nimax = max(self.nimax, max(i.idx)) + 1
        for i in li:
            self.addi(i)
<|end_body_1|>

<|body_start_2|>
        if not isinstance(self.typ, np.ndarray):
            self.typ = np.zeros(self.nimax, dtype=str)
        if i.typ == -1:
            self.B = i
            self['B'] = i.idx
            self.typ[i.idx] = 'B'
        if i.typ == 0:
            self.L = i
            self['L'] = i.idx
            self.typ[i.idx] = 'L'
        if i.typ == 1:
            self.D = i
            self['D'] = i.idx
            self.typ[i.idx] = 'D'
        if i.typ == 2:
            self.R = i
            self['R'] = i.idx
            self.typ[i.idx] = 'R'
        if i.typ == 3:
            self.T = i
            self['T'] = i.idx
            self.typ[i.idx] = 'T'
<|end_body_2|>

<|body_start_3|>
        self.fGHz = fGHz
        self.nf = len(fGHz)
        self.I = np.zeros((self.nf, self.nimax, 3, 3), dtype=complex)
        self.sout = np.zeros(self.nimax)
        self.si0 = np.zeros(self.nimax)
        self.alpha = np.ones(self.nimax, dtype=complex)
        self.gamma = np.ones(self.nimax, dtype=complex)
        if len(self.R.data) != 0:
            self.I[:, self.R.idx, :, :] = self.R.eval(fGHz=fGHz)
            self.sout[self.R.idx] = self.R.sout
            self.si0[self.R.idx] = self.R.si0
            self.alpha[self.R.idx] = self.R.alpha
            self.gamma[self.R.idx] = self.R.gamma
        if len(self.T.data) != 0:
            self.I[:, self.T.idx, :, :] = self.T.eval(fGHz=fGHz)
            self.sout[self.T.idx] = self.T.sout
            self.si0[self.T.idx] = self.T.si0
            self.alpha[self.T.idx] = self.T.alpha
            self.gamma[self.T.idx] = self.T.gamma
        if len(self.D.data) != 0:
            self.I[:, self.D.idx, :, :] = self.D.eval(fGHz=fGHz)
            self.sout[self.D.idx] = self.D.sout
            self.si0[self.D.idx] = self.D.si0
        self.evaluated = True
<|end_body_3|>

Solution:
class Interactions:
    """Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all thoses interactions 5 following types of interactions B : local basis transformation matrix (unitary) L : LOS case R : Reflection T : Transmission D : Diffraction"""

    def __init__(self, slab={}):
        """object constructor"""
        Inter.__init__(self, slab=slab)
        self['B'] = []
        self['L'] = []
        self[''] = []
        self['T'] = []
        self['D'] = []
        self.evaluated = False
        self.nimax = 0

    def add(self, li):
        """add a list of interactions Parameters ---------- li : list list of interactions"""
        for i in li:
            if i.idx != []:
                self.nimax = max(self.nimax, max(i.idx)) + 1
        for i in li:
            self.addi(i)

    def addi(self, i):
        """add interactions into Interactions class Parameters ---------- i : Inter object"""
        if not isinstance(self.typ, np.ndarray):
            self.typ = np.zeros(self.nimax, dtype=str)
        if i.typ == -1:
            self.B = i
            self['B'] = i.idx
            self.typ[i.idx] = 'B'
        if i.typ == 0:
            self.L = i
            self['L'] = i.idx
            self.typ[i.idx] = 'L'
        if i.typ == 1:
            self.D = i
            self['D'] = i.idx
            self.typ[i.idx] = 'D'
        if i.typ == 2:
            self.R = i
            self['R'] = i.idx
            self.typ[i.idx] = 'R'
        if i.typ == 3:
            self.T = i
            self['T'] = i.idx
            self.typ[i.idx] = 'T'

    def eval(self, fGHz=np.array([2.4])):
        """evaluate all the interactions Parameters ---------- fGHz : np.array() Notes ----- self.I : np.shape(self.I) = (self.nf,self.nimax,2,2) with self.nf : number of frequences self.nimax : the total number of interactions ( of all rays) self.sout : distance from one interaction to the next one self.si0 : distance from the previous interaction to the one self.alpha : alpha as described in JFL Thesis self.gamma : !! gamma**2 !!! (squared included) as described"""
        self.fGHz = fGHz
        self.nf = len(fGHz)
        self.I = np.zeros((self.nf, self.nimax, 3, 3), dtype=complex)
        self.sout = np.zeros(self.nimax)
        self.si0 = np.zeros(self.nimax)
        self.alpha = np.ones(self.nimax, dtype=complex)
        self.gamma = np.ones(self.nimax, dtype=complex)
        if len(self.R.data) != 0:
            self.I[:, self.R.idx, :, :] = self.R.eval(fGHz=fGHz)
            self.sout[self.R.idx] = self.R.sout
            self.si0[self.R.idx] = self.R.si0
            self.alpha[self.R.idx] = self.R.alpha
            self.gamma[self.R.idx] = self.R.gamma
        if len(self.T.data) != 0:
            self.I[:, self.T.idx, :, :] = self.T.eval(fGHz=fGHz)
            self.sout[self.T.idx] = self.T.sout
            self.si0[self.T.idx] = self.T.si0
            self.alpha[self.T.idx] = self.T.alpha
            self.gamma[self.T.idx] = self.T.gamma
        if len(self.D.data) != 0:
            self.I[:, self.D.idx, :, :] = self.D.eval(fGHz=fGHz)
            self.sout[self.D.idx] = self.D.sout
            self.si0[self.D.idx] = self.D.si0
        self.evaluated = True


pop

Prompt:
Implement the Python class `MyQueue` described below.

Class description:
Implement the MyQueue class.

Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def push(self, x: int) -> None: Push element x to the back of queue.
- def pop(self) -> int: Removes the element from in front of queue and returns that element.
- def peek(self) -> int: Get the front element.
- def empty(self) -> bool: Returns whether the queue is empty.

Implement the Python class `MyQueue` described below.

Class description:
Implement the MyQueue class.

Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def push(self, x: int) -> None: Push element x to the back of queue.
- def pop(self) -> int: Removes the element from in front of queue and returns that element.
- def peek(self) -> int: Get the front element.
- def empty(self) -> bool: Returns whether the queue is empty.

<|skeleton|>
class MyQueue:

    def __init__(self):
        """Initialize your data structure here."""
        <|body_0|>

    def push(self, x: int) -> None:
        """Push element x to the back of queue."""
        <|body_1|>

    def pop(self) -> int:
        """Removes the element from in front of queue and returns that element."""
        <|body_2|>

    def peek(self) -> int:
        """Get the front element."""
        <|body_3|>

    def empty(self) -> bool:
        """Returns whether the queue is empty."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.s1 = []
        self.s2 = []
        self.front = None
<|end_body_0|>

<|body_start_1|>
        if not self.s1:
            self.front = x
        self.s1.append(x)
<|end_body_1|>

<|body_start_2|>
        if not self.s2:
            while self.s1:
                self.s2.append(self.s1.pop())
        return self.s2.pop()
<|end_body_2|>

<|body_start_3|>
        if self.s2:
            return self.s2[-1]
        return self.front
<|end_body_3|>

<|body_start_4|>
        if not self.s1 and (not self.s2):
            return True
        return False
<|end_body_4|>

Solution:
class MyQueue:
    def __init__(self):
        """Initialize your data structure here."""
        self.s1 = []
        self.s2 = []
        self.front = None

    def push(self, x: int) -> None:
        """Push element x to the back of queue."""
        if not self.s1:
            self.front = x
        self.s1.append(x)

    def pop(self) -> int:
        """Removes the element from in front of queue and returns that element."""
        if not self.s2:
            while self.s1:
                self.s2.append(self.s1.pop())
        return self.s2.pop()

    def peek(self) -> int:
        """Get the front element."""
        if self.s2:
            return self.s2[-1]
        return self.front

    def empty(self) -> bool:
        """Returns whether the queue is empty."""
        if not self.s1 and (not self.s2):
            return True
        return False


Prompt:
Implement the Python class `SpectralMaskEnhancement` described below.

Class description:
A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> enhancer = SpectralMaskEnhancement.from_hparams( ... source="speechbrain/mtl-mimic-voicebank", ... savedir=tmpdir, ... ) >>> noisy, fs = torchaudio.load("samples/audio_samples/example_noisy.wav") >>> # Channel dimension is interpreted as batch dimension here >>> enhanced = enhancer.enhance_batch(noisy)

Method signatures and docstrings:
- def compute_features(self, wavs): Compute the log spectral magnitude features for masking. Arguments --------- wavs : torch.tensor A batch of waveforms to convert to log spectral mags.
- def enhance_batch(self, noisy, lengths=None): Enhance a batch of noisy waveforms. Arguments --------- noisy : torch.tensor A batch of waveforms to perform enhancement on. lengths : torch.tensor The lengths of the waveforms if the enhancement model handles them. Returns ------- torch.tensor A batch of enhanced waveforms of the same shape as input.
- def enhance_file(self, filename, output_filename=None): Enhance a wav file. Arguments --------- filename : str Location on disk to load file for enhancement. output_filename : str If provided, writes enhanced data to this file.

Implement the Python class `SpectralMaskEnhancement` described below.

Class description:
A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> enhancer = SpectralMaskEnhancement.from_hparams( ... source="speechbrain/mtl-mimic-voicebank", ... savedir=tmpdir, ... ) >>> noisy, fs = torchaudio.load("samples/audio_samples/example_noisy.wav") >>> # Channel dimension is interpreted as batch dimension here >>> enhanced = enhancer.enhance_batch(noisy)

Method signatures and docstrings:
- def compute_features(self, wavs): Compute the log spectral magnitude features for masking. Arguments --------- wavs : torch.tensor A batch of waveforms to convert to log spectral mags.
- def enhance_batch(self, noisy, lengths=None): Enhance a batch of noisy waveforms. Arguments --------- noisy : torch.tensor A batch of waveforms to perform enhancement on. lengths : torch.tensor The lengths of the waveforms if the enhancement model handles them. Returns ------- torch.tensor A batch of enhanced waveforms of the same shape as input.
- def enhance_file(self, filename, output_filename=None): Enhance a wav file. Arguments --------- filename : str Location on disk to load file for enhancement. output_filename : str If provided, writes enhanced data to this file.

