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

Class description:
AutomaticRoutine that parks a qubit at the specified spot where it performs an AdaptiveQubitSpectroscopy routine to find its ge_freq. The flux and voltage, together with initial and measured values for ge_freq, can be retrieved from the `results` attribute. In the case where the requested parking point is different from the designated one (`qubit.flux_parking()`): 1. The initial qubit frequency will be calculated according to the flux. 2. The measured qubit frequency will not be updated at the end of the routine. Examples:: settings_user = { 'ParkAndQubitSpectroscopy': {'General': { 'flux': '{designated}'}}, 'AdaptiveQubitSpectroscopy': {'General': {'n_spectroscopies': 1, 'max_iterations': 2

Method signatures and docstrings:
- def __init__(self, dev, qubits: List[QuDev_transmon], fluxlines_dict, **kw): Initializes the ParkAndQubitSpectroscopy routine. The fluxes and/or voltages at which the AdaptiveQubitSpectroscopies are run are specified with the settings of SetBiasVoltageAndFluxPulseAssistedReadOut. If no settings are specified there, it is possible to use the 'General' scope of ParkAndQubitSpectroscopy. In this case (i.e., no settings are specified for SetBiasVoltageAndFluxPulseAssistedReadOut), "flux" or "voltage" can be specified as a keyword argument of ParkAndQubitSpectroscopy. Args: dev (Device): Device that is being measured. qubits (list[QuDev_transmon]): List of qubits that should be measured. fluxlines_dict (dict): fluxlines_dict object for accessing and changing the dac volta
- def create_routine_template(self): Creates routine template.
- def post_run(self): Save the results of the routine.

Implement the Python class `ParkAndQubitSpectroscopy` described below.

Class description:
AutomaticRoutine that parks a qubit at the specified spot where it performs an AdaptiveQubitSpectroscopy routine to find its ge_freq. The flux and voltage, together with initial and measured values for ge_freq, can be retrieved from the `results` attribute. In the case where the requested parking point is different from the designated one (`qubit.flux_parking()`): 1. The initial qubit frequency will be calculated according to the flux. 2. The measured qubit frequency will not be updated at the end of the routine. Examples:: settings_user = { 'ParkAndQubitSpectroscopy': {'General': { 'flux': '{designated}'}}, 'AdaptiveQubitSpectroscopy': {'General': {'n_spectroscopies': 1, 'max_iterations': 2

Method signatures and docstrings:
- def __init__(self, dev, qubits: List[QuDev_transmon], fluxlines_dict, **kw): Initializes the ParkAndQubitSpectroscopy routine. The fluxes and/or voltages at which the AdaptiveQubitSpectroscopies are run are specified with the settings of SetBiasVoltageAndFluxPulseAssistedReadOut. If no settings are specified there, it is possible to use the 'General' scope of ParkAndQubitSpectroscopy. In this case (i.e., no settings are specified for SetBiasVoltageAndFluxPulseAssistedReadOut), "flux" or "voltage" can be specified as a keyword argument of ParkAndQubitSpectroscopy. Args: dev (Device): Device that is being measured. qubits (list[QuDev_transmon]): List of qubits that should be measured. fluxlines_dict (dict): fluxlines_dict object for accessing and changing the dac volta
- def create_routine_template(self): Creates routine template.
- def post_run(self): Save the results of the routine.

<|skeleton|>
class ParkAndQubitSpectroscopy:
    """AutomaticRoutine that parks a qubit at the specified spot where it performs an AdaptiveQubitSpectroscopy routine to find its ge_freq. The flux and voltage, together with initial and measured values for ge_freq, can be retrieved from the `results` attribute. In the case where the requested parking point is different from the designated one (`qubit.flux_parking()`): 1. The initial qubit frequency will be calculated according to the flux. 2. The measured qubit frequency will not be updated at the end of the routine. Examples:: settings_user = { 'ParkAndQubitSpectroscopy': {'General': { 'flux': '{designated}'}}, 'AdaptiveQubitSpectroscopy': {'General': {'n_spectroscopies': 1, 'max_iterations': 2"""

    def __init__(self, dev, qubits: List[QuDev_transmon], fluxlines_dict, **kw):
        """Initializes the ParkAndQubitSpectroscopy routine. The fluxes and/or voltages at which the AdaptiveQubitSpectroscopies are run are specified with the settings of SetBiasVoltageAndFluxPulseAssistedReadOut. If no settings are specified there, it is possible to use the 'General' scope of ParkAndQubitSpectroscopy. In this case (i.e., no settings are specified for SetBiasVoltageAndFluxPulseAssistedReadOut), "flux" or "voltage" can be specified as a keyword argument of ParkAndQubitSpectroscopy. Args: dev (Device): Device that is being measured. qubits (list[QuDev_transmon]): List of qubits that should be measured. fluxlines_dict (dict): fluxlines_dict object for accessing and changing the dac volta"""
        <|body_0|>

    def create_routine_template(self):
        """Creates routine template."""
        <|body_1|>

    def post_run(self):
        """Save the results of the routine."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(dev=dev, qubits=qubits, fluxlines_dict=fluxlines_dict, **kw)
        self.fluxlines_dict = fluxlines_dict
        routines_utils.append_DCsources(self)
        self.results: Dict[str, ParkAndQubitSpectroscopyResults] = {}
        for qb in self.qubits:
            flux, voltage = routines_utils.get_qubit_flux_and_voltage(qb=qb, fluxlines_dict=self.fluxlines_dict, flux=self.get_param_value('flux', qubit=qb.name), voltage=self.get_param_value('voltage', qubit=qb.name))
            self.results[qb.name] = ParkAndQubitSpectroscopyResults(**dict(voltage=voltage, flux=flux))
            self.qubits_frequencies = []
            if flux != qb.flux_parking() or not qb.ge_freq():
                transmon_freq_model = routines_utils.get_transmon_freq_model(qb)
                updated_frequency = qb.calculate_frequency(flux=flux, model=transmon_freq_model)
            else:
                updated_frequency = qb.ge_freq()
            if flux != qb.flux_parking():
                self.settings[self.step_label]['General']['update'] = False
            self.results[qb.name].initial_ge_freq = updated_frequency
            self.qubits_frequencies.append(updated_frequency)
        self._DEFAULT_ROUTINE_TEMPLATE = RoutineTemplate([[self.SetBiasVoltageAndFluxPulseAssistedReadOut, 'set_bias_voltage_and_fp_assisted_ro', {}], [UpdateFrequency, 'update_frequency', {'transition': 'ge', 'frequencies': self.qubits_frequencies}], [AdaptiveQubitSpectroscopy, 'adaptive_qubit_spectroscopy', {}]])
        self.final_init(**kw)
<|end_body_0|>

<|body_start_1|>
        super().create_routine_template()
        for i, step in reversed(list(enumerate(self.routine_template))):
            self.split_step_for_parallel_groups(index=i)
<|end_body_1|>

<|body_start_2|>
        for qb in self.qubits:
            self.results[qb.name].measured_ge_freq = qb.ge_freq()
        if self.routine is None:
            for qb in self.qubits:
                if qb.flux_parking() != self.results[qb.name].flux:
                    log.warning(f'The routine results will not be updated since {qb.name} was not measured at its designated sweet spot')
                    self.settings[self.step_label]['General']['update'] = False
        super().post_run()
<|end_body_2|>

Solution:
class ParkAndQubitSpectroscopy:
    """AutomaticRoutine that parks a qubit at the specified spot where it performs an AdaptiveQubitSpectroscopy routine to find its ge_freq. The flux and voltage, together with initial and measured values for ge_freq, can be retrieved from the `results` attribute. In the case where the requested parking point is different from the designated one (`qubit.flux_parking()`): 1. The initial qubit frequency will be calculated according to the flux. 2. The measured qubit frequency will not be updated at the end of the routine. Examples:: settings_user = { 'ParkAndQubitSpectroscopy': {'General': { 'flux': '{designated}'}}, 'AdaptiveQubitSpectroscopy': {'General': {'n_spectroscopies': 1, 'max_iterations': 2"""

    def __init__(self, dev, qubits: List[QuDev_transmon], fluxlines_dict, **kw):
        """Initializes the ParkAndQubitSpectroscopy routine. The fluxes and/or voltages at which the AdaptiveQubitSpectroscopies are run are specified with the settings of SetBiasVoltageAndFluxPulseAssistedReadOut. If no settings are specified there, it is possible to use the 'General' scope of ParkAndQubitSpectroscopy. In this case (i.e., no settings are specified for SetBiasVoltageAndFluxPulseAssistedReadOut), "flux" or "voltage" can be specified as a keyword argument of ParkAndQubitSpectroscopy. Args: dev (Device): Device that is being measured. qubits (list[QuDev_transmon]): List of qubits that should be measured. fluxlines_dict (dict): fluxlines_dict object for accessing and changing the dac volta"""
        super().__init__(dev=dev, qubits=qubits, fluxlines_dict=fluxlines_dict, **kw)
        self.fluxlines_dict = fluxlines_dict
        routines_utils.append_DCsources(self)
        self.results: Dict[str, ParkAndQubitSpectroscopyResults] = {}
        for qb in self.qubits:
            flux, voltage = routines_utils.get_qubit_flux_and_voltage(qb=qb, fluxlines_dict=self.fluxlines_dict, flux=self.get_param_value('flux', qubit=qb.name), voltage=self.get_param_value('voltage', qubit=qb.name))
            self.results[qb.name] = ParkAndQubitSpectroscopyResults(**dict(voltage=voltage, flux=flux))
            self.qubits_frequencies = []
            if flux != qb.flux_parking() or not qb.ge_freq():
                transmon_freq_model = routines_utils.get_transmon_freq_model(qb)
                updated_frequency = qb.calculate_frequency(flux=flux, model=transmon_freq_model)
            else:
                updated_frequency = qb.ge_freq()
            if flux != qb.flux_parking():
                self.settings[self.step_label]['General']['update'] = False
            self.results[qb.name].initial_ge_freq = updated_frequency
            self.qubits_frequencies.append(updated_frequency)
        self._DEFAULT_ROUTINE_TEMPLATE = RoutineTemplate([[self.SetBiasVoltageAndFluxPulseAssistedReadOut, 'set_bias_voltage_and_fp_assisted_ro', {}], [UpdateFrequency, 'update_frequency', {'transition': 'ge', 'frequencies': self.qubits_frequencies}], [AdaptiveQubitSpectroscopy, 'adaptive_qubit_spectroscopy', {}]])
        self.final_init(**kw)

    def create_routine_template(self):
        """Creates routine template."""
        super().create_routine_template()
        for i, step in reversed(list(enumerate(self.routine_template))):
            self.split_step_for_parallel_groups(index=i)

    def post_run(self):
        """Save the results of the routine."""
        for qb in self.qubits:
            self.results[qb.name].measured_ge_freq = qb.ge_freq()
        if self.routine is None:
            for qb in self.qubits:
                if qb.flux_parking() != self.results[qb.name].flux:
                    log.warning(f'The routine results will not be updated since {qb.name} was not measured at its designated sweet spot')
                    self.settings[self.step_label]['General']['update'] = False
        super().post_run()


canIWin

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def canIWin(self, maxChoosableInteger, desiredTotal): :type maxChoosableInteger: int :type desiredTotal: int :rtype: bool
- def dfs(self, total, choose): 深度搜索 :type total: int :type choose: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def canIWin(self, maxChoosableInteger, desiredTotal): :type maxChoosableInteger: int :type desiredTotal: int :rtype: bool
- def dfs(self, total, choose): 深度搜索 :type total: int :type choose: int

