pagination

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

Class description:
Implement the BlogMixin class.

Method signatures and docstrings:
- def get_archive(request, display_year=None, display_month=None): Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The year (integer or None) which should be opened by default when this archive is displayed :param display_month : The month (integer or None) which should be opened by default when this archive is displayed
- def get_tag_cloud(): Fetch the tag cloud data - categorise the Tags as upper, Average or lower Categorisation based on whether they are above or below average (mean) Maybe better to use MODE - i.e. the most frequent count. Using mode ensures that the largest number of labels are in the 'average' category
- def pagination(self, query_set, page, url_args): Split the query set into pages based on the instance paging attributes :param query_set : The fully ordered query set of all the posts filtered by Tag or date :param page : The page being requested :param url_args : The set of arguments required to create the url :return A tuple of the query_set, the url for the previous page, and a url for the next page

Implement the Python class `BlogMixin` described below.

Class description:
Implement the BlogMixin class.

Method signatures and docstrings:
- def get_archive(request, display_year=None, display_month=None): Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The year (integer or None) which should be opened by default when this archive is displayed :param display_month : The month (integer or None) which should be opened by default when this archive is displayed
- def get_tag_cloud(): Fetch the tag cloud data - categorise the Tags as upper, Average or lower Categorisation based on whether they are above or below average (mean) Maybe better to use MODE - i.e. the most frequent count. Using mode ensures that the largest number of labels are in the 'average' category
- def pagination(self, query_set, page, url_args): Split the query set into pages based on the instance paging attributes :param query_set : The fully ordered query set of all the posts filtered by Tag or date :param page : The page being requested :param url_args : The set of arguments required to create the url :return A tuple of the query_set, the url for the previous page, and a url for the next page

<|skeleton|>
class BlogMixin:

    def get_archive(request, display_year=None, display_month=None):
        """Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The year (integer or None) which should be opened by default when this archive is displayed :param display_month : The month (integer or None) which should be opened by default when this archive is displayed"""
        <|body_0|>

    def get_tag_cloud():
        """Fetch the tag cloud data - categorise the Tags as upper, Average or lower Categorisation based on whether they are above or below average (mean) Maybe better to use MODE - i.e. the most frequent count. Using mode ensures that the largest number of labels are in the 'average' category"""
        <|body_1|>

    def pagination(self, query_set, page, url_args):
        """Split the query set into pages based on the instance paging attributes :param query_set : The fully ordered query set of all the posts filtered by Tag or date :param page : The page being requested :param url_args : The set of arguments required to create the url :return A tuple of the query_set, the url for the previous page, and a url for the next page"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        archive = models.Entry.objects.filter(is_published=True).order_by('-pub_date')
        if not archive:
            return {'list': archive, 'display_year': None, 'display_month': None}
        if display_year is None and display_month is None:
            display_year, display_month = (archive[0].pub_date.year, archive[0].pub_date.month)
        elif display_year is not None and display_month is None:
            display_month = archive.filter(pub_date__year=display_year).order_by('-pub_date')[0].pub_date.month
        return {'list': archive, 'display_year': display_year, 'display_month': display_month}
<|end_body_0|>

<|body_start_1|>
        data = models.Tag.objects.annotate(num_entries=Count('entries')).filter(num_entries__gt=0).aggregate(mode=Mode('num_entries'), mean=Avg('num_entries'), std_dev=StdDev('num_entries'))
        mode, mean, std_dev = (data['mode'], data['mean'], data['std_dev'])
        if not mode and (not mean) and (not std_dev):
            return []
        return models.Tag.objects.annotate(num_entries=Count('entries')).filter(num_entries__gt=0).annotate(category=Case(When(num_entries__gt=mean + std_dev, then=Value('upper')), When(num_entries__lt=mean - std_dev, then=Value('lower')), default=Value('avg'), output_field=CharField()))
<|end_body_1|>

<|body_start_2|>
        if not query_set:
            return (query_set, None, None, 0)
        total = query_set.count()
        total_pages, orphans = divmod(total, self.max_per_page)
        start_elem = (page - 1) * self.max_per_page
        remaining = total - (page - 1) * self.max_per_page
        if remaining >= self.max_per_page + self.pagination_orphans:
            query_set, next_page, prev_page = (query_set[start_elem:start_elem + self.max_per_page], page + 1, page - 1 if page > 1 else None)
        else:
            query_set, next_page, prev_page = (query_set[start_elem:], None, page - 1 if page > 1 else None)
        return (query_set, prev_page, next_page, total_pages if orphans < self.pagination_orphans else total_pages + 1)
<|end_body_2|>