<|skeleton|>
class SpectralMaskEnhancement:
    """A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> enhancer = SpectralMaskEnhancement.from_hparams( ... source="speechbrain/mtl-mimic-voicebank", ... savedir=tmpdir, ... ) >>> noisy, fs = torchaudio.load("samples/audio_samples/example_noisy.wav") >>> # Channel dimension is interpreted as batch dimension here >>> enhanced = enhancer.enhance_batch(noisy)"""

    def compute_features(self, wavs):
        """Compute the log spectral magnitude features for masking. Arguments --------- wavs : torch.tensor A batch of waveforms to convert to log spectral mags."""
        <|body_0|>

    def enhance_batch(self, noisy, lengths=None):
        """Enhance a batch of noisy waveforms. Arguments --------- noisy : torch.tensor A batch of waveforms to perform enhancement on. lengths : torch.tensor The lengths of the waveforms if the enhancement model handles them. Returns ------- torch.tensor A batch of enhanced waveforms of the same shape as input."""
        <|body_1|>

    def enhance_file(self, filename, output_filename=None):
        """Enhance a wav file. Arguments --------- filename : str Location on disk to load file for enhancement. output_filename : str If provided, writes enhanced data to this file."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        feats = self.hparams.compute_stft(wavs)
        feats = self.hparams.spectral_magnitude(feats)
        return torch.log1p(feats)
<|end_body_0|>

<|body_start_1|>
        noisy = noisy.to(self.device)
        noisy_features = self.compute_features(noisy)
        if lengths is not None:
            mask = self.modules.enhance_model(noisy_features, lengths=lengths)
        else:
            mask = self.modules.enhance_model(noisy_features)
        enhanced = torch.mul(mask, noisy_features)
        return self.hparams.resynth(torch.expm1(enhanced), noisy)
<|end_body_1|>

<|body_start_2|>
        noisy = self.load_audio(filename)
        noisy = noisy.to(self.device)
        batch = noisy.unsqueeze(0)
        enhanced = self.enhance_batch(batch)
        if output_filename is not None:
            torchaudio.save(output_filename, enhanced, channels_first=False)
        return enhanced.squeeze(0)
<|end_body_2|>

Solution:
class SpectralMaskEnhancement:
    """A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> enhancer = SpectralMaskEnhancement.from_hparams( ... source="speechbrain/mtl-mimic-voicebank", ... savedir=tmpdir, ... ) >>> noisy, fs = torchaudio.load("samples/audio_samples/example_noisy.wav") >>> # Channel dimension is interpreted as batch dimension here >>> enhanced = enhancer.enhance_batch(noisy)"""

    def compute_features(self, wavs):
        """Compute the log spectral magnitude features for masking. Arguments --------- wavs : torch.tensor A batch of waveforms to convert to log spectral mags."""
        feats = self.hparams.compute_stft(wavs)
        feats = self.hparams.spectral_magnitude(feats)
        return torch.log1p(feats)

    def enhance_batch(self, noisy, lengths=None):
        """Enhance a batch of noisy waveforms. Arguments --------- noisy : torch.tensor A batch of waveforms to perform enhancement on. lengths : torch.tensor The lengths of the waveforms if the enhancement model handles them. Returns ------- torch.tensor A batch of enhanced waveforms of the same shape as input."""
        noisy = noisy.to(self.device)
        noisy_features = self.compute_features(noisy)
        if lengths is not None:
            mask = self.modules.enhance_model(noisy_features, lengths=lengths)
        else:
            mask = self.modules.enhance_model(noisy_features)
        enhanced = torch.mul(mask, noisy_features)
        return self.hparams.resynth(torch.expm1(enhanced), noisy)

    def enhance_file(self, filename, output_filename=None):
        """Enhance a wav file. Arguments --------- filename : str Location on disk to load file for enhancement. output_filename : str If provided, writes enhanced data to this file."""
        noisy = self.load_audio(filename)
        noisy = noisy.to(self.device)
        batch = noisy.unsqueeze(0)
        enhanced = self.enhance_batch(batch)
        if output_filename is not None:
            torchaudio.save(output_filename, enhanced, channels_first=False)
        return enhanced.squeeze(0)


Prompt:
Implement the Python class `ScannerEngine` described below.

Class description:
ScannerEngine class.

Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None): Initialize ScannerEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None
- def start_scanner_engine(self): Start the scanner engine and stat scanning the files using three (3) engines in a multi-processing environment. 1. Hash Scanner Engine 2. Yara Scanner Engine 3. Clam AV Scanner Engine Args: None Raises: None Returns: None

Implement the Python class `ScannerEngine` described below.

Class description:
ScannerEngine class.

Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None): Initialize ScannerEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None
- def start_scanner_engine(self): Start the scanner engine and stat scanning the files using three (3) engines in a multi-processing environment. 1. Hash Scanner Engine 2. Yara Scanner Engine 3. Clam AV Scanner Engine Args: None Raises: None Returns: None

<|skeleton|>
class ScannerEngine:
    """ScannerEngine class."""

    def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None):
        """Initialize ScannerEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None"""
        <|body_0|>

    def start_scanner_engine(self):
        """Start the scanner engine and stat scanning the files using three (3) engines in a multi-processing environment. 1. Hash Scanner Engine 2. Yara Scanner Engine 3. Clam AV Scanner Engine Args: None Raises: None Returns: None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.logger = AntiVirusLogger(__name__, debug=debug)
        if config_path is not None:
            self._CONFIG_PATH = config_path
        else:
            self.logger.log('Configuration file path not found.', logtype='error')
            sys.exit(0)
        if file_list:
            self.file_list = file_list
        else:
            self.file_list = []
        self.hash_scanner = HashScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.yara_scanner = YaraScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.clamd_scanner = ClamAVScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.process_pool = []
<|end_body_0|>

<|body_start_1|>
        try:
            hash_scanner_process = multiprocessing.Process(target=self.hash_scanner.start_scan)
            yara_scanner_process = multiprocessing.Process(target=self.yara_scanner.start_scan)
            clamd_scanner_process = multiprocessing.Process(target=self.clamd_scanner.start_scan)
            self.process_pool.append(hash_scanner_process)
            self.process_pool.append(yara_scanner_process)
            self.process_pool.append(clamd_scanner_process)
            hash_scanner_process.start()
            self.logger.log('Hash Scanner engine started', logtype='info')
            yara_scanner_process.start()
            self.logger.log('Yara Scanner engine started', logtype='info')
            clamd_scanner_process.start()
            self.logger.log('Clam AV Scanner engine started', logtype='info')
            for process in self.process_pool:
                process.join()
                return True
        except KeyboardInterrupt:
            for process in self.process_pool:
                process.terminate()
                return True
        except Exception as e:
            self.logger.log('Error occurred: ' + str(e), logtype='error')
            return True
<|end_body_1|>

Solution:
class ScannerEngine:
    """ScannerEngine class."""

    def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None):
        """Initialize ScannerEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None"""
        self.logger = AntiVirusLogger(__name__, debug=debug)
        if config_path is not None:
            self._CONFIG_PATH = config_path
        else:
            self.logger.log('Configuration file path not found.', logtype='error')
            sys.exit(0)
        if file_list:
            self.file_list = file_list
        else:
            self.file_list = []
        self.hash_scanner = HashScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.yara_scanner = YaraScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.clamd_scanner = ClamAVScanner(debug=debug, config_path=self._CONFIG_PATH, file_list=self.file_list, vt_api_key=vt_api_key)
        self.process_pool = []

    def start_scanner_engine(self):
        """Start the scanner engine and stat scanning the files using three (3) engines in a multi-processing environment. 1. Hash Scanner Engine 2. Yara Scanner Engine 3. Clam AV Scanner Engine Args: None Raises: None Returns: None"""
        try:
            hash_scanner_process = multiprocessing.Process(target=self.hash_scanner.start_scan)
            yara_scanner_process = multiprocessing.Process(target=self.yara_scanner.start_scan)
            clamd_scanner_process = multiprocessing.Process(target=self.clamd_scanner.start_scan)
            self.process_pool.append(hash_scanner_process)
            self.process_pool.append(yara_scanner_process)
            self.process_pool.append(clamd_scanner_process)
            hash_scanner_process.start()
            self.logger.log('Hash Scanner engine started', logtype='info')
            yara_scanner_process.start()
            self.logger.log('Yara Scanner engine started', logtype='info')
            clamd_scanner_process.start()
            self.logger.log('Clam AV Scanner engine started', logtype='info')
            for process in self.process_pool:
                process.join()
                return True
        except KeyboardInterrupt:
            for process in self.process_pool:
                process.terminate()
                return True
        except Exception as e:
            self.logger.log('Error occurred: ' + str(e), logtype='error')
            return True


conversions

Prompt:
Implement the Python class `BaseConverter` described below.

Class description:
A creator of unit-conversion dictionaries

Method signatures and docstrings:
- def __init__(self, to_units, kilo_prefix): base_converter constructor :param: - `to_units`: a list of the units to covert to (has to be half to-bits, half to-bytes) - `kilo_prefix`: kilo multiplier matching type of units
- def prefix_conversions(self): List of lists of prefix conversions
- def bits_to_bytes(self): List of conversions for bits to bytes
- def bytes_to_bits(self): list of conversions for bytes to bits
- def conversions(self, conversion_factor, start_list=None): Creates the converter-lists :param: - `conversion_factor`: multiplier for values (8 or 1/8, or 1) - `start_list`: if given, use to start the conversion-list :return: list of conversion_lists
- def build_conversions(self): builds the dictionary

Implement the Python class `BaseConverter` described below.