<|skeleton|>
class Solution:

    def canIWin(self, maxChoosableInteger, desiredTotal):
        """:type maxChoosableInteger: int :type desiredTotal: int :rtype: bool"""
        <|body_0|>

    def dfs(self, total, choose):
        """深度搜索 :type total: int :type choose: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.max_num = maxChoosableInteger
        if (maxChoosableInteger + 1) * maxChoosableInteger / 2 <= desiredTotal or desiredTotal < 0:
            return False
        if maxChoosableInteger >= desiredTotal:
            return True
        self.d = dict()
        return self.dfs(desiredTotal, 0)
<|end_body_0|>

<|body_start_1|>
        if total <= 0:
            return False
        if choose in self.d:
            return self.d[choose]
        for i in range(1, self.max_num + 1):
            if choose ^ 1 << i < choose:
                continue
            next_choose = choose | 1 << i
            if not self.dfs(total - i, next_choose):
                self.d[choose] = True
                return True
        self.d[choose] = False
        return False
<|end_body_1|>

Solution:
class Solution:
    def canIWin(self, maxChoosableInteger, desiredTotal):
        """:type maxChoosableInteger: int :type desiredTotal: int :rtype: bool"""
        self.max_num = maxChoosableInteger
        if (maxChoosableInteger + 1) * maxChoosableInteger / 2 <= desiredTotal or desiredTotal < 0:
            return False
        if maxChoosableInteger >= desiredTotal:
            return True
        self.d = dict()
        return self.dfs(desiredTotal, 0)

    def dfs(self, total, choose):
        """深度搜索 :type total: int :type choose: int"""
        if total <= 0:
            return False
        if choose in self.d:
            return self.d[choose]
        for i in range(1, self.max_num + 1):
            if choose ^ 1 << i < choose:
                continue
            next_choose = choose | 1 << i
            if not self.dfs(total - i, next_choose):
                self.d[choose] = True
                return True
        self.d[choose] = False
        return False


getMsg

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

Class description:
Used when a worker thread is told to do lengthy things to a shared resource (say a gui window) and only the last (whenever that may arrive) one matters. Like a 1 deep queue which only contains the last added entry.

Method signatures and docstrings:
- def __init__(self, msg=None, lock=None): 'msg' is an initial value. 'lock' is an optional lock to be used else a new one is allocated.
- def setMsg(self, msg): place the message and ensure the worker thread is notified is waiting
- def getMsg(self, timeOut=None): get the latest message else wait for one

Implement the Python class `LatestMessage` described below.

Class description:
Used when a worker thread is told to do lengthy things to a shared resource (say a gui window) and only the last (whenever that may arrive) one matters. Like a 1 deep queue which only contains the last added entry.

Method signatures and docstrings:
- def __init__(self, msg=None, lock=None): 'msg' is an initial value. 'lock' is an optional lock to be used else a new one is allocated.
- def setMsg(self, msg): place the message and ensure the worker thread is notified is waiting
- def getMsg(self, timeOut=None): get the latest message else wait for one

<|skeleton|>
class LatestMessage:
    """Used when a worker thread is told to do lengthy things to a shared resource (say a gui window) and only the last (whenever that may arrive) one matters. Like a 1 deep queue which only contains the last added entry."""

    def __init__(self, msg=None, lock=None):
        """'msg' is an initial value. 'lock' is an optional lock to be used else a new one is allocated."""
        <|body_0|>

    def setMsg(self, msg):
        """place the message and ensure the worker thread is notified is waiting"""
        <|body_1|>

    def getMsg(self, timeOut=None):
        """get the latest message else wait for one"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        object.__init__(self)
        self.stateLock = threading.Condition() if lock is None else lock
        self.msg = msg
<|end_body_0|>

<|body_start_1|>
        self.stateLock.acquire()
        try:
            self.msg = msg
        finally:
            self.stateLock.notify()
            self.stateLock.release()
<|end_body_1|>

<|body_start_2|>
        self.stateLock.acquire()
        while self.msg is None:
            self.stateLock.wait(timeOut)
        msg = self.msg
        self.msg = None
        self.stateLock.release()
        return msg
<|end_body_2|>

Solution:
class LatestMessage:
    """Used when a worker thread is told to do lengthy things to a shared resource (say a gui window) and only the last (whenever that may arrive) one matters. Like a 1 deep queue which only contains the last added entry."""

    def __init__(self, msg=None, lock=None):
        """'msg' is an initial value. 'lock' is an optional lock to be used else a new one is allocated."""
        object.__init__(self)
        self.stateLock = threading.Condition() if lock is None else lock
        self.msg = msg

    def setMsg(self, msg):
        """place the message and ensure the worker thread is notified is waiting"""
        self.stateLock.acquire()
        try:
            self.msg = msg
        finally:
            self.stateLock.notify()
            self.stateLock.release()

    def getMsg(self, timeOut=None):
        """get the latest message else wait for one"""
        self.stateLock.acquire()
        while self.msg is None:
            self.stateLock.wait(timeOut)
        msg = self.msg
        self.msg = None
        self.stateLock.release()
        return msg


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def Find_1(self, target, array): 方法一：常规扫描整个二维数组。时间复杂度为O(n*m) 行数*列数，可视为O(n^2) 没有用到 每行从左到右递增、每列从上到下递增 的特性
- def Find_2(self, target, array): 方法二：利用 每行从左到右递增、每列从上到下递增 的特性，减少扫描、比较次数。时间复杂度为O(n)

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def Find_1(self, target, array): 方法一：常规扫描整个二维数组。时间复杂度为O(n*m) 行数*列数，可视为O(n^2) 没有用到 每行从左到右递增、每列从上到下递增 的特性
- def Find_2(self, target, array): 方法二：利用 每行从左到右递增、每列从上到下递增 的特性，减少扫描、比较次数。时间复杂度为O(n)

<|skeleton|>
class Solution:

    def Find_1(self, target, array):
        """方法一：常规扫描整个二维数组。时间复杂度为O(n*m) 行数*列数，可视为O(n^2) 没有用到 每行从左到右递增、每列从上到下递增 的特性"""
        <|body_0|>

    def Find_2(self, target, array):
        """方法二：利用 每行从左到右递增、每列从上到下递增 的特性，减少扫描、比较次数。时间复杂度为O(n)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        row_num = len(array)
        for i in range(row_num):
            col_num = len(array[i])
            for j in range(col_num):
                if array[i][j] == target:
                    return True
        return False
<|end_body_0|>

<|body_start_1|>
        row_num = 0
        col_num = len(array[0]) - 1
        row_count = len(array)
        while row_num < row_count and col_num >= 0:
            val = array[row_num][col_num]
            if val == target:
                return True
            elif val > target:
                col_num -= 1
            else:
                row_num += 1
        return False
<|end_body_1|>

Solution:
class Solution:
    def Find_1(self, target, array):
        """方法一：常规扫描整个二维数组。时间复杂度为O(n*m) 行数*列数，可视为O(n^2) 没有用到 每行从左到右递增、每列从上到下递增 的特性"""
        row_num = len(array)
        for i in range(row_num):
            col_num = len(array[i])
            for j in range(col_num):
                if array[i][j] == target:
                    return True
        return False

    def Find_2(self, target, array):
        """方法二：利用 每行从左到右递增、每列从上到下递增 的特性，减少扫描、比较次数。时间复杂度为O(n)"""
        row_num = 0
        col_num = len(array[0]) - 1
        row_count = len(array)
        while row_num < row_count and col_num >= 0:
            val = array[row_num][col_num]
            if val == target:
                return True
            elif val > target:
                col_num -= 1
            else:
                row_num += 1
        return False


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

Class description:
Responsible for launching a python executor. The executor will be launched in the same process of the rest of the component, i.e. its driver and publisher.

Method signatures and docstrings:
- def can_launch(cls, component_executor_spec: executor_spec.ExecutorSpec, component_config: base_component_config.BaseComponentConfig) -> bool: Checks if the launcher can launch the executor spec.
- def _run_executor(self, execution_id: int, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: Execute underlying component implementation.

Implement the Python class `InProcessComponentLauncher` described below.

Class description:
Responsible for launching a python executor. The executor will be launched in the same process of the rest of the component, i.e. its driver and publisher.

Method signatures and docstrings:
- def can_launch(cls, component_executor_spec: executor_spec.ExecutorSpec, component_config: base_component_config.BaseComponentConfig) -> bool: Checks if the launcher can launch the executor spec.
- def _run_executor(self, execution_id: int, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: Execute underlying component implementation.