Solution:
class BlogMixin:
    def get_archive(request, display_year=None, display_month=None):
        """Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The year (integer or None) which should be opened by default when this archive is displayed :param display_month : The month (integer or None) which should be opened by default when this archive is displayed"""
        archive = models.Entry.objects.filter(is_published=True).order_by('-pub_date')
        if not archive:
            return {'list': archive, 'display_year': None, 'display_month': None}
        if display_year is None and display_month is None:
            display_year, display_month = (archive[0].pub_date.year, archive[0].pub_date.month)
        elif display_year is not None and display_month is None:
            display_month = archive.filter(pub_date__year=display_year).order_by('-pub_date')[0].pub_date.month
        return {'list': archive, 'display_year': display_year, 'display_month': display_month}

    def get_tag_cloud():
        """Fetch the tag cloud data - categorise the Tags as upper, Average or lower Categorisation based on whether they are above or below average (mean) Maybe better to use MODE - i.e. the most frequent count. Using mode ensures that the largest number of labels are in the 'average' category"""
        data = models.Tag.objects.annotate(num_entries=Count('entries')).filter(num_entries__gt=0).aggregate(mode=Mode('num_entries'), mean=Avg('num_entries'), std_dev=StdDev('num_entries'))
        mode, mean, std_dev = (data['mode'], data['mean'], data['std_dev'])
        if not mode and (not mean) and (not std_dev):
            return []
        return models.Tag.objects.annotate(num_entries=Count('entries')).filter(num_entries__gt=0).annotate(category=Case(When(num_entries__gt=mean + std_dev, then=Value('upper')), When(num_entries__lt=mean - std_dev, then=Value('lower')), default=Value('avg'), output_field=CharField()))

    def pagination(self, query_set, page, url_args):
        """Split the query set into pages based on the instance paging attributes :param query_set : The fully ordered query set of all the posts filtered by Tag or date :param page : The page being requested :param url_args : The set of arguments required to create the url :return A tuple of the query_set, the url for the previous page, and a url for the next page"""
        if not query_set:
            return (query_set, None, None, 0)
        total = query_set.count()
        total_pages, orphans = divmod(total, self.max_per_page)
        start_elem = (page - 1) * self.max_per_page
        remaining = total - (page - 1) * self.max_per_page
        if remaining >= self.max_per_page + self.pagination_orphans:
            query_set, next_page, prev_page = (query_set[start_elem:start_elem + self.max_per_page], page + 1, page - 1 if page > 1 else None)
        else:
            query_set, next_page, prev_page = (query_set[start_elem:], None, page - 1 if page > 1 else None)
        return (query_set, prev_page, next_page, total_pages if orphans < self.pagination_orphans else total_pages + 1)


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

Class description:
Implement the PanelFilePath class.

Method signatures and docstrings:
- def __init__(self, window_file_panel): constructor initializes a widget with a edit line and button to be put on WindowFilePanel. Keyword arguments: :param window_file_panel: an initialized instance (parent widget) of WindowFilePanel class
- def setup_file_path_ui(self): setup path file elements including go to parent button used only from constructor
- def setup_connections(self): setup path and go to parent button connections used only from constructor
- def goto_parent_clicked_connection(self): This connection visually goes to parent folder from current folder
- def update_file_path_connection(self): This connection visually goes to written path in the path field

Implement the Python class `PanelFilePath` described below.

Class description:
Implement the PanelFilePath class.