Class description:
A creator of unit-conversion dictionaries

Method signatures and docstrings:
- def __init__(self, to_units, kilo_prefix): base_converter constructor :param: - `to_units`: a list of the units to covert to (has to be half to-bits, half to-bytes) - `kilo_prefix`: kilo multiplier matching type of units
- def prefix_conversions(self): List of lists of prefix conversions
- def bits_to_bytes(self): List of conversions for bits to bytes
- def bytes_to_bits(self): list of conversions for bytes to bits
- def conversions(self, conversion_factor, start_list=None): Creates the converter-lists :param: - `conversion_factor`: multiplier for values (8 or 1/8, or 1) - `start_list`: if given, use to start the conversion-list :return: list of conversion_lists
- def build_conversions(self): builds the dictionary

<|skeleton|>
class BaseConverter:
    """A creator of unit-conversion dictionaries"""

    def __init__(self, to_units, kilo_prefix):
        """base_converter constructor :param: - `to_units`: a list of the units to covert to (has to be half to-bits, half to-bytes) - `kilo_prefix`: kilo multiplier matching type of units"""
        <|body_0|>

    def prefix_conversions(self):
        """List of lists of prefix conversions"""
        <|body_1|>

    def bits_to_bytes(self):
        """List of conversions for bits to bytes"""
        <|body_2|>

    def bytes_to_bits(self):
        """list of conversions for bytes to bits"""
        <|body_3|>

    def conversions(self, conversion_factor, start_list=None):
        """Creates the converter-lists :param: - `conversion_factor`: multiplier for values (8 or 1/8, or 1) - `start_list`: if given, use to start the conversion-list :return: list of conversion_lists"""
        <|body_4|>

    def build_conversions(self):
        """builds the dictionary"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        self.to_units = to_units
        self.kilo_prefix = kilo_prefix
        self._prefix_conversions = None
        self._bits_to_bytes = None
        self._bytes_to_bits = None
        self.bit_conversions = self.byte_conversions = len(to_units) // 2
        self.bit_units = to_units[:self.bit_conversions]
        self.byte_units = to_units[self.byte_conversions:]
        return
<|end_body_0|>

<|body_start_1|>
        if self._prefix_conversions is None:
            start_list = [self.kilo_prefix ** (-power) for power in range(self.bit_conversions)]
            self._prefix_conversions = self.conversions(conversion_factor=1, start_list=start_list)
        return self._prefix_conversions
<|end_body_1|>

<|body_start_2|>
        if self._bits_to_bytes is None:
            self._bits_to_bytes = self.conversions(conversion_factor=TO_BYTE)
        return self._bits_to_bytes
<|end_body_2|>

<|body_start_3|>
        if self._bytes_to_bits is None:
            self._bytes_to_bits = self.conversions(conversion_factor=BYTE)
        return self._bytes_to_bits
<|end_body_3|>

<|body_start_4|>
        if start_list is None:
            start_list = self.prefix_conversions[0]
        converter_list = [[conversion_factor * conversion for conversion in start_list]]
        for previous in range(self.bit_conversions - 1):
            next_conversions = [self.kilo_prefix ** (previous + 1) * conversion_factor] + converter_list[previous][:-1]
            converter_list.append(next_conversions)
        return converter_list
<|end_body_4|>

<|body_start_5|>
        for index, units in enumerate(self.bit_units):
            self[units] = dict(list(zip(self.to_units, self.prefix_conversions[index] + self.bits_to_bytes[index])))
        for index, units in enumerate(self.byte_units):
            self[units] = dict(list(zip(self.to_units, self.bytes_to_bits[index] + self.prefix_conversions[index])))
        return
<|end_body_5|>

Solution:
class BaseConverter:
    """A creator of unit-conversion dictionaries"""

    def __init__(self, to_units, kilo_prefix):
        """base_converter constructor :param: - `to_units`: a list of the units to covert to (has to be half to-bits, half to-bytes) - `kilo_prefix`: kilo multiplier matching type of units"""
        self.to_units = to_units
        self.kilo_prefix = kilo_prefix
        self._prefix_conversions = None
        self._bits_to_bytes = None
        self._bytes_to_bits = None
        self.bit_conversions = self.byte_conversions = len(to_units) // 2
        self.bit_units = to_units[:self.bit_conversions]
        self.byte_units = to_units[self.byte_conversions:]
        return

    def prefix_conversions(self):
        """List of lists of prefix conversions"""
        if self._prefix_conversions is None:
            start_list = [self.kilo_prefix ** (-power) for power in range(self.bit_conversions)]
            self._prefix_conversions = self.conversions(conversion_factor=1, start_list=start_list)
        return self._prefix_conversions

    def bits_to_bytes(self):
        """List of conversions for bits to bytes"""
        if self._bits_to_bytes is None:
            self._bits_to_bytes = self.conversions(conversion_factor=TO_BYTE)
        return self._bits_to_bytes

    def bytes_to_bits(self):
        """list of conversions for bytes to bits"""
        if self._bytes_to_bits is None:
            self._bytes_to_bits = self.conversions(conversion_factor=BYTE)
        return self._bytes_to_bits

    def conversions(self, conversion_factor, start_list=None):
        """Creates the converter-lists :param: - `conversion_factor`: multiplier for values (8 or 1/8, or 1) - `start_list`: if given, use to start the conversion-list :return: list of conversion_lists"""
        if start_list is None:
            start_list = self.prefix_conversions[0]
        converter_list = [[conversion_factor * conversion for conversion in start_list]]
        for previous in range(self.bit_conversions - 1):
            next_conversions = [self.kilo_prefix ** (previous + 1) * conversion_factor] + converter_list[previous][:-1]
            converter_list.append(next_conversions)
        return converter_list

    def build_conversions(self):
        """builds the dictionary"""
        for index, units in enumerate(self.bit_units):
            self[units] = dict(list(zip(self.to_units, self.prefix_conversions[index] + self.bits_to_bytes[index])))
        for index, units in enumerate(self.byte_units):
            self[units] = dict(list(zip(self.to_units, self.bytes_to_bits[index] + self.prefix_conversions[index])))
        return


GetDictOfCommitPositionAndRevision

Prompt:
Implement the Python class `DataPoint` described below.

Class description:
Implement the DataPoint class.

Method signatures and docstrings:
- def GetCommitPosition(self, revision): Gets the commit position of a revision within blame_list. Args: revision (str): The revision to search for. Returns: commit_position (int): The calculated commit position of revision.
- def GetRevisionAtCommitPosition(self, commit_position): Gets the corresponding revision to commit_position. Args: commit_position (int): The commit position for which to find the corresponding revision within self.blame_list. Returns: revision (str): The git revision corresponding to commit_position.
- def GetDictOfCommitPositionAndRevision(self): Gets a dict of commit_position:revision items for this data_point.

Implement the Python class `DataPoint` described below.

Class description:
Implement the DataPoint class.

Method signatures and docstrings:
- def GetCommitPosition(self, revision): Gets the commit position of a revision within blame_list. Args: revision (str): The revision to search for. Returns: commit_position (int): The calculated commit position of revision.
- def GetRevisionAtCommitPosition(self, commit_position): Gets the corresponding revision to commit_position. Args: commit_position (int): The commit position for which to find the corresponding revision within self.blame_list. Returns: revision (str): The git revision corresponding to commit_position.
- def GetDictOfCommitPositionAndRevision(self): Gets a dict of commit_position:revision items for this data_point.

<|skeleton|>
class DataPoint:

    def GetCommitPosition(self, revision):
        """Gets the commit position of a revision within blame_list. Args: revision (str): The revision to search for. Returns: commit_position (int): The calculated commit position of revision."""
        <|body_0|>

    def GetRevisionAtCommitPosition(self, commit_position):
        """Gets the corresponding revision to commit_position. Args: commit_position (int): The commit position for which to find the corresponding revision within self.blame_list. Returns: revision (str): The git revision corresponding to commit_position."""
        <|body_1|>

    def GetDictOfCommitPositionAndRevision(self):
        """Gets a dict of commit_position:revision items for this data_point."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        assert revision in self.blame_list
        for i in range(0, len(self.blame_list)):
            if revision == self.blame_list[i]:
                return i + self.previous_build_commit_position + 1
<|end_body_0|>

<|body_start_1|>
        length = len(self.blame_list)
        assert commit_position > self.commit_position - length and commit_position <= self.commit_position
        return self.blame_list[length - (self.commit_position - commit_position) - 1]
<|end_body_1|>

<|body_start_2|>
        blamed_cls = {}
        commit_position = self.commit_position
        for i in xrange(len(self.blame_list) - 1, -1, -1):
            blamed_cls[commit_position] = self.blame_list[i]
            commit_position -= 1
        return blamed_cls
<|end_body_2|>

Solution:
class DataPoint:
    def GetCommitPosition(self, revision):
        """Gets the commit position of a revision within blame_list. Args: revision (str): The revision to search for. Returns: commit_position (int): The calculated commit position of revision."""
        assert revision in self.blame_list
        for i in range(0, len(self.blame_list)):
            if revision == self.blame_list[i]:
                return i + self.previous_build_commit_position + 1

    def GetRevisionAtCommitPosition(self, commit_position):
        """Gets the corresponding revision to commit_position. Args: commit_position (int): The commit position for which to find the corresponding revision within self.blame_list. Returns: revision (str): The git revision corresponding to commit_position."""
        length = len(self.blame_list)
        assert commit_position > self.commit_position - length and commit_position <= self.commit_position
        return self.blame_list[length - (self.commit_position - commit_position) - 1]

    def GetDictOfCommitPositionAndRevision(self):
        """Gets a dict of commit_position:revision items for this data_point."""
        blamed_cls = {}
        commit_position = self.commit_position
        for i in xrange(len(self.blame_list) - 1, -1, -1):
            blamed_cls[commit_position] = self.blame_list[i]
            commit_position -= 1
        return blamed_cls


run

Prompt:
Implement the Python class `TimeoutThread` described below.

Class description:
A Thread which can be used to timeout

Method signatures and docstrings:
- def __init__(self, func, *args, **kwargs) -> None: Construct a Timeout Thread :param func: The function being called :param args: The arguments :param kwargs: The key word arguments
- def run(self) -> None: Call the function with the arguments to get the result Effect: Modifies the result of the function call

Implement the Python class `TimeoutThread` described below.

Class description:
A Thread which can be used to timeout

Method signatures and docstrings:
- def __init__(self, func, *args, **kwargs) -> None: Construct a Timeout Thread :param func: The function being called :param args: The arguments :param kwargs: The key word arguments
- def run(self) -> None: Call the function with the arguments to get the result Effect: Modifies the result of the function call

<|skeleton|>
class TimeoutThread:
    """A Thread which can be used to timeout"""

    def __init__(self, func, *args, **kwargs) -> None:
        """Construct a Timeout Thread :param func: The function being called :param args: The arguments :param kwargs: The key word arguments"""
        <|body_0|>

    def run(self) -> None:
        """Call the function with the arguments to get the result Effect: Modifies the result of the function call"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        threading.Thread.__init__(self)
        self.func = func
        self.args = args
        self.kwargs = kwargs
        self.result = NoResult
<|end_body_0|>

<|body_start_1|>
        try:
            self.result = self.func(*self.args, **self.kwargs)
        except Exception as e:
            self.result = NoResult
<|end_body_1|>

Solution:
class TimeoutThread:
    """A Thread which can be used to timeout"""

    def __init__(self, func, *args, **kwargs) -> None:
        """Construct a Timeout Thread :param func: The function being called :param args: The arguments :param kwargs: The key word arguments"""
        threading.Thread.__init__(self)
        self.func = func
        self.args = args
        self.kwargs = kwargs
        self.result = NoResult

    def run(self) -> None:
        """Call the function with the arguments to get the result Effect: Modifies the result of the function call"""
        try:
            self.result = self.func(*self.args, **self.kwargs)
        except Exception as e:
            self.result = NoResult


step

Prompt:
Implement the Python class `PressurizedBountifulSeaTreasure` described below.