<|skeleton|>
class InProcessComponentLauncher:
    """Responsible for launching a python executor. The executor will be launched in the same process of the rest of the component, i.e. its driver and publisher."""

    def can_launch(cls, component_executor_spec: executor_spec.ExecutorSpec, component_config: base_component_config.BaseComponentConfig) -> bool:
        """Checks if the launcher can launch the executor spec."""
        <|body_0|>

    def _run_executor(self, execution_id: int, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None:
        """Execute underlying component implementation."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if component_config:
            return False
        return isinstance(component_executor_spec, executor_spec.ExecutorClassSpec)
<|end_body_0|>

<|body_start_1|>
        executor_class_spec = cast(executor_spec.ExecutorClassSpec, self._component_executor_spec)
        if issubclass(executor_class_spec.executor_class, base_beam_executor.BaseBeamExecutor):
            executor_context = base_beam_executor.BaseBeamExecutor.Context(beam_pipeline_args=self._beam_pipeline_args, tmp_dir=os.path.join(self._pipeline_info.pipeline_root, '.temp', ''), unique_id=str(execution_id))
        else:
            executor_context = base_executor.BaseExecutor.Context(extra_flags=self._beam_pipeline_args, tmp_dir=os.path.join(self._pipeline_info.pipeline_root, '.temp', ''), unique_id=str(execution_id))
        executor = executor_class_spec.executor_class(executor_context)
        executor.Do(copy.deepcopy(input_dict), output_dict, copy.deepcopy(exec_properties))
<|end_body_1|>

Solution:
class InProcessComponentLauncher:
    """Responsible for launching a python executor. The executor will be launched in the same process of the rest of the component, i.e. its driver and publisher."""

    def can_launch(cls, component_executor_spec: executor_spec.ExecutorSpec, component_config: base_component_config.BaseComponentConfig) -> bool:
        """Checks if the launcher can launch the executor spec."""
        if component_config:
            return False
        return isinstance(component_executor_spec, executor_spec.ExecutorClassSpec)

    def _run_executor(self, execution_id: int, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None:
        """Execute underlying component implementation."""
        executor_class_spec = cast(executor_spec.ExecutorClassSpec, self._component_executor_spec)
        if issubclass(executor_class_spec.executor_class, base_beam_executor.BaseBeamExecutor):
            executor_context = base_beam_executor.BaseBeamExecutor.Context(beam_pipeline_args=self._beam_pipeline_args, tmp_dir=os.path.join(self._pipeline_info.pipeline_root, '.temp', ''), unique_id=str(execution_id))
        else:
            executor_context = base_executor.BaseExecutor.Context(extra_flags=self._beam_pipeline_args, tmp_dir=os.path.join(self._pipeline_info.pipeline_root, '.temp', ''), unique_id=str(execution_id))
        executor = executor_class_spec.executor_class(executor_context)
        executor.Do(copy.deepcopy(input_dict), output_dict, copy.deepcopy(exec_properties))


call

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

Class description:
Compute the focal loss between `logits` and the golden `target` values. Focal loss = -(1-pt)^gamma * log(pt) where pt is the probability of being classified to the true class.

Method signatures and docstrings:
- def __init__(self, alpha, gamma, label_smoothing=0.0, **kwargs): Initialize focal loss. Args: alpha: A float32 scalar multiplying alpha to the loss from positive examples and (1-alpha) to the loss from negative examples. gamma: A float32 scalar modulating loss from hard and easy examples. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. **kwargs: other params.
- def call(self, y, y_pred): Compute focal loss for y and y_pred. Args: y: A tuple of (normalizer, y_true), where y_true is the target class. y_pred: A float32 tensor [batch, height_in, width_in, num_predictions]. Returns: the focal loss.

Implement the Python class `FocalLoss` described below.

Class description:
Compute the focal loss between `logits` and the golden `target` values. Focal loss = -(1-pt)^gamma * log(pt) where pt is the probability of being classified to the true class.

Method signatures and docstrings:
- def __init__(self, alpha, gamma, label_smoothing=0.0, **kwargs): Initialize focal loss. Args: alpha: A float32 scalar multiplying alpha to the loss from positive examples and (1-alpha) to the loss from negative examples. gamma: A float32 scalar modulating loss from hard and easy examples. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. **kwargs: other params.
- def call(self, y, y_pred): Compute focal loss for y and y_pred. Args: y: A tuple of (normalizer, y_true), where y_true is the target class. y_pred: A float32 tensor [batch, height_in, width_in, num_predictions]. Returns: the focal loss.

<|skeleton|>
class FocalLoss:
    """Compute the focal loss between `logits` and the golden `target` values. Focal loss = -(1-pt)^gamma * log(pt) where pt is the probability of being classified to the true class."""

    def __init__(self, alpha, gamma, label_smoothing=0.0, **kwargs):
        """Initialize focal loss. Args: alpha: A float32 scalar multiplying alpha to the loss from positive examples and (1-alpha) to the loss from negative examples. gamma: A float32 scalar modulating loss from hard and easy examples. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. **kwargs: other params."""
        <|body_0|>

    def call(self, y, y_pred):
        """Compute focal loss for y and y_pred. Args: y: A tuple of (normalizer, y_true), where y_true is the target class. y_pred: A float32 tensor [batch, height_in, width_in, num_predictions]. Returns: the focal loss."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        self.alpha = alpha
        self.gamma = gamma
        self.label_smoothing = label_smoothing
<|end_body_0|>

<|body_start_1|>
        normalizer, y_true = y
        alpha = tf.convert_to_tensor(self.alpha, dtype=y_pred.dtype)
        gamma = tf.convert_to_tensor(self.gamma, dtype=y_pred.dtype)
        pred_prob = tf.sigmoid(y_pred)
        p_t = y_true * pred_prob + (1 - y_true) * (1 - pred_prob)
        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)
        modulating_factor = (1.0 - p_t) ** gamma
        y_true = y_true * (1.0 - self.label_smoothing) + 0.5 * self.label_smoothing
        ce = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)
        return alpha_factor * modulating_factor * ce / normalizer
<|end_body_1|>

Solution:
class FocalLoss:
    """Compute the focal loss between `logits` and the golden `target` values. Focal loss = -(1-pt)^gamma * log(pt) where pt is the probability of being classified to the true class."""

    def __init__(self, alpha, gamma, label_smoothing=0.0, **kwargs):
        """Initialize focal loss. Args: alpha: A float32 scalar multiplying alpha to the loss from positive examples and (1-alpha) to the loss from negative examples. gamma: A float32 scalar modulating loss from hard and easy examples. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. **kwargs: other params."""
        super().__init__(**kwargs)
        self.alpha = alpha
        self.gamma = gamma
        self.label_smoothing = label_smoothing

    def call(self, y, y_pred):
        """Compute focal loss for y and y_pred. Args: y: A tuple of (normalizer, y_true), where y_true is the target class. y_pred: A float32 tensor [batch, height_in, width_in, num_predictions]. Returns: the focal loss."""
        normalizer, y_true = y
        alpha = tf.convert_to_tensor(self.alpha, dtype=y_pred.dtype)
        gamma = tf.convert_to_tensor(self.gamma, dtype=y_pred.dtype)
        pred_prob = tf.sigmoid(y_pred)
        p_t = y_true * pred_prob + (1 - y_true) * (1 - pred_prob)
        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)
        modulating_factor = (1.0 - p_t) ** gamma
        y_true = y_true * (1.0 - self.label_smoothing) + 0.5 * self.label_smoothing
        ce = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)
        return alpha_factor * modulating_factor * ce / normalizer


delete

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

Class description:
Implement the ApiGroupMembers class.

Method signatures and docstrings:
- def get(self, group_id=None): 获取组成员信息 :param group_id: :return:
- def post(self): 添加组成员 :return:
- def put(self): 编辑组成员角色 :return:
- def delete(self, group_id, user_id): 删除组成员 :param group_id: :param user_id: :return:

Implement the Python class `ApiGroupMembers` described below.

Class description:
Implement the ApiGroupMembers class.

Method signatures and docstrings:
- def get(self, group_id=None): 获取组成员信息 :param group_id: :return:
- def post(self): 添加组成员 :return:
- def put(self): 编辑组成员角色 :return:
- def delete(self, group_id, user_id): 删除组成员 :param group_id: :param user_id: :return:

<|skeleton|>
class ApiGroupMembers:

    def get(self, group_id=None):
        """获取组成员信息 :param group_id: :return:"""
        <|body_0|>

    def post(self):
        """添加组成员 :return:"""
        <|body_1|>

    def put(self):
        """编辑组成员角色 :return:"""
        <|body_2|>

    def delete(self, group_id, user_id):
        """删除组成员 :param group_id: :param user_id: :return:"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        result = {'result': 'NG'}
        ctrl_obj = CtrlGroup()
        content = ctrl_obj.get_group_members(group_id)
        if content:
            result['result'] = 'OK'
            result['content'] = content
        return result
<|end_body_0|>

<|body_start_1|>
        json_data = request.get_json(force=True)
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.add_group_members(json_data)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result
<|end_body_1|>

<|body_start_2|>
        json_data = request.get_json(force=True)
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.update_member_role(json_data)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result
<|end_body_2|>

<|body_start_3|>
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.delete_member(group_id, user_id)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result
<|end_body_3|>

Solution:
class ApiGroupMembers:
    def get(self, group_id=None):
        """获取组成员信息 :param group_id: :return:"""
        result = {'result': 'NG'}
        ctrl_obj = CtrlGroup()
        content = ctrl_obj.get_group_members(group_id)
        if content:
            result['result'] = 'OK'
            result['content'] = content
        return result

    def post(self):
        """添加组成员 :return:"""
        json_data = request.get_json(force=True)
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.add_group_members(json_data)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result

    def put(self):
        """编辑组成员角色 :return:"""
        json_data = request.get_json(force=True)
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.update_member_role(json_data)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result

    def delete(self, group_id, user_id):
        """删除组成员 :param group_id: :param user_id: :return:"""
        ctrl_obj = CtrlGroup()
        result = {'result': 'NG', 'error': ''}
        try:
            ctrl_obj.delete_member(group_id, user_id)
            result['result'] = 'OK'
        except Exception as e:
            current_app.logger.error('%s' % str(e))
            result['error'] = str(e)
        return result


deserialize

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

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

<|skeleton|>
class Codec:

    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        <|body_0|>

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        def inorderSerialize(node):
            if not node:
                return ''
            return inorderSerialize(node.left) + str(node.val) + inorderSerialize(node.right)

        def preorderSerialize(node):
            if not node:
                return ''
            return str(node.val) + preorderSerialize(node.left) + preorderSerialize(node.right)
        s1 = s2 = root
        return inorderSerialize(s1) + '/' + preorderSerialize(s2)
<|end_body_0|>

<|body_start_1|>
        def constructTree(inorderList, preorderList):
            if not inorderList:
                return None
            r_val = preorderList.pop(0)
            r_index = inorderList.index(r_val)
            root = TreeNode(r_val)
            root.left = constructTree(inorderList[:r_index], preorderList)
            root.right = constructTree(inorderList[r_index + 1:], preorderList)
            return root
        inorder, preorder = tuple(data.split('/'))
        return constructTree([int(s) for s in inorder], [int(s) for s in preorder])
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        def inorderSerialize(node):
            if not node:
                return ''
            return inorderSerialize(node.left) + str(node.val) + inorderSerialize(node.right)

        def preorderSerialize(node):
            if not node:
                return ''
            return str(node.val) + preorderSerialize(node.left) + preorderSerialize(node.right)
        s1 = s2 = root
        return inorderSerialize(s1) + '/' + preorderSerialize(s2)

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        def constructTree(inorderList, preorderList):
            if not inorderList:
                return None
            r_val = preorderList.pop(0)
            r_index = inorderList.index(r_val)
            root = TreeNode(r_val)
            root.left = constructTree(inorderList[:r_index], preorderList)
            root.right = constructTree(inorderList[r_index + 1:], preorderList)
            return root
        inorder, preorder = tuple(data.split('/'))
        return constructTree([int(s) for s in inorder], [int(s) for s in preorder])


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

Class description:
A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values.

Method signatures and docstrings:
- def validate_type(x, valid_type): Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type: type or tuple of type A tuple, as in ``validate_type(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_type(x, A) or validate_type(x, B) or ...`` etc. Returns ------- out: True or raise TypeError
- def validate_value(x, valid_value): Check whether an object's value is one of `valid_type`. Parameters ---------- x: object object to be verified valid_value: tuple, list A tuple, as in ``validate_value(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_value(x, A) or validate_value(x, B) or ...`` etc. Returns ------- out: True or raise TypeError
- def to_device(*args, device=None): transfer all of ``args`` to ``device``

Implement the Python class `Validation` described below.

Class description:
A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values.

Method signatures and docstrings:
- def validate_type(x, valid_type): Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type: type or tuple of type A tuple, as in ``validate_type(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_type(x, A) or validate_type(x, B) or ...`` etc. Returns ------- out: True or raise TypeError
- def validate_value(x, valid_value): Check whether an object's value is one of `valid_type`. Parameters ---------- x: object object to be verified valid_value: tuple, list A tuple, as in ``validate_value(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_value(x, A) or validate_value(x, B) or ...`` etc. Returns ------- out: True or raise TypeError
- def to_device(*args, device=None): transfer all of ``args`` to ``device``

<|skeleton|>
class Validation:
    """A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values."""

    def validate_type(x, valid_type):
        """Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type: type or tuple of type A tuple, as in ``validate_type(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_type(x, A) or validate_type(x, B) or ...`` etc. Returns ------- out: True or raise TypeError"""
        <|body_0|>

    def validate_value(x, valid_value):
        """Check whether an object's value is one of `valid_type`. Parameters ---------- x: object object to be verified valid_value: tuple, list A tuple, as in ``validate_value(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_value(x, A) or validate_value(x, B) or ...`` etc. Returns ------- out: True or raise TypeError"""
        <|body_1|>

    def to_device(*args, device=None):
        """transfer all of ``args`` to ``device``"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if isinstance(x, valid_type):
            pass
        else:
            raise TypeError(f'Expected type of {x} is an instance of {valid_type}, but got ``{type(x)}``.')
<|end_body_0|>

<|body_start_1|>
        if x in valid_value:
            pass
        else:
            raise ValueError(f'Expected `x` is one of {valid_value}, but got ``{x}``.')
<|end_body_1|>

<|body_start_2|>
        out = []
        for each in args:
            if isinstance(each, np.ndarray):
                each = torch.tensor(each, device=device)
            elif isinstance(each, torch.Tensor):
                each = each.to(device=device)
            else:
                raise TypeError(f'Expected type of the args is ``np.ndarray` or ``torch.Tensor``, but got ``{type(each)}``.')
            out.append(each)
        if len(out) > 1:
            return tuple(out)
        else:
            return out[0]
<|end_body_2|>

Solution:
class Validation:
    """A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values."""

    def validate_type(x, valid_type):
        """Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type: type or tuple of type A tuple, as in ``validate_type(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_type(x, A) or validate_type(x, B) or ...`` etc. Returns ------- out: True or raise TypeError"""
        if isinstance(x, valid_type):
            pass
        else:
            raise TypeError(f'Expected type of {x} is an instance of {valid_type}, but got ``{type(x)}``.')

    def validate_value(x, valid_value):
        """Check whether an object's value is one of `valid_type`. Parameters ---------- x: object object to be verified valid_value: tuple, list A tuple, as in ``validate_value(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_value(x, A) or validate_value(x, B) or ...`` etc. Returns ------- out: True or raise TypeError"""
        if x in valid_value:
            pass
        else:
            raise ValueError(f'Expected `x` is one of {valid_value}, but got ``{x}``.')

    def to_device(*args, device=None):
        """transfer all of ``args`` to ``device``"""
        out = []
        for each in args:
            if isinstance(each, np.ndarray):
                each = torch.tensor(each, device=device)
            elif isinstance(each, torch.Tensor):
                each = each.to(device=device)
            else:
                raise TypeError(f'Expected type of the args is ``np.ndarray` or ``torch.Tensor``, but got ``{type(each)}``.')
            out.append(each)
        if len(out) > 1:
            return tuple(out)
        else:
            return out[0]


revert

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

Class description:
Task to allocate a VIP.

Method signatures and docstrings:
- def execute(self, loadbalancer): Allocate a vip to the loadbalancer.
- def revert(self, result, loadbalancer, *args, **kwargs): Handle a failure to allocate vip.

Implement the Python class `AllocateVIP` described below.

Class description:
Task to allocate a VIP.

Method signatures and docstrings:
- def execute(self, loadbalancer): Allocate a vip to the loadbalancer.
- def revert(self, result, loadbalancer, *args, **kwargs): Handle a failure to allocate vip.

<|skeleton|>
class AllocateVIP:
    """Task to allocate a VIP."""

    def execute(self, loadbalancer):
        """Allocate a vip to the loadbalancer."""
        <|body_0|>

    def revert(self, result, loadbalancer, *args, **kwargs):
        """Handle a failure to allocate vip."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        LOG.debug('Allocating vip with port id %s, subnet id %s, ip address %s for load balancer %s', loadbalancer[constants.VIP_PORT_ID], loadbalancer[constants.VIP_SUBNET_ID], loadbalancer[constants.VIP_ADDRESS], loadbalancer[constants.LOADBALANCER_ID])
        session = db_apis.get_session()
        with session.begin():
            db_lb = self.loadbalancer_repo.get(session, id=loadbalancer[constants.LOADBALANCER_ID])
        vip, additional_vips = self.network_driver.allocate_vip(db_lb)
        LOG.info('Allocated vip with port id %s, subnet id %s, ip address %s for load balancer %s', loadbalancer[constants.VIP_PORT_ID], loadbalancer[constants.VIP_SUBNET_ID], loadbalancer[constants.VIP_ADDRESS], loadbalancer[constants.LOADBALANCER_ID])
        for add_vip in additional_vips:
            LOG.debug('Allocated an additional VIP: subnet=%(subnet)s ip_address=%(ip)s', {'subnet': add_vip.subnet_id, 'ip': add_vip.ip_address})
        return (vip.to_dict(), [additional_vip.to_dict() for additional_vip in additional_vips])
<|end_body_0|>

<|body_start_1|>
        if isinstance(result, failure.Failure):
            LOG.exception('Unable to allocate VIP')
            return
        vip, additional_vips = result
        vip = data_models.Vip(**vip)
        LOG.warning('Deallocating vip %s', vip.ip_address)
        try:
            self.network_driver.deallocate_vip(vip)
        except Exception as e:
            LOG.error('Failed to deallocate VIP.  Resources may still be in use from vip: %(vip)s due to error: %(except)s', {'vip': vip.ip_address, 'except': str(e)})
<|end_body_1|>

Solution:
class AllocateVIP:
    """Task to allocate a VIP."""

    def execute(self, loadbalancer):
        """Allocate a vip to the loadbalancer."""
        LOG.debug('Allocating vip with port id %s, subnet id %s, ip address %s for load balancer %s', loadbalancer[constants.VIP_PORT_ID], loadbalancer[constants.VIP_SUBNET_ID], loadbalancer[constants.VIP_ADDRESS], loadbalancer[constants.LOADBALANCER_ID])
        session = db_apis.get_session()
        with session.begin():
            db_lb = self.loadbalancer_repo.get(session, id=loadbalancer[constants.LOADBALANCER_ID])
        vip, additional_vips = self.network_driver.allocate_vip(db_lb)
        LOG.info('Allocated vip with port id %s, subnet id %s, ip address %s for load balancer %s', loadbalancer[constants.VIP_PORT_ID], loadbalancer[constants.VIP_SUBNET_ID], loadbalancer[constants.VIP_ADDRESS], loadbalancer[constants.LOADBALANCER_ID])
        for add_vip in additional_vips:
            LOG.debug('Allocated an additional VIP: subnet=%(subnet)s ip_address=%(ip)s', {'subnet': add_vip.subnet_id, 'ip': add_vip.ip_address})
        return (vip.to_dict(), [additional_vip.to_dict() for additional_vip in additional_vips])

    def revert(self, result, loadbalancer, *args, **kwargs):
        """Handle a failure to allocate vip."""
        if isinstance(result, failure.Failure):
            LOG.exception('Unable to allocate VIP')
            return
        vip, additional_vips = result
        vip = data_models.Vip(**vip)
        LOG.warning('Deallocating vip %s', vip.ip_address)
        try:
            self.network_driver.deallocate_vip(vip)
        except Exception as e:
            LOG.error('Failed to deallocate VIP.  Resources may still be in use from vip: %(vip)s due to error: %(except)s', {'vip': vip.ip_address, 'except': str(e)})


dottest

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

Class description:
A linear interpolation operator

Method signatures and docstrings:
- def __init__(self, nm, om, dm, crd): lint constructor Parameters: nm - size of model (regularly sampled) om - origin of model axis dm - sampling of model axis nd - size of data (irregularly sampled) crd - input coordinates of the values (of the irregularly sampled data)
- def forward(self, add, mod, dat): Applies the forward linear interpolation operator Parameters: add - whether to add to the input (True/False) mod - input model numpy array dat - input data numpy array
- def adjoint(self, add, mod, dat): Applies the adjoint transient convolution operator Parameters add - whether to add to the input (True/False) mod - output model numpy array dat - input data numpy array
- def dottest(self, add=False): Performs the dot product test of the operator

Implement the Python class `lint` described below.