Method signatures and docstrings:
- def __init__(self, window_file_panel): constructor initializes a widget with a edit line and button to be put on WindowFilePanel. Keyword arguments: :param window_file_panel: an initialized instance (parent widget) of WindowFilePanel class
- def setup_file_path_ui(self): setup path file elements including go to parent button used only from constructor
- def setup_connections(self): setup path and go to parent button connections used only from constructor
- def goto_parent_clicked_connection(self): This connection visually goes to parent folder from current folder
- def update_file_path_connection(self): This connection visually goes to written path in the path field

<|skeleton|>
class PanelFilePath:

    def __init__(self, window_file_panel):
        """constructor initializes a widget with a edit line and button to be put on WindowFilePanel. Keyword arguments: :param window_file_panel: an initialized instance (parent widget) of WindowFilePanel class"""
        <|body_0|>

    def setup_file_path_ui(self):
        """setup path file elements including go to parent button used only from constructor"""
        <|body_1|>

    def setup_connections(self):
        """setup path and go to parent button connections used only from constructor"""
        <|body_2|>

    def goto_parent_clicked_connection(self):
        """This connection visually goes to parent folder from current folder"""
        <|body_3|>

    def update_file_path_connection(self):
        """This connection visually goes to written path in the path field"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        super(PanelFilePath, self).__init__(window_file_panel.tab_widget)
        self.window_file_panel = window_file_panel
        self.setup_file_path_ui()
        self.setup_connections()
<|end_body_0|>

<|body_start_1|>
        self.path_layout = QtGui.QHBoxLayout(self)
        self.path_layout.setMargin(0)
        self.path_layout.setSpacing(0)
        self.path_line_edit = PanelDoubleClickPath(self)
        self.path_line_edit.setReadOnly(True)
        self.path_layout.addWidget(self.path_line_edit)
        self.push_up_dir = QtGui.QPushButton(self)
        self.push_up_dir.setToolTip('Go up folder')
        self.push_up_dir.setIcon(QtGui.QIcon('resources/icon/cdtoparent.png'))
        self.push_up_dir.setIconSize(QtCore.QSize(24, 24))
        self.path_layout.addWidget(self.push_up_dir)
<|end_body_1|>

<|body_start_2|>
        self.push_up_dir.clicked.connect(self.goto_parent_clicked_connection)
        self.path_line_edit.returnPressed.connect(self.update_file_path_connection)
        self.connect(self.window_file_panel.tree_view, QtCore.SIGNAL('backspacePressed'), self.goto_parent_clicked_connection)
<|end_body_2|>

<|body_start_3|>
        if self.window_file_panel.current_folder_name != '':
            parent_index = self.window_file_panel.tree_view.model.index(self.window_file_panel.current_folder_path)
            folder_index = self.window_file_panel.tree_view.model.parent(parent_index)
            self.window_file_panel.goto_folder(folder_index)
<|end_body_3|>

<|body_start_4|>
        edit_path = str(self.path_line_edit.text())
        if os.path.exists(edit_path):
            edit_path.replace('/', '//')
            self.window_file_panel.goto_folder(self.window_file_panel.tree_view.model.index(edit_path))
            self.path_line_edit.setReadOnly(True)
<|end_body_4|>

Solution:
class PanelFilePath:
    def __init__(self, window_file_panel):
        """constructor initializes a widget with a edit line and button to be put on WindowFilePanel. Keyword arguments: :param window_file_panel: an initialized instance (parent widget) of WindowFilePanel class"""
        super(PanelFilePath, self).__init__(window_file_panel.tab_widget)
        self.window_file_panel = window_file_panel
        self.setup_file_path_ui()
        self.setup_connections()

    def setup_file_path_ui(self):
        """setup path file elements including go to parent button used only from constructor"""
        self.path_layout = QtGui.QHBoxLayout(self)
        self.path_layout.setMargin(0)
        self.path_layout.setSpacing(0)
        self.path_line_edit = PanelDoubleClickPath(self)
        self.path_line_edit.setReadOnly(True)
        self.path_layout.addWidget(self.path_line_edit)
        self.push_up_dir = QtGui.QPushButton(self)
        self.push_up_dir.setToolTip('Go up folder')
        self.push_up_dir.setIcon(QtGui.QIcon('resources/icon/cdtoparent.png'))
        self.push_up_dir.setIconSize(QtCore.QSize(24, 24))
        self.path_layout.addWidget(self.push_up_dir)

    def setup_connections(self):
        """setup path and go to parent button connections used only from constructor"""
        self.push_up_dir.clicked.connect(self.goto_parent_clicked_connection)
        self.path_line_edit.returnPressed.connect(self.update_file_path_connection)
        self.connect(self.window_file_panel.tree_view, QtCore.SIGNAL('backspacePressed'), self.goto_parent_clicked_connection)