Class description:
Implement the PressurizedBountifulSeaTreasure class.

Method signatures and docstrings:
- def __init__(self, initial_state: tuple=(0, 0), default_reward: tuple=(0,), seed: int=0, columns: int=0, action_space: gym.spaces=None): :param initial_state: Initial state where start the agent. :param default_reward: (treasure_value, ) :param seed: Seed used for np.random.RandomState method.
- def step(self, action: int) -> (tuple, Vector, bool, dict): Given an action, do a step :param action: :return: (position, (time_inverted, treasure_value), final, extra)
- def transition_reward(self, state: tuple, action: int, next_state: tuple) -> Vector: Return reward for reach `next_state` from `position` using `action`. :param state: initial position :param action: action to do :param next_state: next position reached :return:

Implement the Python class `PressurizedBountifulSeaTreasure` described below.

Class description:
Implement the PressurizedBountifulSeaTreasure class.

Method signatures and docstrings:
- def __init__(self, initial_state: tuple=(0, 0), default_reward: tuple=(0,), seed: int=0, columns: int=0, action_space: gym.spaces=None): :param initial_state: Initial state where start the agent. :param default_reward: (treasure_value, ) :param seed: Seed used for np.random.RandomState method.
- def step(self, action: int) -> (tuple, Vector, bool, dict): Given an action, do a step :param action: :return: (position, (time_inverted, treasure_value), final, extra)
- def transition_reward(self, state: tuple, action: int, next_state: tuple) -> Vector: Return reward for reach `next_state` from `position` using `action`. :param state: initial position :param action: action to do :param next_state: next position reached :return:

<|skeleton|>
class PressurizedBountifulSeaTreasure:

    def __init__(self, initial_state: tuple=(0, 0), default_reward: tuple=(0,), seed: int=0, columns: int=0, action_space: gym.spaces=None):
        """:param initial_state: Initial state where start the agent. :param default_reward: (treasure_value, ) :param seed: Seed used for np.random.RandomState method."""
        <|body_0|>

    def step(self, action: int) -> (tuple, Vector, bool, dict):
        """Given an action, do a step :param action: :return: (position, (time_inverted, treasure_value), final, extra)"""
        <|body_1|>

    def transition_reward(self, state: tuple, action: int, next_state: tuple) -> Vector:
        """Return reward for reach `next_state` from `position` using `action`. :param state: initial position :param action: action to do :param next_state: next position reached :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        original_mesh_shape = (10, 11)
        if columns < 1 or columns > original_mesh_shape[0]:
            columns = original_mesh_shape[0]
        finals = {(0, 1): 5, (1, 2): 80, (2, 3): 120, (3, 4): 140, (4, 4): 145, (5, 4): 150, (6, 7): 163, (7, 7): 166, (8, 9): 173, (9, 10): 175}
        finals = dict(filter(lambda x: x[0][0] < columns, finals.items()))
        obstacles = frozenset()
        obstacles = obstacles.union([(0, y) for y in range(2, 11)])
        obstacles = obstacles.union([(1, y) for y in range(3, 11)])
        obstacles = obstacles.union([(2, y) for y in range(4, 11)])
        obstacles = obstacles.union([(3, y) for y in range(5, 11)])
        obstacles = obstacles.union([(4, y) for y in range(5, 11)])
        obstacles = obstacles.union([(5, y) for y in range(5, 11)])
        obstacles = obstacles.union([(6, y) for y in range(8, 11)])
        obstacles = obstacles.union([(7, y) for y in range(8, 11)])
        obstacles = obstacles.union([(8, y) for y in range(10, 11)])
        obstacles = frozenset(filter(lambda x: x[0] < columns, obstacles))
        mesh_shape = (columns, 11)
        default_reward = (-1,) + default_reward + (0,)
        default_reward = Vector(default_reward)
        super().__init__(mesh_shape=mesh_shape, seed=seed, default_reward=default_reward, initial_state=initial_state, finals=finals, obstacles=obstacles, action_space=action_space)
<|end_body_0|>

<|body_start_1|>
        reward = self.default_reward.copy()
        self.current_state = self.next_state(action=action)
        reward[1] = self.finals.get(self.current_state, self.default_reward[1])
        reward[2] = -(self.current_state[1] + 1)
        info = {}
        final = self.is_final(self.current_state)
        return (self.current_state, reward, final, info)
<|end_body_1|>

<|body_start_2|>
        reward = self.default_reward.copy()
        reward[1] = self.finals.get(next_state, self.default_reward[1])
        reward[2] = -(next_state[1] + 1)
        return reward
<|end_body_2|>

Solution:
class PressurizedBountifulSeaTreasure:
    def __init__(self, initial_state: tuple=(0, 0), default_reward: tuple=(0,), seed: int=0, columns: int=0, action_space: gym.spaces=None):
        """:param initial_state: Initial state where start the agent. :param default_reward: (treasure_value, ) :param seed: Seed used for np.random.RandomState method."""
        original_mesh_shape = (10, 11)
        if columns < 1 or columns > original_mesh_shape[0]:
            columns = original_mesh_shape[0]
        finals = {(0, 1): 5, (1, 2): 80, (2, 3): 120, (3, 4): 140, (4, 4): 145, (5, 4): 150, (6, 7): 163, (7, 7): 166, (8, 9): 173, (9, 10): 175}
        finals = dict(filter(lambda x: x[0][0] < columns, finals.items()))
        obstacles = frozenset()
        obstacles = obstacles.union([(0, y) for y in range(2, 11)])
        obstacles = obstacles.union([(1, y) for y in range(3, 11)])
        obstacles = obstacles.union([(2, y) for y in range(4, 11)])
        obstacles = obstacles.union([(3, y) for y in range(5, 11)])
        obstacles = obstacles.union([(4, y) for y in range(5, 11)])
        obstacles = obstacles.union([(5, y) for y in range(5, 11)])
        obstacles = obstacles.union([(6, y) for y in range(8, 11)])
        obstacles = obstacles.union([(7, y) for y in range(8, 11)])
        obstacles = obstacles.union([(8, y) for y in range(10, 11)])
        obstacles = frozenset(filter(lambda x: x[0] < columns, obstacles))
        mesh_shape = (columns, 11)
        default_reward = (-1,) + default_reward + (0,)
        default_reward = Vector(default_reward)
        super().__init__(mesh_shape=mesh_shape, seed=seed, default_reward=default_reward, initial_state=initial_state, finals=finals, obstacles=obstacles, action_space=action_space)

    def step(self, action: int) -> (tuple, Vector, bool, dict):
        """Given an action, do a step :param action: :return: (position, (time_inverted, treasure_value), final, extra)"""
        reward = self.default_reward.copy()
        self.current_state = self.next_state(action=action)
        reward[1] = self.finals.get(self.current_state, self.default_reward[1])
        reward[2] = -(self.current_state[1] + 1)
        info = {}
        final = self.is_final(self.current_state)
        return (self.current_state, reward, final, info)

    def transition_reward(self, state: tuple, action: int, next_state: tuple) -> Vector:
        """Return reward for reach `next_state` from `position` using `action`. :param state: initial position :param action: action to do :param next_state: next position reached :return:"""
        reward = self.default_reward.copy()
        reward[1] = self.finals.get(next_state, self.default_reward[1])
        reward[2] = -(next_state[1] + 1)
        return reward


test

input为输入矩阵，filter为卷积核，返回卷积之后的结果

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def test(self, input, filter, padding, stride): input为输入矩阵，filter为卷积核，返回卷积之后的结果
- def sum(self, input, row, col, filter): row,col为卷积操作在input的起始位置

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def test(self, input, filter, padding, stride): input为输入矩阵，filter为卷积核，返回卷积之后的结果
- def sum(self, input, row, col, filter): row,col为卷积操作在input的起始位置

<|skeleton|>
class Solution:

    def test(self, input, filter, padding, stride):
        """input为输入矩阵，filter为卷积核，返回卷积之后的结果"""
        <|body_0|>

    def sum(self, input, row, col, filter):
        """row,col为卷积操作在input的起始位置"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if padding:
            for item in input:
                item.insert(0, 0)
                item.append(0)
            z = [0] * len(input[0])
            input.insert(0, z)
            input.append(z)
        i_h = len(input)
        i_w = len(input[0])
        f_h = len(filter)
        f_w = len(filter[0])
        if (i_h - f_h) % stride != 0:
            print('!!请重新分配padding的长度')
            return
        if (i_w - f_w) % stride != 0:
            print('请重新分配padding的长度')
            return
        res = []
        for i in range(0, i_h - f_h + stride, stride):
            sub_res = []
            for j in range(0, i_w - f_w + stride, stride):
                sub_res.append(self.sum(input, i, j, filter))
            res.append(sub_res)
        return res
<|end_body_0|>

<|body_start_1|>
        f_h = len(filter)
        f_w = len(filter[0])
        res = 0
        for i in range(0, f_h):
            for j in range(0, f_w):
                res += input[row + i][col + j] * filter[i][j]
        return res
<|end_body_1|>

Solution:
class Solution:
    def test(self, input, filter, padding, stride):
        """input为输入矩阵，filter为卷积核，返回卷积之后的结果"""
        if padding:
            for item in input:
                item.insert(0, 0)
                item.append(0)
            z = [0] * len(input[0])
            input.insert(0, z)
            input.append(z)
        i_h = len(input)
        i_w = len(input[0])
        f_h = len(filter)
        f_w = len(filter[0])
        if (i_h - f_h) % stride != 0:
            print('!!请重新分配padding的长度')
            return
        if (i_w - f_w) % stride != 0:
            print('请重新分配padding的长度')
            return
        res = []
        for i in range(0, i_h - f_h + stride, stride):
            sub_res = []
            for j in range(0, i_w - f_w + stride, stride):
                sub_res.append(self.sum(input, i, j, filter))
            res.append(sub_res)
        return res

    def sum(self, input, row, col, filter):
        """row,col为卷积操作在input的起始位置"""
        f_h = len(filter)
        f_w = len(filter[0])
        res = 0
        for i in range(0, f_h):
            for j in range(0, f_w):
                res += input[row + i][col + j] * filter[i][j]
        return res


Prompt:
Implement the Python class `ActivityCompile` described below.

Class description:
Implement the ActivityCompile class.

Method signatures and docstrings:
- def encrypt_activity_compile_to_code(activity_compile): deprecated later
- def decrypt_code_to_activity_compile(code): deprecated later

Implement the Python class `ActivityCompile` described below.