Class description:
A linear interpolation operator

Method signatures and docstrings:
- def __init__(self, nm, om, dm, crd): lint constructor Parameters: nm - size of model (regularly sampled) om - origin of model axis dm - sampling of model axis nd - size of data (irregularly sampled) crd - input coordinates of the values (of the irregularly sampled data)
- def forward(self, add, mod, dat): Applies the forward linear interpolation operator Parameters: add - whether to add to the input (True/False) mod - input model numpy array dat - input data numpy array
- def adjoint(self, add, mod, dat): Applies the adjoint transient convolution operator Parameters add - whether to add to the input (True/False) mod - output model numpy array dat - input data numpy array
- def dottest(self, add=False): Performs the dot product test of the operator

<|skeleton|>
class lint:
    """A linear interpolation operator"""

    def __init__(self, nm, om, dm, crd):
        """lint constructor Parameters: nm - size of model (regularly sampled) om - origin of model axis dm - sampling of model axis nd - size of data (irregularly sampled) crd - input coordinates of the values (of the irregularly sampled data)"""
        <|body_0|>

    def forward(self, add, mod, dat):
        """Applies the forward linear interpolation operator Parameters: add - whether to add to the input (True/False) mod - input model numpy array dat - input data numpy array"""
        <|body_1|>

    def adjoint(self, add, mod, dat):
        """Applies the adjoint transient convolution operator Parameters add - whether to add to the input (True/False) mod - output model numpy array dat - input data numpy array"""
        <|body_2|>

    def dottest(self, add=False):
        """Performs the dot product test of the operator"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.__nm = nm
        self.__om = om
        self.__dm = dm
        self.__nd = len(crd)
        self.__crd = crd
        if self.__crd.shape[0] != self.__nd:
            raise Exception('coordinate array must be same length as data')
<|end_body_0|>

<|body_start_1|>
        if mod.shape[0] != self.__nm or dat.shape[0] != self.__nd:
            raise Exception('lint forward: input shapes do not match those passed to constructor')
        if add == False:
            dat[:] = 0.0
        forward_lint(self.__om, self.__dm, self.__nm, self.__nd, self.__crd, mod, dat)
<|end_body_1|>

<|body_start_2|>
        if mod.shape[0] != self.__nm or dat.shape[0] != self.__nd:
            raise Exception('lint adjoint: input shapes do not match those passed to constructor')
        if add == False:
            mod[:] = 0.0
        adjoint_lint(self.__om, self.__dm, self.__nm, self.__nd, self.__crd, mod, dat)
<|end_body_2|>

<|body_start_3|>
        m = np.random.rand(self.__nm).astype('float32')
        mh = np.zeros(self.__nm, dtype='float32')
        d = np.random.rand(self.__nd).astype('float32')
        dh = np.zeros(self.__nd, dtype='float32')
        if add:
            self.forward(True, m, dh)
            self.adjoint(True, mh, d)
            dotm = np.dot(m, mh)
            dotd = np.dot(d, dh)
            print('Dot product test (add==True):')
            print('Dotm = %f Dotd = %f' % (dotm, dotd))
            print('Absolute error = %f' % abs(dotm - dotd))
            print('Relative error = %f' % (abs(dotm - dotd) / dotd))
        else:
            self.forward(False, m, dh)
            self.adjoint(False, mh, d)
            dotm = np.dot(m, mh)
            dotd = np.dot(d, dh)
            print('Dot product test (add==False):')
            print('Dotm = %f Dotd = %f' % (dotm, dotd))
            print('Absolute error = %f' % abs(dotm - dotd))
            print('Relative error = %f' % (abs(dotm - dotd) / dotd))
<|end_body_3|>

Solution:
class lint:
    """A linear interpolation operator"""

    def __init__(self, nm, om, dm, crd):
        """lint constructor Parameters: nm - size of model (regularly sampled) om - origin of model axis dm - sampling of model axis nd - size of data (irregularly sampled) crd - input coordinates of the values (of the irregularly sampled data)"""
        self.__nm = nm
        self.__om = om
        self.__dm = dm
        self.__nd = len(crd)
        self.__crd = crd
        if self.__crd.shape[0] != self.__nd:
            raise Exception('coordinate array must be same length as data')

    def forward(self, add, mod, dat):
        """Applies the forward linear interpolation operator Parameters: add - whether to add to the input (True/False) mod - input model numpy array dat - input data numpy array"""
        if mod.shape[0] != self.__nm or dat.shape[0] != self.__nd:
            raise Exception('lint forward: input shapes do not match those passed to constructor')
        if add == False:
            dat[:] = 0.0
        forward_lint(self.__om, self.__dm, self.__nm, self.__nd, self.__crd, mod, dat)

    def adjoint(self, add, mod, dat):
        """Applies the adjoint transient convolution operator Parameters add - whether to add to the input (True/False) mod - output model numpy array dat - input data numpy array"""
        if mod.shape[0] != self.__nm or dat.shape[0] != self.__nd:
            raise Exception('lint adjoint: input shapes do not match those passed to constructor')
        if add == False:
            mod[:] = 0.0
        adjoint_lint(self.__om, self.__dm, self.__nm, self.__nd, self.__crd, mod, dat)

    def dottest(self, add=False):
        """Performs the dot product test of the operator"""
        m = np.random.rand(self.__nm).astype('float32')
        mh = np.zeros(self.__nm, dtype='float32')
        d = np.random.rand(self.__nd).astype('float32')
        dh = np.zeros(self.__nd, dtype='float32')
        if add:
            self.forward(True, m, dh)
            self.adjoint(True, mh, d)
            dotm = np.dot(m, mh)
            dotd = np.dot(d, dh)
            print('Dot product test (add==True):')
            print('Dotm = %f Dotd = %f' % (dotm, dotd))
            print('Absolute error = %f' % abs(dotm - dotd))
            print('Relative error = %f' % (abs(dotm - dotd) / dotd))
        else:
            self.forward(False, m, dh)
            self.adjoint(False, mh, d)
            dotm = np.dot(m, mh)
            dotd = np.dot(d, dh)
            print('Dot product test (add==False):')
            print('Dotm = %f Dotd = %f' % (dotm, dotd))
            print('Absolute error = %f' % abs(dotm - dotd))
            print('Relative error = %f' % (abs(dotm - dotd) / dotd))


fire

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

Class description:
Implement the DiscreteTransition class.

Method signatures and docstrings:
- def __init__(self, transition_id, label, firing_condition, consumption_speeds, production_speeds, delay): In addition to the arguments specified in the super class ContinuousTransition, a delay function must be specified for a discrete transition. Args: delay (int): number of time-steps after which transition is fired if firing_condition still holds true.
- def fire(self, time_step): Check if the firing condition is satisfied during the delay.

Implement the Python class `DiscreteTransition` described below.

Class description:
Implement the DiscreteTransition class.

Method signatures and docstrings:
- def __init__(self, transition_id, label, firing_condition, consumption_speeds, production_speeds, delay): In addition to the arguments specified in the super class ContinuousTransition, a delay function must be specified for a discrete transition. Args: delay (int): number of time-steps after which transition is fired if firing_condition still holds true.
- def fire(self, time_step): Check if the firing condition is satisfied during the delay.

<|skeleton|>
class DiscreteTransition:

    def __init__(self, transition_id, label, firing_condition, consumption_speeds, production_speeds, delay):
        """In addition to the arguments specified in the super class ContinuousTransition, a delay function must be specified for a discrete transition. Args: delay (int): number of time-steps after which transition is fired if firing_condition still holds true."""
        <|body_0|>

    def fire(self, time_step):
        """Check if the firing condition is satisfied during the delay."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(DiscreteTransition, self).__init__(transition_id, label, firing_condition, consumption_speeds, production_speeds)
        self.delay = delay
        self.delay_counter = 0
<|end_body_0|>

<|body_start_1|>
        input_place_tokens = self.get_input_place_tokens()
        if self.firing_condition(input_place_tokens) == True:
            if self.delay_counter == int(self.delay / time_step):
                super().fire(1)
                self.delay_counter = 0
            else:
                self.delay_counter += 1
        else:
            self.delay_counter = 0
<|end_body_1|>

Solution:
class DiscreteTransition:
    def __init__(self, transition_id, label, firing_condition, consumption_speeds, production_speeds, delay):
        """In addition to the arguments specified in the super class ContinuousTransition, a delay function must be specified for a discrete transition. Args: delay (int): number of time-steps after which transition is fired if firing_condition still holds true."""
        super(DiscreteTransition, self).__init__(transition_id, label, firing_condition, consumption_speeds, production_speeds)
        self.delay = delay
        self.delay_counter = 0

    def fire(self, time_step):
        """Check if the firing condition is satisfied during the delay."""
        input_place_tokens = self.get_input_place_tokens()
        if self.firing_condition(input_place_tokens) == True:
            if self.delay_counter == int(self.delay / time_step):
                super().fire(1)
                self.delay_counter = 0
            else:
                self.delay_counter += 1
        else:
            self.delay_counter = 0