    def goto_parent_clicked_connection(self):
        """This connection visually goes to parent folder from current folder"""
        if self.window_file_panel.current_folder_name != '':
            parent_index = self.window_file_panel.tree_view.model.index(self.window_file_panel.current_folder_path)
            folder_index = self.window_file_panel.tree_view.model.parent(parent_index)
            self.window_file_panel.goto_folder(folder_index)

    def update_file_path_connection(self):
        """This connection visually goes to written path in the path field"""
        edit_path = str(self.path_line_edit.text())
        if os.path.exists(edit_path):
            edit_path.replace('/', '//')
            self.window_file_panel.goto_folder(self.window_file_panel.tree_view.model.index(edit_path))
            self.path_line_edit.setReadOnly(True)


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

Class description:
Implement the ExchangeWF class.

Method signatures and docstrings:
- def __init__(self, magnetic_structures, energies, default_magmoms=None, db_file=DB_FILE, name='Exchange WF'): Workflow for computing exchange parameters. This workflow takes a set of magnetic orderings and their energies from MagneticOrderingsWF and fits to a classical Heisenberg Hamiltonian to compute exchange parameters. The critical temperature can then be calculated with Monte Carlo. Optionally, only the lowest energy FM and AFM configurations can be used to compute the average exchange interaction, <J>, without any static calculations. Args: magnetic_structures (list): Structure objects with the 'magmom' site property. energies (list): Energies per atom in eV. default_magmoms (dict): (optional, defaults provided) dict of magnetic elements to their initial magnetic moments in µB, generally these
- def get_wf(self, num_orderings_hard_limit=16, c=None): Retrieve Fireworks workflow. c is an optional dictionary that can contain: * heisenberg_settings: cutoff (float): Starting point for nearest neighbor search. tol (float): Tolerance for equivalent NN bonds. * mc_settings: mc_box_size (float): MC simulation box size in nm. equil_timesteps (int): Number of MC equilibration moves. mc_timesteps (int): Number of MC moves for averaging. avg (bool): Compute only <J>. * DB_FILE: path to db.json. Args: num_orderings_hard_limit (int): will make sure total number of magnetic orderings does not exceed this number even if there are extra orderings of equivalent symmetry c Optional[dict]: additional config dict described above Returns: wf (Workflow): Heise

Implement the Python class `ExchangeWF` described below.

Class description:
Implement the ExchangeWF class.

Method signatures and docstrings:
- def __init__(self, magnetic_structures, energies, default_magmoms=None, db_file=DB_FILE, name='Exchange WF'): Workflow for computing exchange parameters. This workflow takes a set of magnetic orderings and their energies from MagneticOrderingsWF and fits to a classical Heisenberg Hamiltonian to compute exchange parameters. The critical temperature can then be calculated with Monte Carlo. Optionally, only the lowest energy FM and AFM configurations can be used to compute the average exchange interaction, <J>, without any static calculations. Args: magnetic_structures (list): Structure objects with the 'magmom' site property. energies (list): Energies per atom in eV. default_magmoms (dict): (optional, defaults provided) dict of magnetic elements to their initial magnetic moments in µB, generally these
- def get_wf(self, num_orderings_hard_limit=16, c=None): Retrieve Fireworks workflow. c is an optional dictionary that can contain: * heisenberg_settings: cutoff (float): Starting point for nearest neighbor search. tol (float): Tolerance for equivalent NN bonds. * mc_settings: mc_box_size (float): MC simulation box size in nm. equil_timesteps (int): Number of MC equilibration moves. mc_timesteps (int): Number of MC moves for averaging. avg (bool): Compute only <J>. * DB_FILE: path to db.json. Args: num_orderings_hard_limit (int): will make sure total number of magnetic orderings does not exceed this number even if there are extra orderings of equivalent symmetry c Optional[dict]: additional config dict described above Returns: wf (Workflow): Heise