Class description:
Implement the ActivityCompile class.

Method signatures and docstrings:
- def encrypt_activity_compile_to_code(activity_compile): deprecated later
- def decrypt_code_to_activity_compile(code): deprecated later

<|skeleton|>
class ActivityCompile:

    def encrypt_activity_compile_to_code(activity_compile):
        """deprecated later"""
        <|body_0|>

    def decrypt_code_to_activity_compile(code):
        """deprecated later"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            code = pickle.dumps(activity_compile)
            obj = AES.new(AES_KEY, AES.MODE_ECB)
            code = code + '=' * (16 - len(code) % 16)
            code = obj.encrypt(code)
            code = base64.urlsafe_b64encode(code)
            return code
        except Exception as e:
            logging.error('encrypt_activity_compile_to_code error')
            logging.error(e)
            return ''
<|end_body_0|>

<|body_start_1|>
        code = str(code)
        try:
            code = base64.urlsafe_b64decode(code)
            obj = AES.new(AES_KEY, AES.MODE_ECB)
            code = obj.decrypt(code)
            code = code.rstrip('=')
            activity_compile = pickle.loads(code)
            return activity_compile
        except Exception as e:
            logging.error('decrypt_code_to_activity_compile error')
            logging.error(e)
            return None
<|end_body_1|>

Solution:
class ActivityCompile:
    def encrypt_activity_compile_to_code(activity_compile):
        """deprecated later"""
        try:
            code = pickle.dumps(activity_compile)
            obj = AES.new(AES_KEY, AES.MODE_ECB)
            code = code + '=' * (16 - len(code) % 16)
            code = obj.encrypt(code)
            code = base64.urlsafe_b64encode(code)
            return code
        except Exception as e:
            logging.error('encrypt_activity_compile_to_code error')
            logging.error(e)
            return ''

    def decrypt_code_to_activity_compile(code):
        """deprecated later"""
        code = str(code)
        try:
            code = base64.urlsafe_b64decode(code)
            obj = AES.new(AES_KEY, AES.MODE_ECB)
            code = obj.decrypt(code)
            code = code.rstrip('=')
            activity_compile = pickle.loads(code)
            return activity_compile
        except Exception as e:
            logging.error('decrypt_code_to_activity_compile error')
            logging.error(e)
            return None


Prompt:
Implement the Python class `ReadyHandler` described below.

Class description:
Implement the ReadyHandler class.

Method signatures and docstrings:
- def get(self, ident): 获取预生产发布信息
- def post(self, ident=0): 预生产创建nginx及consul
- def delete(self, ident): 预生产删除nginx及consul

Implement the Python class `ReadyHandler` described below.

Class description:
Implement the ReadyHandler class.

Method signatures and docstrings:
- def get(self, ident): 获取预生产发布信息
- def post(self, ident=0): 预生产创建nginx及consul
- def delete(self, ident): 预生产删除nginx及consul

<|skeleton|>
class ReadyHandler:

    def get(self, ident):
        """获取预生产发布信息"""
        <|body_0|>

    def post(self, ident=0):
        """预生产创建nginx及consul"""
        <|body_1|>

    def delete(self, ident):
        """预生产删除nginx及consul"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        username = self.get_cookie('username')
        page = int(self.get_argument('page', 1))
        searchKey = self.get_argument('searchKey', None)
        pagesize = int(self.get_argument('pagesize', self._PageSize))
        totalquery = self.db.query(ReadyReleaseServer.Id)
        NgReleaseObj = self.db.query(ReadyReleaseServer)
        if searchKey:
            totalquery = totalquery.filter(or_(ReadyReleaseServer.DomainName.like('%%%s%%' % searchKey), ReadyReleaseServer.HealthExam.like('%%%s%%' % searchKey), ReadyReleaseServer.Port.like('%%%s%%' % searchKey)))
            NgReleaseObj = NgReleaseObj.filter(or_(ReadyReleaseServer.DomainName.like('%%%s%%' % searchKey), ReadyReleaseServer.HealthExam.like('%%%s%%' % searchKey), ReadyReleaseServer.Port.like('%%%s%%' % searchKey)))
        self.Result['total'] = totalquery.count()
        serverTask = NgReleaseObj.order_by(desc(ReadyReleaseServer.Id)).limit(pagesize).offset((page - 1) * pagesize).all()
        self.Result['rows'] = list(map(lambda obj: obj.toDict(), serverTask))
        self.Result['username'] = username
        self.finish(self.Result)
<|end_body_0|>

<|body_start_1|>
        data = json.loads(self.request.body.decode('utf-8'))
        objTask = ReadyReleaseServer()
        domainname = data['params'].get('DomainName', None)
        objTask.name = ''.join(domainname.split('-'))
        objTask.DomainName = domainname + '.pdyf.open.com.cn'
        objTask.HealthExam = data['params'].get('HealthExam', None)
        objTask.Port = int(data['params'].get('Port', None))
        objTask.Publisher = self.get_cookie('username')
        self.db.add(objTask)
        self.db.commit()
        tasks.ready_Release.delay(objTask.name, objTask.Port, domainname, objTask.HealthExam)
        self.Result['rows'] = 1
        self.Result['info'] = u'创建成功'
        self.finish(self.Result)
<|end_body_1|>

<|body_start_2|>
        pro = self.db.query(ReadyReleaseServer).filter(ReadyReleaseServer.Id == ident).first()
        domainname = pro.DomainName.split('.')[0]
        tasks.ready_Release_del.delay(domainname)
        self.db.query(ReadyReleaseServer).filter(ReadyReleaseServer.Id == ident).delete()
        self.db.commit()
        self.Result['info'] = u'删除nginx及consul成功'
        self.finish(self.Result)
<|end_body_2|>

Solution:
class ReadyHandler:
    def get(self, ident):
        """获取预生产发布信息"""
        username = self.get_cookie('username')
        page = int(self.get_argument('page', 1))
        searchKey = self.get_argument('searchKey', None)
        pagesize = int(self.get_argument('pagesize', self._PageSize))
        totalquery = self.db.query(ReadyReleaseServer.Id)
        NgReleaseObj = self.db.query(ReadyReleaseServer)
        if searchKey:
            totalquery = totalquery.filter(or_(ReadyReleaseServer.DomainName.like('%%%s%%' % searchKey), ReadyReleaseServer.HealthExam.like('%%%s%%' % searchKey), ReadyReleaseServer.Port.like('%%%s%%' % searchKey)))
            NgReleaseObj = NgReleaseObj.filter(or_(ReadyReleaseServer.DomainName.like('%%%s%%' % searchKey), ReadyReleaseServer.HealthExam.like('%%%s%%' % searchKey), ReadyReleaseServer.Port.like('%%%s%%' % searchKey)))
        self.Result['total'] = totalquery.count()
        serverTask = NgReleaseObj.order_by(desc(ReadyReleaseServer.Id)).limit(pagesize).offset((page - 1) * pagesize).all()
        self.Result['rows'] = list(map(lambda obj: obj.toDict(), serverTask))
        self.Result['username'] = username
        self.finish(self.Result)

    def post(self, ident=0):
        """预生产创建nginx及consul"""
        data = json.loads(self.request.body.decode('utf-8'))
        objTask = ReadyReleaseServer()
        domainname = data['params'].get('DomainName', None)
        objTask.name = ''.join(domainname.split('-'))
        objTask.DomainName = domainname + '.pdyf.open.com.cn'
        objTask.HealthExam = data['params'].get('HealthExam', None)
        objTask.Port = int(data['params'].get('Port', None))
        objTask.Publisher = self.get_cookie('username')
        self.db.add(objTask)
        self.db.commit()
        tasks.ready_Release.delay(objTask.name, objTask.Port, domainname, objTask.HealthExam)
        self.Result['rows'] = 1
        self.Result['info'] = u'创建成功'
        self.finish(self.Result)

    def delete(self, ident):
        """预生产删除nginx及consul"""
        pro = self.db.query(ReadyReleaseServer).filter(ReadyReleaseServer.Id == ident).first()
        domainname = pro.DomainName.split('.')[0]
        tasks.ready_Release_del.delay(domainname)
        self.db.query(ReadyReleaseServer).filter(ReadyReleaseServer.Id == ident).delete()
        self.db.commit()
        self.Result['info'] = u'删除nginx及consul成功'
        self.finish(self.Result)


options

写入跨域请求header

Prompt:
Implement the Python class `CorsMixin` described below.

Class description:
Implement the CorsMixin class.

Method signatures and docstrings:
- def set_default_headers(self): 设置默认头
- def options(self, *args, **kwargs): 写入跨域请求header
- def _get_methods(self): 设置支持的跨域方法

Implement the Python class `CorsMixin` described below.

Class description:
Implement the CorsMixin class.

Method signatures and docstrings:
- def set_default_headers(self): 设置默认头
- def options(self, *args, **kwargs): 写入跨域请求header
- def _get_methods(self): 设置支持的跨域方法

<|skeleton|>
class CorsMixin:

    def set_default_headers(self):
        """设置默认头"""
        <|body_0|>

    def options(self, *args, **kwargs):
        """写入跨域请求header"""
        <|body_1|>

    def _get_methods(self):
        """设置支持的跨域方法"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if self.CORS_ORIGIN:
            self.set_header('Access-Control-Allow-Origin', self.CORS_ORIGIN)
        if self.CORS_EXPOSE_HEADERS:
            self.set_header('Access-Control-Expose-Headers', self.CORS_EXPOSE_HEADERS)
<|end_body_0|>

<|body_start_1|>
        if self.CORS_HEADERS:
            self.set_header('Access-Control-Allow-Headers', self.CORS_HEADERS)
        if self.CORS_METHODS:
            self.set_header('Access-Control-Allow-Methods', self.CORS_METHODS)
        else:
            self.set_header('Access-Control-Allow-Methods', self._get_methods())
        if self.CORS_CREDENTIALS != None:
            self.set_header('Access-Control-Allow-Credentials', 'true' if self.CORS_CREDENTIALS else 'false')
        if self.CORS_MAX_AGE:
            self.set_header('Access-Control-Max-Age', self.CORS_MAX_AGE)
        if self.CORS_EXPOSE_HEADERS:
            self.set_header('Access-Control-Expose-Headers', self.CORS_EXPOSE_HEADERS)
        self.set_status(204)
        self.finish()
<|end_body_1|>