原数组排序再比较，求出左右两边不满足要求的索引

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findUnsortedSubarray1(self, nums: List[int]) -> int: 原数组排序再比较，求出左右两边不满足要求的索引
- def findUnsortedSubarray2(self, nums: List[int]) -> int: 1.从数组两端遍历找破坏升序顺序的索引，从而确定需要无序的连续子数组 2.在无序子数组中找出最大值和最小值 3.再次从两端遍历，找出最大值和最小值正确的索引，两者之差即为最短无序子数组

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findUnsortedSubarray1(self, nums: List[int]) -> int: 原数组排序再比较，求出左右两边不满足要求的索引
- def findUnsortedSubarray2(self, nums: List[int]) -> int: 1.从数组两端遍历找破坏升序顺序的索引，从而确定需要无序的连续子数组 2.在无序子数组中找出最大值和最小值 3.再次从两端遍历，找出最大值和最小值正确的索引，两者之差即为最短无序子数组

<|skeleton|>
class Solution:

    def findUnsortedSubarray1(self, nums: List[int]) -> int:
        """原数组排序再比较，求出左右两边不满足要求的索引"""
        <|body_0|>

    def findUnsortedSubarray2(self, nums: List[int]) -> int:
        """1.从数组两端遍历找破坏升序顺序的索引，从而确定需要无序的连续子数组 2.在无序子数组中找出最大值和最小值 3.再次从两端遍历，找出最大值和最小值正确的索引，两者之差即为最短无序子数组"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        new = sorted(nums)
        l, r = (0, len(nums) - 1)
        while l < r and nums[l] == new[l]:
            l += 1
        while l < r and nums[r] == new[r]:
            r -= 1
        return r - l + 1 if r > l else 0
<|end_body_0|>

<|body_start_1|>
        l, r = (0, len(nums) - 1)
        while l < r and nums[l] <= nums[l + 1]:
            l += 1
        while l < r and nums[r] >= nums[r - 1]:
            r -= 1
        if l == r:
            return 0
        min_, max_ = (min(nums[l:r + 1]), max(nums[l:r + 1]))
        l, r = (0, len(nums) - 1)
        while l < r and min_ >= nums[l]:
            l += 1
        while l < r and max_ <= nums[r]:
            r -= 1
        return r - l + 1
<|end_body_1|>

Solution:
class Solution:
    def findUnsortedSubarray1(self, nums: List[int]) -> int:
        """原数组排序再比较，求出左右两边不满足要求的索引"""
        new = sorted(nums)
        l, r = (0, len(nums) - 1)
        while l < r and nums[l] == new[l]:
            l += 1
        while l < r and nums[r] == new[r]:
            r -= 1
        return r - l + 1 if r > l else 0

    def findUnsortedSubarray2(self, nums: List[int]) -> int:
        """1.从数组两端遍历找破坏升序顺序的索引，从而确定需要无序的连续子数组 2.在无序子数组中找出最大值和最小值 3.再次从两端遍历，找出最大值和最小值正确的索引，两者之差即为最短无序子数组"""
        l, r = (0, len(nums) - 1)
        while l < r and nums[l] <= nums[l + 1]:
            l += 1
        while l < r and nums[r] >= nums[r - 1]:
            r -= 1
        if l == r:
            return 0
        min_, max_ = (min(nums[l:r + 1]), max(nums[l:r + 1]))
        l, r = (0, len(nums) - 1)
        while l < r and min_ >= nums[l]:
            l += 1
        while l < r and max_ <= nums[r]:
            r -= 1
        return r - l + 1


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numFriendRequests_1(self, ages): :type ages: List[int] :rtype: int
- def numFriendRequests_2(self, ages): :type ages: List[int] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numFriendRequests_1(self, ages): :type ages: List[int] :rtype: int
- def numFriendRequests_2(self, ages): :type ages: List[int] :rtype: int

<|skeleton|>
class Solution:

    def numFriendRequests_1(self, ages):
        """:type ages: List[int] :rtype: int"""
        <|body_0|>

    def numFriendRequests_2(self, ages):
        """:type ages: List[int] :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        count = [0] * 121
        for age in ages:
            count[age] += 1
        ans = 0
        for ageA, countA in enumerate(count):
            for ageB, countB in enumerate(count):
                if ageA * 0.5 + 7 >= ageB:
                    continue
                if ageA < ageB:
                    continue
                if ageA < 100 < ageB:
                    continue
                ans += countA * countB
                if ageA == ageB:
                    ans -= countA
        return ans
<|end_body_0|>

<|body_start_1|>
        bucket = [0] * 121
        ct = collections.Counter()
        ans = 0
        for age in ages:
            ct[age] += 1
            a, b = (age / 2 + 8, age)
            for i in range(a, b):
                bucket[i] += 1
        for age in ages:
            ans += bucket[age]
        for k, v in ct.items():
            if v > 1 and k > 14:
                ans += v * (v - 1)
        return ans
<|end_body_1|>

Solution:
class Solution:
    def numFriendRequests_1(self, ages):
        """:type ages: List[int] :rtype: int"""
        count = [0] * 121
        for age in ages:
            count[age] += 1
        ans = 0
        for ageA, countA in enumerate(count):
            for ageB, countB in enumerate(count):
                if ageA * 0.5 + 7 >= ageB:
                    continue
                if ageA < ageB:
                    continue
                if ageA < 100 < ageB:
                    continue
                ans += countA * countB
                if ageA == ageB:
                    ans -= countA
        return ans

    def numFriendRequests_2(self, ages):
        """:type ages: List[int] :rtype: int"""
        bucket = [0] * 121
        ct = collections.Counter()
        ans = 0
        for age in ages:
            ct[age] += 1
            a, b = (age / 2 + 8, age)
            for i in range(a, b):
                bucket[i] += 1
        for age in ages:
            ans += bucket[age]
        for k, v in ct.items():
            if v > 1 and k > 14:
                ans += v * (v - 1)
        return ans


minCut

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCut(self, s: str) -> int: :给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。
- def minCut2(self, s: str) -> int: :给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCut(self, s: str) -> int: :给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。
- def minCut2(self, s: str) -> int: :给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。

<|skeleton|>
class Solution:

    def minCut(self, s: str) -> int:
        """:给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。"""
        <|body_0|>

    def minCut2(self, s: str) -> int:
        """:给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n = len(s)
        adjMap = defaultdict(set)
        for start in range(n):
            left, right = (start, start)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
            left, right = (start, start + 1)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
        queue = deque([0])
        visited = set([0])
        depth = 0
        while queue:
            curLen = len(queue)
            for _ in range(curLen):
                cur = queue.popleft()
                if cur == n:
                    return depth - 1
                for next in adjMap[cur]:
                    if next not in visited:
                        visited.add(next)
                        queue.append(next)
            depth += 1
        return n - 1
<|end_body_0|>

<|body_start_1|>
        n = len(s)
        adjMap = defaultdict(set)
        for start in range(n):
            left, right = (start, start)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
            left, right = (start, start + 1)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1

        @lru_cache(None)
        def dfs(index: int) -> int:
            if index >= n:
                return 0
            return min((1 + dfs(next) for next in adjMap[index]))
        return dfs(0) - 1
<|end_body_1|>

Solution:
class Solution:
    def minCut(self, s: str) -> int:
        """:给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。"""
        n = len(s)
        adjMap = defaultdict(set)
        for start in range(n):
            left, right = (start, start)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
            left, right = (start, start + 1)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
        queue = deque([0])
        visited = set([0])
        depth = 0
        while queue:
            curLen = len(queue)
            for _ in range(curLen):
                cur = queue.popleft()
                if cur == n:
                    return depth - 1
                for next in adjMap[cur]:
                    if next not in visited:
                        visited.add(next)
                        queue.append(next)
            depth += 1
        return n - 1

    def minCut2(self, s: str) -> int:
        """:给你一个字符串 s，请你将 s 分割成一些子串，使每个子串都是回文。 返回符合要求的 最少分割次数 。"""
        n = len(s)
        adjMap = defaultdict(set)
        for start in range(n):
            left, right = (start, start)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1
            left, right = (start, start + 1)
            while left >= 0 and right < n and (s[left] == s[right]):
                adjMap[left].add(right + 1)
                left -= 1
                right += 1

        @lru_cache(None)
        def dfs(index: int) -> int:
            if index >= n:
                return 0
            return min((1 + dfs(next) for next in adjMap[index]))
        return dfs(0) - 1

11_动态规划/dp分类/区间dp/dfs/回文/分割回文串/132_分割回文串-最短路建图.py


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

Class description:
Implementation of the 'RemoteRestoreIndexingStatus' model. Specifies the status of an indexing task. Attributes: end_time_usecs (long|int): Specifies the end time of the time range that is being indexed. The indexing task is creating an index of the Job Runs that occurred between the startTimeUsecs and this endTimeUsecs. This field is recorded as a Unix epoch Timestamp (in microseconds). error (string): Specifies the error message if the indexing Job/task fails. indexing_task_end_time_usecs (long|int): Specifies when the indexing task completed. This time is recorded as a Unix epoch Timestamp (in microseconds). This field is not set if the indexing task is still in progress. indexing_task_st

Method signatures and docstrings:
- def __init__(self, end_time_usecs=None, error=None, indexing_task_end_time_usecs=None, indexing_task_start_time_usecs=None, indexing_task_status=None, indexing_task_uid=None, latest_expiry_time_usecs=None, progress_monitor_task=None, start_time_usecs=None): Constructor for the RemoteRestoreIndexingStatus class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `RemoteRestoreIndexingStatus` described below.