<|skeleton|>
class ExchangeWF:

    def __init__(self, magnetic_structures, energies, default_magmoms=None, db_file=DB_FILE, name='Exchange WF'):
        """Workflow for computing exchange parameters. This workflow takes a set of magnetic orderings and their energies from MagneticOrderingsWF and fits to a classical Heisenberg Hamiltonian to compute exchange parameters. The critical temperature can then be calculated with Monte Carlo. Optionally, only the lowest energy FM and AFM configurations can be used to compute the average exchange interaction, <J>, without any static calculations. Args: magnetic_structures (list): Structure objects with the 'magmom' site property. energies (list): Energies per atom in eV. default_magmoms (dict): (optional, defaults provided) dict of magnetic elements to their initial magnetic moments in µB, generally these"""
        <|body_0|>

    def get_wf(self, num_orderings_hard_limit=16, c=None):
        """Retrieve Fireworks workflow. c is an optional dictionary that can contain: * heisenberg_settings: cutoff (float): Starting point for nearest neighbor search. tol (float): Tolerance for equivalent NN bonds. * mc_settings: mc_box_size (float): MC simulation box size in nm. equil_timesteps (int): Number of MC equilibration moves. mc_timesteps (int): Number of MC moves for averaging. avg (bool): Compute only <J>. * DB_FILE: path to db.json. Args: num_orderings_hard_limit (int): will make sure total number of magnetic orderings does not exceed this number even if there are extra orderings of equivalent symmetry c Optional[dict]: additional config dict described above Returns: wf (Workflow): Heise"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.uuid = str(uuid4())
        self.wf_meta = {'wf_uuid': self.uuid, 'wf_name': self.__class__.__name__, 'wf_version': __version__}
        ordered_structures = [s for _, s in sorted(zip(energies, magnetic_structures), reverse=False)]
        ordered_energies = sorted(energies, reverse=False)
        self.structures = ordered_structures
        self.energies = ordered_energies
        for s in magnetic_structures:
            try:
                s.site_properties['magmom']
            except Exception:
                raise RuntimeError("All structures must have 'magmom' site                     property.")
<|end_body_0|>

<|body_start_1|>
        c = c or {'DB_FILE': DB_FILE}
        if 'DB_FILE' not in c:
            c['DB_FILE'] = DB_FILE
        heisenberg_settings = c.get('heisenberg_settings', {})
        fws = []
        heisenberg_model_fw = HeisenbergModelFW(wf_uuid=self.uuid, parent_structure=self.structures[0], db_file=c['DB_FILE'], heisenberg_settings=heisenberg_settings, parents=None, structures=self.structures, energies=self.energies)
        mc_settings = c.get('mc_settings', {})
        vampire_fw = VampireCallerFW(wf_uuid=self.uuid, parent_structure=self.structures[0], parents=[heisenberg_model_fw], db_file=c['DB_FILE'], mc_settings=mc_settings)
        fws = [heisenberg_model_fw, vampire_fw]
        wf = Workflow(fws)
        wf = add_additional_fields_to_taskdocs(wf, {'wf_meta': self.wf_meta})
        formula = self.structures[0].composition.reduced_formula
        wf.name = f'{formula} - Exchange'
        return wf
<|end_body_1|>

Solution:
class ExchangeWF:
    def __init__(self, magnetic_structures, energies, default_magmoms=None, db_file=DB_FILE, name='Exchange WF'):
        """Workflow for computing exchange parameters. This workflow takes a set of magnetic orderings and their energies from MagneticOrderingsWF and fits to a classical Heisenberg Hamiltonian to compute exchange parameters. The critical temperature can then be calculated with Monte Carlo. Optionally, only the lowest energy FM and AFM configurations can be used to compute the average exchange interaction, <J>, without any static calculations. Args: magnetic_structures (list): Structure objects with the 'magmom' site property. energies (list): Energies per atom in eV. default_magmoms (dict): (optional, defaults provided) dict of magnetic elements to their initial magnetic moments in µB, generally these"""
        self.uuid = str(uuid4())
        self.wf_meta = {'wf_uuid': self.uuid, 'wf_name': self.__class__.__name__, 'wf_version': __version__}
        ordered_structures = [s for _, s in sorted(zip(energies, magnetic_structures), reverse=False)]
        ordered_energies = sorted(energies, reverse=False)
        self.structures = ordered_structures
        self.energies = ordered_energies
        for s in magnetic_structures:
            try:
                s.site_properties['magmom']
            except Exception:
                raise RuntimeError("All structures must have 'magmom' site                     property.")