<|body_start_2|>
        supported_methods = [method.lower() for method in self.SUPPORTED_METHODS]
        methods = []
        for meth in supported_methods:
            instance_meth = getattr(self, meth)
            if not meth:
                continue
            handler_class = _get_class_that_defined_method(instance_meth)
            if not handler_class is RequestHandler:
                methods.append(meth.upper())
        return ', '.join(methods)
<|end_body_2|>

Solution:
class CorsMixin:
    def set_default_headers(self):
        """设置默认头"""
        if self.CORS_ORIGIN:
            self.set_header('Access-Control-Allow-Origin', self.CORS_ORIGIN)
        if self.CORS_EXPOSE_HEADERS:
            self.set_header('Access-Control-Expose-Headers', self.CORS_EXPOSE_HEADERS)

    def options(self, *args, **kwargs):
        """写入跨域请求header"""
        if self.CORS_HEADERS:
            self.set_header('Access-Control-Allow-Headers', self.CORS_HEADERS)
        if self.CORS_METHODS:
            self.set_header('Access-Control-Allow-Methods', self.CORS_METHODS)
        else:
            self.set_header('Access-Control-Allow-Methods', self._get_methods())
        if self.CORS_CREDENTIALS != None:
            self.set_header('Access-Control-Allow-Credentials', 'true' if self.CORS_CREDENTIALS else 'false')
        if self.CORS_MAX_AGE:
            self.set_header('Access-Control-Max-Age', self.CORS_MAX_AGE)
        if self.CORS_EXPOSE_HEADERS:
            self.set_header('Access-Control-Expose-Headers', self.CORS_EXPOSE_HEADERS)
        self.set_status(204)
        self.finish()

    def _get_methods(self):
        """设置支持的跨域方法"""
        supported_methods = [method.lower() for method in self.SUPPORTED_METHODS]
        methods = []
        for meth in supported_methods:
            instance_meth = getattr(self, meth)
            if not meth:
                continue
            handler_class = _get_class_that_defined_method(instance_meth)
            if not handler_class is RequestHandler:
                methods.append(meth.upper())
        return ', '.join(methods)


Prompt:
Implement the Python class `AttributesFrame` described below.

Class description:
Frame represents attributes of a single item. Contains form layouts - labels and inputs

Method signatures and docstrings:
- def __init__(self, main_window, item): main_window: MainWindow instance item: CalculableObject instance
- def _get_input_changed_func(self, main_window): I don't want to keep reference to the MainWindow

Implement the Python class `AttributesFrame` described below.

Class description:
Frame represents attributes of a single item. Contains form layouts - labels and inputs

Method signatures and docstrings:
- def __init__(self, main_window, item): main_window: MainWindow instance item: CalculableObject instance
- def _get_input_changed_func(self, main_window): I don't want to keep reference to the MainWindow

<|skeleton|>
class AttributesFrame:
    """Frame represents attributes of a single item. Contains form layouts - labels and inputs"""

    def __init__(self, main_window, item):
        """main_window: MainWindow instance item: CalculableObject instance"""
        <|body_0|>

    def _get_input_changed_func(self, main_window):
        """I don't want to keep reference to the MainWindow"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.item = item
        self.inputs = []
        self.input_changed = self._get_input_changed_func(main_window)
        hbox = QHBoxLayout()
        self.setLayout(hbox)
        hbox.setSpacing(0)
        rows = 5
        for i, arg_name in enumerate(item):
            if i % rows == 0:
                try:
                    vbox.addStretch(10)
                except NameError:
                    pass
                vbox = QVBoxLayout()
                vbox.setSpacing(0)
                vbox.setContentsMargins(0, 0, 0, 0)
                hbox.addLayout(vbox)
            self.inputs.append(InputWidget(self, main_window, arg_name))
            vbox.addWidget(self.inputs[-1])
        try:
            vbox.addStretch()
        except NameError:
            pass
        hbox.addStretch(100)
<|end_body_0|>

<|body_start_1|>
        def input_changed(label, value):
            self.item.set(label, value)
            self.item.calculate()
            for each in self.inputs:
                if label != each.label_text:
                    each.set_value(self.item[each.label_text])
            main_window.input_changed(self.item)
        return input_changed
<|end_body_1|>

Solution:
class AttributesFrame:
    """Frame represents attributes of a single item. Contains form layouts - labels and inputs"""

    def __init__(self, main_window, item):
        """main_window: MainWindow instance item: CalculableObject instance"""
        super().__init__()
        self.item = item
        self.inputs = []
        self.input_changed = self._get_input_changed_func(main_window)
        hbox = QHBoxLayout()
        self.setLayout(hbox)
        hbox.setSpacing(0)
        rows = 5
        for i, arg_name in enumerate(item):
            if i % rows == 0:
                try:
                    vbox.addStretch(10)
                except NameError:
                    pass
                vbox = QVBoxLayout()
                vbox.setSpacing(0)
                vbox.setContentsMargins(0, 0, 0, 0)
                hbox.addLayout(vbox)
            self.inputs.append(InputWidget(self, main_window, arg_name))
            vbox.addWidget(self.inputs[-1])
        try:
            vbox.addStretch()
        except NameError:
            pass
        hbox.addStretch(100)

    def _get_input_changed_func(self, main_window):
        """I don't want to keep reference to the MainWindow"""
        def input_changed(label, value):
            self.item.set(label, value)
            self.item.calculate()
            for each in self.inputs:
                if label != each.label_text:
                    each.set_value(self.item[each.label_text])
            main_window.input_changed(self.item)
        return input_changed


Prompt:
Implement the Python class `CT_Body` described below.

Class description:
``<w:body>``, the container element for the main document story in ``document.xml``.

Method signatures and docstrings:
- def add_section_break(self): Return the current ``<w:sectPr>`` element after adding a clone of it in a new ``<w:p>`` element appended to the block content elements. Note that the "current" ``<w:sectPr>`` will always be the sentinel sectPr in this case since we're always working at the end of the block content.
- def clear_content(self): Remove all content child elements from this <w:body> element. Leave the <w:sectPr> element if it is present.

Implement the Python class `CT_Body` described below.

Class description:
``<w:body>``, the container element for the main document story in ``document.xml``.

Method signatures and docstrings:
- def add_section_break(self): Return the current ``<w:sectPr>`` element after adding a clone of it in a new ``<w:p>`` element appended to the block content elements. Note that the "current" ``<w:sectPr>`` will always be the sentinel sectPr in this case since we're always working at the end of the block content.
- def clear_content(self): Remove all content child elements from this <w:body> element. Leave the <w:sectPr> element if it is present.

<|skeleton|>
class CT_Body:
    """``<w:body>``, the container element for the main document story in ``document.xml``."""

    def add_section_break(self):
        """Return the current ``<w:sectPr>`` element after adding a clone of it in a new ``<w:p>`` element appended to the block content elements. Note that the "current" ``<w:sectPr>`` will always be the sentinel sectPr in this case since we're always working at the end of the block content."""
        <|body_0|>

    def clear_content(self):
        """Remove all content child elements from this <w:body> element. Leave the <w:sectPr> element if it is present."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        sentinel_sectPr = self.get_or_add_sectPr()
        cloned_sectPr = sentinel_sectPr.clone()
        p = self.add_p()
        p.set_sectPr(cloned_sectPr)
        return sentinel_sectPr
<|end_body_0|>

<|body_start_1|>
        if self.sectPr is not None:
            content_elms = self[:-1]
        else:
            content_elms = self[:]
        for content_elm in content_elms:
            self.remove(content_elm)
<|end_body_1|>

Solution:
class CT_Body:
    """``<w:body>``, the container element for the main document story in ``document.xml``."""

    def add_section_break(self):
        """Return the current ``<w:sectPr>`` element after adding a clone of it in a new ``<w:p>`` element appended to the block content elements. Note that the "current" ``<w:sectPr>`` will always be the sentinel sectPr in this case since we're always working at the end of the block content."""
        sentinel_sectPr = self.get_or_add_sectPr()
        cloned_sectPr = sentinel_sectPr.clone()
        p = self.add_p()
        p.set_sectPr(cloned_sectPr)
        return sentinel_sectPr

    def clear_content(self):
        """Remove all content child elements from this <w:body> element. Leave the <w:sectPr> element if it is present."""
        if self.sectPr is not None:
            content_elms = self[:-1]
        else:
            content_elms = self[:]
        for content_elm in content_elms:
            self.remove(content_elm)


Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSquares_DP(self, n): :type n: int :rtype: int
- def numSquares_Math(self, n): :type n: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSquares_DP(self, n): :type n: int :rtype: int
- def numSquares_Math(self, n): :type n: int :rtype: int

<|skeleton|>
class Solution:

    def numSquares_DP(self, n):
        """:type n: int :rtype: int"""
        <|body_0|>

    def numSquares_Math(self, n):
        """:type n: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if n < 4:
            return n
        counts = list(range(n + 1))
        for i in range(n):
            j = 1
            while True:
                temp = i + j * j
                if temp > n:
                    break
                if counts[temp] > counts[i] + 1:
                    counts[temp] = counts[i] + 1
                j += 1
        return counts[n]
<|end_body_0|>

<|body_start_1|>
        if n < 4:
            return n
        sqrt_n = int(math.sqrt(n))
        if n == sqrt_n ** 2:
            return 1
        while n % 4 == 0:
            n //= 4
        if n % 8 == 7:
            return 4
        sqrt_n = int(math.sqrt(n))
        for i in range(1, sqrt_n + 1):
            nii = n - i ** 2
            sqrt_nii = int(math.sqrt(nii))
            if nii == sqrt_nii ** 2:
                return 2
        return 3
<|end_body_1|>

Solution:
class Solution:
    def numSquares_DP(self, n):
        """:type n: int :rtype: int"""
        if n < 4:
            return n
        counts = list(range(n + 1))
        for i in range(n):
            j = 1
            while True:
                temp = i + j * j
                if temp > n:
                    break
                if counts[temp] > counts[i] + 1:
                    counts[temp] = counts[i] + 1
                j += 1
        return counts[n]

    def numSquares_Math(self, n):
        """:type n: int :rtype: int"""
        if n < 4:
            return n
        sqrt_n = int(math.sqrt(n))
        if n == sqrt_n ** 2:
            return 1
        while n % 4 == 0:
            n //= 4
        if n % 8 == 7:
            return 4
        sqrt_n = int(math.sqrt(n))
        for i in range(1, sqrt_n + 1):
            nii = n - i ** 2
            sqrt_nii = int(math.sqrt(nii))
            if nii == sqrt_nii ** 2:
                return 2
        return 3


Prompt:
Implement the Python class `S3VDCHook` described below.