Class description:
Implementation of the 'RemoteRestoreIndexingStatus' model. Specifies the status of an indexing task. Attributes: end_time_usecs (long|int): Specifies the end time of the time range that is being indexed. The indexing task is creating an index of the Job Runs that occurred between the startTimeUsecs and this endTimeUsecs. This field is recorded as a Unix epoch Timestamp (in microseconds). error (string): Specifies the error message if the indexing Job/task fails. indexing_task_end_time_usecs (long|int): Specifies when the indexing task completed. This time is recorded as a Unix epoch Timestamp (in microseconds). This field is not set if the indexing task is still in progress. indexing_task_st

Method signatures and docstrings:
- def __init__(self, end_time_usecs=None, error=None, indexing_task_end_time_usecs=None, indexing_task_start_time_usecs=None, indexing_task_status=None, indexing_task_uid=None, latest_expiry_time_usecs=None, progress_monitor_task=None, start_time_usecs=None): Constructor for the RemoteRestoreIndexingStatus class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class RemoteRestoreIndexingStatus:
    """Implementation of the 'RemoteRestoreIndexingStatus' model. Specifies the status of an indexing task. Attributes: end_time_usecs (long|int): Specifies the end time of the time range that is being indexed. The indexing task is creating an index of the Job Runs that occurred between the startTimeUsecs and this endTimeUsecs. This field is recorded as a Unix epoch Timestamp (in microseconds). error (string): Specifies the error message if the indexing Job/task fails. indexing_task_end_time_usecs (long|int): Specifies when the indexing task completed. This time is recorded as a Unix epoch Timestamp (in microseconds). This field is not set if the indexing task is still in progress. indexing_task_st"""

    def __init__(self, end_time_usecs=None, error=None, indexing_task_end_time_usecs=None, indexing_task_start_time_usecs=None, indexing_task_status=None, indexing_task_uid=None, latest_expiry_time_usecs=None, progress_monitor_task=None, start_time_usecs=None):
        """Constructor for the RemoteRestoreIndexingStatus class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.end_time_usecs = end_time_usecs
        self.error = error
        self.indexing_task_end_time_usecs = indexing_task_end_time_usecs
        self.indexing_task_start_time_usecs = indexing_task_start_time_usecs
        self.indexing_task_status = indexing_task_status
        self.indexing_task_uid = indexing_task_uid
        self.latest_expiry_time_usecs = latest_expiry_time_usecs
        self.progress_monitor_task = progress_monitor_task
        self.start_time_usecs = start_time_usecs
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        end_time_usecs = dictionary.get('endTimeUsecs')
        error = dictionary.get('error')
        indexing_task_end_time_usecs = dictionary.get('indexingTaskEndTimeUsecs')
        indexing_task_start_time_usecs = dictionary.get('indexingTaskStartTimeUsecs')
        indexing_task_status = dictionary.get('indexingTaskStatus')
        indexing_task_uid = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionary(dictionary.get('indexingTaskUid')) if dictionary.get('indexingTaskUid') else None
        latest_expiry_time_usecs = dictionary.get('latestExpiryTimeUsecs')
        progress_monitor_task = dictionary.get('progressMonitorTask')
        start_time_usecs = dictionary.get('startTimeUsecs')
        return cls(end_time_usecs, error, indexing_task_end_time_usecs, indexing_task_start_time_usecs, indexing_task_status, indexing_task_uid, latest_expiry_time_usecs, progress_monitor_task, start_time_usecs)
<|end_body_1|>

Solution:
class RemoteRestoreIndexingStatus:
    """Implementation of the 'RemoteRestoreIndexingStatus' model. Specifies the status of an indexing task. Attributes: end_time_usecs (long|int): Specifies the end time of the time range that is being indexed. The indexing task is creating an index of the Job Runs that occurred between the startTimeUsecs and this endTimeUsecs. This field is recorded as a Unix epoch Timestamp (in microseconds). error (string): Specifies the error message if the indexing Job/task fails. indexing_task_end_time_usecs (long|int): Specifies when the indexing task completed. This time is recorded as a Unix epoch Timestamp (in microseconds). This field is not set if the indexing task is still in progress. indexing_task_st"""

    def __init__(self, end_time_usecs=None, error=None, indexing_task_end_time_usecs=None, indexing_task_start_time_usecs=None, indexing_task_status=None, indexing_task_uid=None, latest_expiry_time_usecs=None, progress_monitor_task=None, start_time_usecs=None):
        """Constructor for the RemoteRestoreIndexingStatus class"""
        self.end_time_usecs = end_time_usecs
        self.error = error
        self.indexing_task_end_time_usecs = indexing_task_end_time_usecs
        self.indexing_task_start_time_usecs = indexing_task_start_time_usecs
        self.indexing_task_status = indexing_task_status
        self.indexing_task_uid = indexing_task_uid
        self.latest_expiry_time_usecs = latest_expiry_time_usecs
        self.progress_monitor_task = progress_monitor_task
        self.start_time_usecs = start_time_usecs

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        end_time_usecs = dictionary.get('endTimeUsecs')
        error = dictionary.get('error')
        indexing_task_end_time_usecs = dictionary.get('indexingTaskEndTimeUsecs')
        indexing_task_start_time_usecs = dictionary.get('indexingTaskStartTimeUsecs')
        indexing_task_status = dictionary.get('indexingTaskStatus')
        indexing_task_uid = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionary(dictionary.get('indexingTaskUid')) if dictionary.get('indexingTaskUid') else None
        latest_expiry_time_usecs = dictionary.get('latestExpiryTimeUsecs')
        progress_monitor_task = dictionary.get('progressMonitorTask')
        start_time_usecs = dictionary.get('startTimeUsecs')
        return cls(end_time_usecs, error, indexing_task_end_time_usecs, indexing_task_start_time_usecs, indexing_task_status, indexing_task_uid, latest_expiry_time_usecs, progress_monitor_task, start_time_usecs)


获取单品价格和属性 :return:

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

Class description:
Implement the goods class.

Method signatures and docstrings:
- def get_goods(self): 获取单品 :return: gid
- def get_price(self): 获取单品价格和属性 :return:
- def get_comment(self): 获取评论列表 gid:产品id size:记录条数 page:页码 :return:

Implement the Python class `goods` described below.

Class description:
Implement the goods class.

Method signatures and docstrings:
- def get_goods(self): 获取单品 :return: gid
- def get_price(self): 获取单品价格和属性 :return:
- def get_comment(self): 获取评论列表 gid:产品id size:记录条数 page:页码 :return:

<|skeleton|>
class goods:

    def get_goods(self):
        """获取单品 :return: gid"""
        <|body_0|>

    def get_price(self):
        """获取单品价格和属性 :return:"""
        <|body_1|>

    def get_comment(self):
        """获取评论列表 gid:产品id size:记录条数 page:页码 :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        url = host + '/api/goods/find'
        data = {'sku': sku, 'gid': goods_id}
        r = requests.post(url=url, data=data).json()
        out_format('获取单品:', r)
<|end_body_0|>

<|body_start_1|>
        url = host + '/api/goods/line'
        data = {'sku': sku, 'gid': goods_id}
        r = requests.post(url=url, data=data).json()
        out_format('获取单品价格和属性:', r)
        return r
<|end_body_1|>

<|body_start_2|>
        url = host + '/api/goods/comment'
        data = {'gid': goods_id, 'size': '10', 'page': '1'}
        r = requests.post(url=url, json=data).json()
        out_format('获取评论列表:', r)
        return r
<|end_body_2|>

Solution:
class goods:
    def get_goods(self):
        """获取单品 :return: gid"""
        url = host + '/api/goods/find'
        data = {'sku': sku, 'gid': goods_id}
        r = requests.post(url=url, data=data).json()
        out_format('获取单品:', r)

    def get_price(self):
        """获取单品价格和属性 :return:"""
        url = host + '/api/goods/line'
        data = {'sku': sku, 'gid': goods_id}
        r = requests.post(url=url, data=data).json()
        out_format('获取单品价格和属性:', r)
        return r

    def get_comment(self):
        """获取评论列表 gid:产品id size:记录条数 page:页码 :return:"""
        url = host + '/api/goods/comment'
        data = {'gid': goods_id, 'size': '10', 'page': '1'}
        r = requests.post(url=url, json=data).json()
        out_format('获取评论列表:', r)
        return r


search

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def binary_search(self, nums, target, low, high): Standard binary search in an array with distinct elements :param nums: :param target: :param low: :param high: :return:
- def find_pivot(self, nums, low, high): find the index of the pivot element in the array; :param nums: :param low: :param high: :return: for [3, 4, 5, 6, 1, 2]. return 3 (the index of 6) if it is fully sorted, return the last element
- def search2(self, nums: List[int], target: int) -> int: Binary search. When there are duplicates, the sorted half cannot be identified when nums[0] == nums[mid]. Therefore, the left pointer can only be moved to the right by 1 instead of moving to mid

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def binary_search(self, nums, target, low, high): Standard binary search in an array with distinct elements :param nums: :param target: :param low: :param high: :return:
- def find_pivot(self, nums, low, high): find the index of the pivot element in the array; :param nums: :param low: :param high: :return: for [3, 4, 5, 6, 1, 2]. return 3 (the index of 6) if it is fully sorted, return the last element
- def search2(self, nums: List[int], target: int) -> int: Binary search. When there are duplicates, the sorted half cannot be identified when nums[0] == nums[mid]. Therefore, the left pointer can only be moved to the right by 1 instead of moving to mid

<|skeleton|>
class Solution:

    def search(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: int"""
        <|body_0|>

    def binary_search(self, nums, target, low, high):
        """Standard binary search in an array with distinct elements :param nums: :param target: :param low: :param high: :return:"""
        <|body_1|>

    def find_pivot(self, nums, low, high):
        """find the index of the pivot element in the array; :param nums: :param low: :param high: :return: for [3, 4, 5, 6, 1, 2]. return 3 (the index of 6) if it is fully sorted, return the last element"""
        <|body_2|>

    def search2(self, nums: List[int], target: int) -> int:
        """Binary search. When there are duplicates, the sorted half cannot be identified when nums[0] == nums[mid]. Therefore, the left pointer can only be moved to the right by 1 instead of moving to mid"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        res = False
        if not nums:
            return False
        pivot = self.find_pivot(nums, 0, len(nums) - 1)
        if pivot == -1 or pivot == len(nums) - 1:
            res = self.binary_search(nums, target, 0, len(nums) - 1)
        elif nums[pivot] == target:
            res = pivot
        elif nums[0] <= target:
            res = self.binary_search(nums, target, 0, pivot)
        else:
            res = self.binary_search(nums, target, pivot + 1, len(nums) - 1)
        return res >= 0
<|end_body_0|>

<|body_start_1|>
        while low <= high:
            mid = int((low + high) / 2)
            if nums[mid] == target:
                return mid
            elif nums[mid] > target:
                high = mid - 1
            else:
                low = mid + 1
        return -1
<|end_body_1|>