    def get_wf(self, num_orderings_hard_limit=16, c=None):
        """Retrieve Fireworks workflow. c is an optional dictionary that can contain: * heisenberg_settings: cutoff (float): Starting point for nearest neighbor search. tol (float): Tolerance for equivalent NN bonds. * mc_settings: mc_box_size (float): MC simulation box size in nm. equil_timesteps (int): Number of MC equilibration moves. mc_timesteps (int): Number of MC moves for averaging. avg (bool): Compute only <J>. * DB_FILE: path to db.json. Args: num_orderings_hard_limit (int): will make sure total number of magnetic orderings does not exceed this number even if there are extra orderings of equivalent symmetry c Optional[dict]: additional config dict described above Returns: wf (Workflow): Heise"""
        c = c or {'DB_FILE': DB_FILE}
        if 'DB_FILE' not in c:
            c['DB_FILE'] = DB_FILE
        heisenberg_settings = c.get('heisenberg_settings', {})
        fws = []
        heisenberg_model_fw = HeisenbergModelFW(wf_uuid=self.uuid, parent_structure=self.structures[0], db_file=c['DB_FILE'], heisenberg_settings=heisenberg_settings, parents=None, structures=self.structures, energies=self.energies)
        mc_settings = c.get('mc_settings', {})
        vampire_fw = VampireCallerFW(wf_uuid=self.uuid, parent_structure=self.structures[0], parents=[heisenberg_model_fw], db_file=c['DB_FILE'], mc_settings=mc_settings)
        fws = [heisenberg_model_fw, vampire_fw]
        wf = Workflow(fws)
        wf = add_additional_fields_to_taskdocs(wf, {'wf_meta': self.wf_meta})
        formula = self.structures[0].composition.reduced_formula
        wf.name = f'{formula} - Exchange'
        return wf


threeSumMultiUsingCombination

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def threeSumMulti(self, arr, target): :type arr: List[int] :type target: int :rtype: int
- def threeSumMultiTwoPointers(self, arr, target): :type arr: List[int] :type target: int :rtype: int
- def threeSumMultiUsingCombination(self, arr, target): :type arr: List[int] :type target: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def threeSumMulti(self, arr, target): :type arr: List[int] :type target: int :rtype: int
- def threeSumMultiTwoPointers(self, arr, target): :type arr: List[int] :type target: int :rtype: int
- def threeSumMultiUsingCombination(self, arr, target): :type arr: List[int] :type target: int :rtype: int

<|skeleton|>
class Solution:

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

    def threeSumMultiTwoPointers(self, arr, target):
        """:type arr: List[int] :type target: int :rtype: int"""
        <|body_1|>

    def threeSumMultiUsingCombination(self, arr, target):
        """:type arr: List[int] :type target: int :rtype: int"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        n = len(arr)
        ans = 0
        MOD = 10 ** 9 + 7
        m = {}
        for i in range(n - 1):
            for j in range(i + 1, n):
                if target - arr[j] - arr[i] in m:
                    ans += m[target - arr[j] - arr[i]]
            m[arr[i]] = m.setdefault(arr[i], 0) + 1
        return ans % MOD
<|end_body_0|>

<|body_start_1|>
        n = len(arr)
        arr.sort()
        ans = 0
        MOD = 10 ** 9 + 7
        for i in range(n - 2):
            j, k = (i + 1, n - 1)
            while j < k:
                if arr[j] + arr[k] < target - arr[i]:
                    j += 1
                elif arr[j] + arr[k] > target - arr[i]:
                    k -= 1
                else:
                    l, r = (1, 1)
                    while j + l < k and arr[j + l] == arr[j]:
                        l += 1
                    while k - r >= j + l and arr[k - r] == arr[k]:
                        r += 1
                    ans += (k - j + 1) * (k - j) // 2 if arr[k] == arr[j] else l * r
                    j += l
                    k -= r
        return ans % MOD
<|end_body_1|>

<|body_start_2|>
        n = len(arr)
        ans = 0
        MOD = 10 ** 9 + 7
        count = [0] * 101
        for a in arr:
            count[a] += 1
        for i in range(target + 1):
            for j in range(i, target + 1):
                k = target - i - j
                if k < 0 or k >= 101 or k < j:
                    continue
                if count[i] == 0 or count[j] == 0 or count[k] == 0:
                    continue
                if i == j and j == k:
                    ans += (count[i] - 2) * (count[i] - 1) * count[i] // 6
                elif i == j and j != k:
                    ans += (count[i] - 1) * count[i] // 2 * count[k]
                elif i != j and j == k:
                    ans += (count[j] - 1) * count[j] // 2 * count[i]
                elif i != j and j != k:
                    ans += count[i] * count[j] * count[k]
        return ans % MOD
<|end_body_2|>