Class description:
S3VDC Training flow control

Method signatures and docstrings:
- def __init__(self, vae: dict, vgmm: dict, hps: Union[tf.contrib.training.HParams, dict], utils: dict, is_chief: bool) -> None: Initialize the S3VDCHook. Arguments: vae {dict} -- The tuple return by calling function get_cvae(). vgmm {dict} -- The global GMM model weights/paramters. hps {Union[tf.contrib.training.HParams, dict]} -- Hyper parameters. utils {dict} -- The utility Tensors to be used by this class. is_chief {bool} -- Is chief node (only useful in distributed training).
- def skl_init(self, session: tf.Session, step: int) -> None: Perform initialization of global GMM model. Arguments: session {tf.Session} -- Session from the context. step {int} -- Current training step.
- def before_run(self, run_context: tfe.SessionRunContext) -> None: The S3VDC flow control Arguments: run_context {tf.estimator.SessionRunContext} -- The run context.

Implement the Python class `S3VDCHook` described below.

Class description:
S3VDC Training flow control

Method signatures and docstrings:
- def __init__(self, vae: dict, vgmm: dict, hps: Union[tf.contrib.training.HParams, dict], utils: dict, is_chief: bool) -> None: Initialize the S3VDCHook. Arguments: vae {dict} -- The tuple return by calling function get_cvae(). vgmm {dict} -- The global GMM model weights/paramters. hps {Union[tf.contrib.training.HParams, dict]} -- Hyper parameters. utils {dict} -- The utility Tensors to be used by this class. is_chief {bool} -- Is chief node (only useful in distributed training).
- def skl_init(self, session: tf.Session, step: int) -> None: Perform initialization of global GMM model. Arguments: session {tf.Session} -- Session from the context. step {int} -- Current training step.
- def before_run(self, run_context: tfe.SessionRunContext) -> None: The S3VDC flow control Arguments: run_context {tf.estimator.SessionRunContext} -- The run context.

<|skeleton|>
class S3VDCHook:
    """S3VDC Training flow control"""

    def __init__(self, vae: dict, vgmm: dict, hps: Union[tf.contrib.training.HParams, dict], utils: dict, is_chief: bool) -> None:
        """Initialize the S3VDCHook. Arguments: vae {dict} -- The tuple return by calling function get_cvae(). vgmm {dict} -- The global GMM model weights/paramters. hps {Union[tf.contrib.training.HParams, dict]} -- Hyper parameters. utils {dict} -- The utility Tensors to be used by this class. is_chief {bool} -- Is chief node (only useful in distributed training)."""
        <|body_0|>

    def skl_init(self, session: tf.Session, step: int) -> None:
        """Perform initialization of global GMM model. Arguments: session {tf.Session} -- Session from the context. step {int} -- Current training step."""
        <|body_1|>

    def before_run(self, run_context: tfe.SessionRunContext) -> None:
        """The S3VDC flow control Arguments: run_context {tf.estimator.SessionRunContext} -- The run context."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.vae = vae
        self.vgmm = vgmm
        self.hps = hps
        self.utils = utils
        self.is_chief = is_chief
        self.gamma_steps = resolve_simple_number(hps, 'gammaSteps')
        self.gmm_steps = resolve_simple_number(hps, 'gmmSteps')
        self.gamma_gmm_training_steps = self.gamma_steps + self.gmm_steps
        self.beta_steps = resolve_simple_number(hps, 'betaSteps')
        self.static_steps = resolve_simple_number(hps, 'staticSteps')
        self.svdc_gmm_finetune_steps = self.beta_steps + self.static_steps
        self.max_steps = resolve_simple_number(hps, 'maxSteps')
        self.mu = tf.placeholder(dtype=tf.float32)
        self.sigma = tf.placeholder(dtype=tf.float32)
        self.mtm = tf.pow(1.0 - self.vgmm['anneal_factor'], 3)
        self.update_svdc_with_gmm_ops = [tf.assign(self.vgmm['sigma'], (1.0 - self.mtm) * self.vgmm['sigma'] + self.mtm * self.sigma)]
        self.update_svdc_with_kmeans_ops = [tf.assign(self.vgmm['mu'], (1.0 - self.mtm) * self.vgmm['mu'] + self.mtm * self.mu)]
        self.gmm_data = None
<|end_body_0|>

<|body_start_1|>
        is_final = True if step == 0 else False
        gmm_input_slice = session.run(self.vae['z_mu'])
        if self.gmm_data is None:
            self.gmm_data = gmm_input_slice
        else:
            self.gmm_data = np.concatenate([self.gmm_data, gmm_input_slice], axis=0)
        tfl.info('GMM data accumulation done at local step {} with {} samples.'.format(step, len(self.gmm_data)))
        if is_final:
            np.random.shuffle(self.gmm_data)
            kmeans = cluster.KMeans(n_clusters=self.hps.numCluster, random_state=0)
            kmeans.fit(self.gmm_data)
            session.run(self.update_svdc_with_kmeans_ops, feed_dict={self.mu: kmeans.cluster_centers_.T})
            skgmm = mixture.GaussianMixture(n_components=self.hps.numCluster, covariance_type='diag', max_iter=10000, means_init=kmeans.cluster_centers_, random_state=100)
            skgmm.fit(self.gmm_data)
            session.run(self.update_svdc_with_gmm_ops, feed_dict={self.sigma: skgmm.covariances_.T})
            self.gmm_data = None
            tfl.info('GMM init done at local step {} with momentum factor {}'.format(step, session.run(self.mtm)))
        session.run(self.utils['incr_gs_step'])
<|end_body_1|>

<|body_start_2|>
        session = run_context.session
        gs = tf.train.global_step(session, self.utils['global_step'])
        if gs == 0:
            tfl.info('S3VDC training begin at global step 1.')
            session.run(self.utils['incr_gs_step'])
            return
        if gs == self.max_steps - 1:
            tfl.info('S3VDC training end at global step {} with latent annealing factor {}.'.format(gs, session.run(self.vgmm['anneal_factor'])))
        gs_offset = gs - self.gamma_gmm_training_steps
        if gs_offset <= 0:
            if gs_offset <= -1.0 * self.gmm_steps:
                if gs_offset == 1 - self.gamma_gmm_training_steps:
                    tfl.info('Gamma training begin at global step {}'.format(gs))
                session.run(self.vgmm['gamma_training_op'])
            else:
                self.skl_init(session, gs_offset)
        else:
            cyc_step = gs_offset % self.svdc_gmm_finetune_steps
            cyc_id = gs_offset // self.svdc_gmm_finetune_steps + 1
            if cyc_step > 0 and cyc_step <= self.beta_steps:
                if cyc_step == 1:
                    tfl.info('PERIOD {}: Beta annealing begin at global step {} with latent annealing factor {}'.format(cyc_id, gs, session.run(self.vgmm['anneal_factor'])))
                elif cyc_step == self.beta_steps:
                    tfl.info('PERIOD {}: Beta annealing end at global step {} with latent annealing factor {}'.format(cyc_id, gs, session.run(self.vgmm['anneal_factor'])))
                session.run(self.vgmm['svdc_finetune_op'])
            else:
                session.run(self.vgmm['svdc_static_train_op'])
<|end_body_2|>

Solution:
class S3VDCHook:
    """S3VDC Training flow control"""

    def __init__(self, vae: dict, vgmm: dict, hps: Union[tf.contrib.training.HParams, dict], utils: dict, is_chief: bool) -> None:
        """Initialize the S3VDCHook. Arguments: vae {dict} -- The tuple return by calling function get_cvae(). vgmm {dict} -- The global GMM model weights/paramters. hps {Union[tf.contrib.training.HParams, dict]} -- Hyper parameters. utils {dict} -- The utility Tensors to be used by this class. is_chief {bool} -- Is chief node (only useful in distributed training)."""
        self.vae = vae
        self.vgmm = vgmm
        self.hps = hps
        self.utils = utils
        self.is_chief = is_chief
        self.gamma_steps = resolve_simple_number(hps, 'gammaSteps')
        self.gmm_steps = resolve_simple_number(hps, 'gmmSteps')
        self.gamma_gmm_training_steps = self.gamma_steps + self.gmm_steps
        self.beta_steps = resolve_simple_number(hps, 'betaSteps')
        self.static_steps = resolve_simple_number(hps, 'staticSteps')
        self.svdc_gmm_finetune_steps = self.beta_steps + self.static_steps
        self.max_steps = resolve_simple_number(hps, 'maxSteps')
        self.mu = tf.placeholder(dtype=tf.float32)
        self.sigma = tf.placeholder(dtype=tf.float32)
        self.mtm = tf.pow(1.0 - self.vgmm['anneal_factor'], 3)
        self.update_svdc_with_gmm_ops = [tf.assign(self.vgmm['sigma'], (1.0 - self.mtm) * self.vgmm['sigma'] + self.mtm * self.sigma)]
        self.update_svdc_with_kmeans_ops = [tf.assign(self.vgmm['mu'], (1.0 - self.mtm) * self.vgmm['mu'] + self.mtm * self.mu)]
        self.gmm_data = None

    def skl_init(self, session: tf.Session, step: int) -> None:
        """Perform initialization of global GMM model. Arguments: session {tf.Session} -- Session from the context. step {int} -- Current training step."""
        is_final = True if step == 0 else False
        gmm_input_slice = session.run(self.vae['z_mu'])
        if self.gmm_data is None:
            self.gmm_data = gmm_input_slice
        else:
            self.gmm_data = np.concatenate([self.gmm_data, gmm_input_slice], axis=0)
        tfl.info('GMM data accumulation done at local step {} with {} samples.'.format(step, len(self.gmm_data)))
        if is_final:
            np.random.shuffle(self.gmm_data)
            kmeans = cluster.KMeans(n_clusters=self.hps.numCluster, random_state=0)
            kmeans.fit(self.gmm_data)
            session.run(self.update_svdc_with_kmeans_ops, feed_dict={self.mu: kmeans.cluster_centers_.T})
            skgmm = mixture.GaussianMixture(n_components=self.hps.numCluster, covariance_type='diag', max_iter=10000, means_init=kmeans.cluster_centers_, random_state=100)
            skgmm.fit(self.gmm_data)
            session.run(self.update_svdc_with_gmm_ops, feed_dict={self.sigma: skgmm.covariances_.T})
            self.gmm_data = None
            tfl.info('GMM init done at local step {} with momentum factor {}'.format(step, session.run(self.mtm)))
        session.run(self.utils['incr_gs_step'])