<|body_start_2|>
        if low > high:
            return -1
        if low == high:
            return low
        mid = int((low + high) / 2)
        if mid < high and nums[mid] > nums[mid + 1]:
            return mid
        elif mid > low and nums[mid] < nums[mid - 1]:
            return mid - 1
        elif nums[mid] < nums[low]:
            return self.find_pivot(nums, low, mid - 1)
        elif nums[mid] == nums[low]:
            return self.find_pivot(nums, low + 1, high)
        else:
            return self.find_pivot(nums, mid + 1, high)
<|end_body_2|>

<|body_start_3|>
        if not nums:
            return False
        l, r = (0, len(nums) - 1)
        while l <= r:
            mid = l + (r - l) // 2
            if nums[mid] == target:
                return True
            if nums[l] < nums[mid]:
                if nums[l] <= target < nums[mid]:
                    r = mid - 1
                else:
                    l = mid + 1
            elif nums[l] > nums[mid]:
                if nums[mid] < target <= nums[r]:
                    l = mid + 1
                else:
                    r = mid - 1
            else:
                l += 1
        return False
<|end_body_3|>

Solution:
class Solution:
    def search(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: int"""
        res = False
        if not nums:
            return False
        pivot = self.find_pivot(nums, 0, len(nums) - 1)
        if pivot == -1 or pivot == len(nums) - 1:
            res = self.binary_search(nums, target, 0, len(nums) - 1)
        elif nums[pivot] == target:
            res = pivot
        elif nums[0] <= target:
            res = self.binary_search(nums, target, 0, pivot)
        else:
            res = self.binary_search(nums, target, pivot + 1, len(nums) - 1)
        return res >= 0

    def binary_search(self, nums, target, low, high):
        """Standard binary search in an array with distinct elements :param nums: :param target: :param low: :param high: :return:"""
        while low <= high:
            mid = int((low + high) / 2)
            if nums[mid] == target:
                return mid
            elif nums[mid] > target:
                high = mid - 1
            else:
                low = mid + 1
        return -1

    def find_pivot(self, nums, low, high):
        """find the index of the pivot element in the array; :param nums: :param low: :param high: :return: for [3, 4, 5, 6, 1, 2]. return 3 (the index of 6) if it is fully sorted, return the last element"""
        if low > high:
            return -1
        if low == high:
            return low
        mid = int((low + high) / 2)
        if mid < high and nums[mid] > nums[mid + 1]:
            return mid
        elif mid > low and nums[mid] < nums[mid - 1]:
            return mid - 1
        elif nums[mid] < nums[low]:
            return self.find_pivot(nums, low, mid - 1)
        elif nums[mid] == nums[low]:
            return self.find_pivot(nums, low + 1, high)
        else:
            return self.find_pivot(nums, mid + 1, high)

    def search2(self, nums: List[int], target: int) -> int:
        """Binary search. When there are duplicates, the sorted half cannot be identified when nums[0] == nums[mid]. Therefore, the left pointer can only be moved to the right by 1 instead of moving to mid"""
        if not nums:
            return False
        l, r = (0, len(nums) - 1)
        while l <= r:
            mid = l + (r - l) // 2
            if nums[mid] == target:
                return True
            if nums[l] < nums[mid]:
                if nums[l] <= target < nums[mid]:
                    r = mid - 1
                else:
                    l = mid + 1
            elif nums[l] > nums[mid]:
                if nums[mid] < target <= nums[r]:
                    l = mid + 1
                else:
                    r = mid - 1
            else:
                l += 1
        return False


largestNumberMath

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def largestNumber(self, nums: List[int]) -> str: 比较
- def largestNumberMath(self, nums: List[int]) -> str: 数学

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def largestNumber(self, nums: List[int]) -> str: 比较
- def largestNumberMath(self, nums: List[int]) -> str: 数学

<|skeleton|>
class Solution:

    def largestNumber(self, nums: List[int]) -> str:
        """比较"""
        <|body_0|>

    def largestNumberMath(self, nums: List[int]) -> str:
        """数学"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        def compare(x, y):
            """ 比较函数，从大到小排序 """
            if y + x > x + y:
                return 1
            return -1
        nums = sorted(map(str, nums), key=cmp_to_key(compare))
        if nums[0] == '0':
            return '0'
        return ''.join(nums)
<|end_body_0|>

<|body_start_1|>
        def func(x):
            if not x:
                return 0
            n = int(math.log10(x)) + 1
            return x / (10 ** n - 1)
        nums.sort(key=func, reverse=True)
        if not nums[0]:
            return '0'
        return ''.join(map(str, nums))
<|end_body_1|>

Solution:
class Solution:
    def largestNumber(self, nums: List[int]) -> str:
        """比较"""
        def compare(x, y):
            """ 比较函数，从大到小排序 """
            if y + x > x + y:
                return 1
            return -1
        nums = sorted(map(str, nums), key=cmp_to_key(compare))
        if nums[0] == '0':
            return '0'
        return ''.join(nums)

    def largestNumberMath(self, nums: List[int]) -> str:
        """数学"""
        def func(x):
            if not x:
                return 0
            n = int(math.log10(x)) + 1
            return x / (10 ** n - 1)
        nums.sort(key=func, reverse=True)
        if not nums[0]:
            return '0'
        return ''.join(map(str, nums))


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

Class description:
Implement the CommentModelTests class.

Method signatures and docstrings:
- def test_create_comment_missing_kwarg_raises_type_error(self): Not passing a required kwarg like body should raise type error
- def test_create_comment_either_parent_or_post_must_be_kwarg(self): If neither 'parent' nor 'post' are passed as kwarg keys, a TypeError should be raised
- def test_create_comment_parent_is_none_when_post_is_kwarg(self): A comment's 'parent' field should be none when 'post' is passed as a kwarg
- def test_post_matches_parent_when_parent_is_comment(self): A comment's 'post'field should match its parent it when has a non-None parent
- def test_post_is_not_none_when_parent_is_kwarg(self): A comment's post field should not be none when parent is passed in as a a kwarg to create()

Implement the Python class `CommentModelTests` described below.

Class description:
Implement the CommentModelTests class.

Method signatures and docstrings:
- def test_create_comment_missing_kwarg_raises_type_error(self): Not passing a required kwarg like body should raise type error
- def test_create_comment_either_parent_or_post_must_be_kwarg(self): If neither 'parent' nor 'post' are passed as kwarg keys, a TypeError should be raised
- def test_create_comment_parent_is_none_when_post_is_kwarg(self): A comment's 'parent' field should be none when 'post' is passed as a kwarg
- def test_post_matches_parent_when_parent_is_comment(self): A comment's 'post'field should match its parent it when has a non-None parent
- def test_post_is_not_none_when_parent_is_kwarg(self): A comment's post field should not be none when parent is passed in as a a kwarg to create()

<|skeleton|>
class CommentModelTests:

    def test_create_comment_missing_kwarg_raises_type_error(self):
        """Not passing a required kwarg like body should raise type error"""
        <|body_0|>

    def test_create_comment_either_parent_or_post_must_be_kwarg(self):
        """If neither 'parent' nor 'post' are passed as kwarg keys, a TypeError should be raised"""
        <|body_1|>

    def test_create_comment_parent_is_none_when_post_is_kwarg(self):
        """A comment's 'parent' field should be none when 'post' is passed as a kwarg"""
        <|body_2|>

    def test_post_matches_parent_when_parent_is_comment(self):
        """A comment's 'post'field should match its parent it when has a non-None parent"""
        <|body_3|>

    def test_post_is_not_none_when_parent_is_kwarg(self):
        """A comment's post field should not be none when parent is passed in as a a kwarg to create()"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        post = create_a_post()
        with self.assertRaises(TypeError):
            comment = Comment.create(post=post)
<|end_body_0|>

<|body_start_1|>
        post = create_a_post()
        with self.assertRaises(TypeError):
            Comment.create(body="This shouldn't work")
<|end_body_1|>

<|body_start_2|>
        post = create_a_post()
        comment = Comment.create(body="I'm a comment without a parent", post=post)
        self.assertIs(comment.parent, None)
<|end_body_2|>

<|body_start_3|>
        post = create_a_post()
        parent = Comment.create(body="I'm a parent comment", post=post)
        comment = Comment.create(body="I'm a child comment", parent=parent)
        self.assertIs(comment.post, parent.post)
<|end_body_3|>

<|body_start_4|>
        post = create_a_post()
        parent = Comment.create(body="I'm a parent comment", post=post)
        comment = Comment.create(body="I'm a child comment", parent=parent)
        self.assertIsNotNone(comment.post)
<|end_body_4|>

Solution:
class CommentModelTests:
    def test_create_comment_missing_kwarg_raises_type_error(self):
        """Not passing a required kwarg like body should raise type error"""
        post = create_a_post()
        with self.assertRaises(TypeError):
            comment = Comment.create(post=post)

    def test_create_comment_either_parent_or_post_must_be_kwarg(self):
        """If neither 'parent' nor 'post' are passed as kwarg keys, a TypeError should be raised"""
        post = create_a_post()
        with self.assertRaises(TypeError):
            Comment.create(body="This shouldn't work")

    def test_create_comment_parent_is_none_when_post_is_kwarg(self):
        """A comment's 'parent' field should be none when 'post' is passed as a kwarg"""
        post = create_a_post()
        comment = Comment.create(body="I'm a comment without a parent", post=post)
        self.assertIs(comment.parent, None)

    def test_post_matches_parent_when_parent_is_comment(self):
        """A comment's 'post'field should match its parent it when has a non-None parent"""
        post = create_a_post()
        parent = Comment.create(body="I'm a parent comment", post=post)
        comment = Comment.create(body="I'm a child comment", parent=parent)
        self.assertIs(comment.post, parent.post)

    def test_post_is_not_none_when_parent_is_kwarg(self):
        """A comment's post field should not be none when parent is passed in as a a kwarg to create()"""
        post = create_a_post()
        parent = Comment.create(body="I'm a parent comment", post=post)
        comment = Comment.create(body="I'm a child comment", parent=parent)
        self.assertIsNotNone(comment.post)