    def before_run(self, run_context: tfe.SessionRunContext) -> None:
        """The S3VDC flow control Arguments: run_context {tf.estimator.SessionRunContext} -- The run context."""
        session = run_context.session
        gs = tf.train.global_step(session, self.utils['global_step'])
        if gs == 0:
            tfl.info('S3VDC training begin at global step 1.')
            session.run(self.utils['incr_gs_step'])
            return
        if gs == self.max_steps - 1:
            tfl.info('S3VDC training end at global step {} with latent annealing factor {}.'.format(gs, session.run(self.vgmm['anneal_factor'])))
        gs_offset = gs - self.gamma_gmm_training_steps
        if gs_offset <= 0:
            if gs_offset <= -1.0 * self.gmm_steps:
                if gs_offset == 1 - self.gamma_gmm_training_steps:
                    tfl.info('Gamma training begin at global step {}'.format(gs))
                session.run(self.vgmm['gamma_training_op'])
            else:
                self.skl_init(session, gs_offset)
        else:
            cyc_step = gs_offset % self.svdc_gmm_finetune_steps
            cyc_id = gs_offset // self.svdc_gmm_finetune_steps + 1
            if cyc_step > 0 and cyc_step <= self.beta_steps:
                if cyc_step == 1:
                    tfl.info('PERIOD {}: Beta annealing begin at global step {} with latent annealing factor {}'.format(cyc_id, gs, session.run(self.vgmm['anneal_factor'])))
                elif cyc_step == self.beta_steps:
                    tfl.info('PERIOD {}: Beta annealing end at global step {} with latent annealing factor {}'.format(cyc_id, gs, session.run(self.vgmm['anneal_factor'])))
                session.run(self.vgmm['svdc_finetune_op'])
            else:
                session.run(self.vgmm['svdc_static_train_op'])


post

Prompt:
Implement the Python class `ServiceLineView` described below.

Class description:
业务线视图函数

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 展示业务线信息 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 新建业务线 :param request: :param args: :param kwargs: :return:

Implement the Python class `ServiceLineView` described below.

Class description:
业务线视图函数

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 展示业务线信息 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 新建业务线 :param request: :param args: :param kwargs: :return:

<|skeleton|>
class ServiceLineView:
    """业务线视图函数"""

    def get(self, request, *args, **kwargs):
        """展示业务线信息 :param request: :param args: :param kwargs: :return:"""
        <|body_0|>

    def post(self, request, *args, **kwargs):
        """新建业务线 :param request: :param args: :param kwargs: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        form = ServiceForm()
        service_obj = models.Services.objects.all().order_by('-id')
        owner_obj = models.User.objects.values('id', 'username')
        return render(request, 'host/service_line.html', {'form': form, 'service_obj': service_obj, 'owner_obj': owner_obj})
<|end_body_0|>

<|body_start_1|>
        username = request.session.get('user_info')['username']
        response = {'status': True, 'data': None, 'msg': None}
        print(request.POST)
        print(request.POST.get('owner_id'))
        form = ServiceForm(data=request.POST)
        if form.is_valid():
            print(form.cleaned_data)
            owner_id_list = form.cleaned_data.pop('owner_id')
            obj = models.Services.objects.create(**form.cleaned_data)
            obj.owner.add(*owner_id_list)
            log.logger.info('username:{},Add service line info: name:{},idc_id:{}'.format(username, form.cleaned_data['name'], form.cleaned_data['idc_id']))
        else:
            print(form.errors)
            response['status'] = False
            response['msg'] = form.errors
        return HttpResponse(json.dumps(response))
<|end_body_1|>

Solution:
class ServiceLineView:
    """业务线视图函数"""

    def get(self, request, *args, **kwargs):
        """展示业务线信息 :param request: :param args: :param kwargs: :return:"""
        form = ServiceForm()
        service_obj = models.Services.objects.all().order_by('-id')
        owner_obj = models.User.objects.values('id', 'username')
        return render(request, 'host/service_line.html', {'form': form, 'service_obj': service_obj, 'owner_obj': owner_obj})

    def post(self, request, *args, **kwargs):
        """新建业务线 :param request: :param args: :param kwargs: :return:"""
        username = request.session.get('user_info')['username']
        response = {'status': True, 'data': None, 'msg': None}
        print(request.POST)
        print(request.POST.get('owner_id'))
        form = ServiceForm(data=request.POST)
        if form.is_valid():
            print(form.cleaned_data)
            owner_id_list = form.cleaned_data.pop('owner_id')
            obj = models.Services.objects.create(**form.cleaned_data)
            obj.owner.add(*owner_id_list)
            log.logger.info('username:{},Add service line info: name:{},idc_id:{}'.format(username, form.cleaned_data['name'], form.cleaned_data['idc_id']))
        else:
            print(form.errors)
            response['status'] = False
            response['msg'] = form.errors
        return HttpResponse(json.dumps(response))


Prompt:
Implement the Python class `OutlineComparer` described below.

Class description:
Implement the OutlineComparer class.

Method signatures and docstrings:
- def __init__(self, training_mask): Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/training object. :type training_mask: np.ndarray
- def outline_distance(self, query_mask): Compare query blob to reference blob in terms of average contour distance. Further, an approximate angle difference is determined. :param query_mask: Binary mask with a single blob to compare against reference blob :type query_mask: np.ndarray :return: The average pixel distance between query blob and reference blob :rtype: float
- def _calc_dist_curve(self, mask): Calculate the distance from mask centroid to each contour pixel. :param mask: Binary mask with a single blob to calculate distance curve for :type mask: np.ndarray :return: Distance curve. :rtype: np.ndarray

Implement the Python class `OutlineComparer` described below.

Class description:
Implement the OutlineComparer class.

Method signatures and docstrings:
- def __init__(self, training_mask): Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/training object. :type training_mask: np.ndarray
- def outline_distance(self, query_mask): Compare query blob to reference blob in terms of average contour distance. Further, an approximate angle difference is determined. :param query_mask: Binary mask with a single blob to compare against reference blob :type query_mask: np.ndarray :return: The average pixel distance between query blob and reference blob :rtype: float
- def _calc_dist_curve(self, mask): Calculate the distance from mask centroid to each contour pixel. :param mask: Binary mask with a single blob to calculate distance curve for :type mask: np.ndarray :return: Distance curve. :rtype: np.ndarray

<|skeleton|>
class OutlineComparer:

    def __init__(self, training_mask):
        """Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/training object. :type training_mask: np.ndarray"""
        <|body_0|>

    def outline_distance(self, query_mask):
        """Compare query blob to reference blob in terms of average contour distance. Further, an approximate angle difference is determined. :param query_mask: Binary mask with a single blob to compare against reference blob :type query_mask: np.ndarray :return: The average pixel distance between query blob and reference blob :rtype: float"""
        <|body_1|>

    def _calc_dist_curve(self, mask):
        """Calculate the distance from mask centroid to each contour pixel. :param mask: Binary mask with a single blob to calculate distance curve for :type mask: np.ndarray :return: Distance curve. :rtype: np.ndarray"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.compare_angle = None
        self._centroid_vectors = None
        self.ref_curve = self._calc_dist_curve(training_mask)
        self._ref_angle = np.arctan2(*self._centroid_vectors[0])
<|end_body_0|>

<|body_start_1|>
        input_curve = self._calc_dist_curve(query_mask)
        cost_landscape = np.abs(self.ref_curve[:, None] - input_curve[None, :])
        n_eval = min(500, self.ref_curve.size, input_curve.size)
        ref_idx = vt.intr(np.linspace(0, self.ref_curve.size - 1, n_eval))
        new_idx = vt.intr(np.linspace(0, input_curve.size - 1, n_eval))
        new_offsets = vt.intr(np.linspace(0, input_curve.size - 1, 101))[:-1]
        new_idx_all = (new_idx[:, None] + new_offsets[None, :]) % input_curve.size
        mean_dist = cost_landscape[ref_idx[:, None], new_idx_all].mean(axis=0)
        self.compare_angle = self._ref_angle - np.arctan2(*self._centroid_vectors[new_offsets[mean_dist.argmin()]])
        return mean_dist.min()
<|end_body_1|>

<|body_start_2|>
        areas_num, labels, stats, centroids = cv2.connectedComponentsWithStats(mask.astype(np.uint8))
        assert areas_num == 2, 'Multiple blobs in mask'
        contours, hierarchy = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        contour = np.squeeze(np.vstack(contours))
        centroid_uv = vt.uv_centroid(mask)
        self._centroid_vectors = contour - centroid_uv
        return np.linalg.norm(self._centroid_vectors, axis=1)
<|end_body_2|>

Solution:
class OutlineComparer:
    def __init__(self, training_mask):
        """Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/training object. :type training_mask: np.ndarray"""
        self.compare_angle = None
        self._centroid_vectors = None
        self.ref_curve = self._calc_dist_curve(training_mask)
        self._ref_angle = np.arctan2(*self._centroid_vectors[0])

    def outline_distance(self, query_mask):
        """Compare query blob to reference blob in terms of average contour distance. Further, an approximate angle difference is determined. :param query_mask: Binary mask with a single blob to compare against reference blob :type query_mask: np.ndarray :return: The average pixel distance between query blob and reference blob :rtype: float"""
        input_curve = self._calc_dist_curve(query_mask)
        cost_landscape = np.abs(self.ref_curve[:, None] - input_curve[None, :])
        n_eval = min(500, self.ref_curve.size, input_curve.size)
        ref_idx = vt.intr(np.linspace(0, self.ref_curve.size - 1, n_eval))
        new_idx = vt.intr(np.linspace(0, input_curve.size - 1, n_eval))
        new_offsets = vt.intr(np.linspace(0, input_curve.size - 1, 101))[:-1]
        new_idx_all = (new_idx[:, None] + new_offsets[None, :]) % input_curve.size
        mean_dist = cost_landscape[ref_idx[:, None], new_idx_all].mean(axis=0)
        self.compare_angle = self._ref_angle - np.arctan2(*self._centroid_vectors[new_offsets[mean_dist.argmin()]])
        return mean_dist.min()

    def _calc_dist_curve(self, mask):
        """Calculate the distance from mask centroid to each contour pixel. :param mask: Binary mask with a single blob to calculate distance curve for :type mask: np.ndarray :return: Distance curve. :rtype: np.ndarray"""
        areas_num, labels, stats, centroids = cv2.connectedComponentsWithStats(mask.astype(np.uint8))
        assert areas_num == 2, 'Multiple blobs in mask'
        contours, hierarchy = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        contour = np.squeeze(np.vstack(contours))
        centroid_uv = vt.uv_centroid(mask)
        self._centroid_vectors = contour - centroid_uv
        return np.linalg.norm(self._centroid_vectors, axis=1)