Solution:
class Solution:
    def threeSumMulti(self, arr, target):
        """:type arr: List[int] :type target: int :rtype: int"""
        n = len(arr)
        ans = 0
        MOD = 10 ** 9 + 7
        m = {}
        for i in range(n - 1):
            for j in range(i + 1, n):
                if target - arr[j] - arr[i] in m:
                    ans += m[target - arr[j] - arr[i]]
            m[arr[i]] = m.setdefault(arr[i], 0) + 1
        return ans % MOD

    def threeSumMultiTwoPointers(self, arr, target):
        """:type arr: List[int] :type target: int :rtype: int"""
        n = len(arr)
        arr.sort()
        ans = 0
        MOD = 10 ** 9 + 7
        for i in range(n - 2):
            j, k = (i + 1, n - 1)
            while j < k:
                if arr[j] + arr[k] < target - arr[i]:
                    j += 1
                elif arr[j] + arr[k] > target - arr[i]:
                    k -= 1
                else:
                    l, r = (1, 1)
                    while j + l < k and arr[j + l] == arr[j]:
                        l += 1
                    while k - r >= j + l and arr[k - r] == arr[k]:
                        r += 1
                    ans += (k - j + 1) * (k - j) // 2 if arr[k] == arr[j] else l * r
                    j += l
                    k -= r
        return ans % MOD

    def threeSumMultiUsingCombination(self, arr, target):
        """:type arr: List[int] :type target: int :rtype: int"""
        n = len(arr)
        ans = 0
        MOD = 10 ** 9 + 7
        count = [0] * 101
        for a in arr:
            count[a] += 1
        for i in range(target + 1):
            for j in range(i, target + 1):
                k = target - i - j
                if k < 0 or k >= 101 or k < j:
                    continue
                if count[i] == 0 or count[j] == 0 or count[k] == 0:
                    continue
                if i == j and j == k:
                    ans += (count[i] - 2) * (count[i] - 1) * count[i] // 6
                elif i == j and j != k:
                    ans += (count[i] - 1) * count[i] // 2 * count[k]
                elif i != j and j == k:
                    ans += (count[j] - 1) * count[j] // 2 * count[i]
                elif i != j and j != k:
                    ans += count[i] * count[j] * count[k]
        return ans % MOD


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

Class description:
this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user

Method signatures and docstrings:
- def __init__(self, positions, num_trials): Initializes the position list and number of simulations
- def generate_outcome(self, pos_val): generates the outcome of betting a total of $1000, given a position value ie. if the position value is 1, it generates the outcome of making 1000 bets of $1
- def repeat_investment(self, pos_val): this function inputs a position value, and outputs a list of the return for each simulation (num_trials)

Implement the Python class `Investment` described below.

Class description:
this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user

Method signatures and docstrings:
- def __init__(self, positions, num_trials): Initializes the position list and number of simulations
- def generate_outcome(self, pos_val): generates the outcome of betting a total of $1000, given a position value ie. if the position value is 1, it generates the outcome of making 1000 bets of $1
- def repeat_investment(self, pos_val): this function inputs a position value, and outputs a list of the return for each simulation (num_trials)

<|skeleton|>
class Investment:
    """this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user"""

    def __init__(self, positions, num_trials):
        """Initializes the position list and number of simulations"""
        <|body_0|>

    def generate_outcome(self, pos_val):
        """generates the outcome of betting a total of $1000, given a position value ie. if the position value is 1, it generates the outcome of making 1000 bets of $1"""
        <|body_1|>

    def repeat_investment(self, pos_val):
        """this function inputs a position value, and outputs a list of the return for each simulation (num_trials)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.positions = positions
        self.position_vals = []
        self.num_trials = num_trials
        try:
            self.num_trials = int(num_trials)
        except:
            raise TypeError
        else:
            if self.num_trials <= 0:
                raise ValueError
            for i in self.positions:
                self.position_vals.append(int(i) / 1000)
<|end_body_0|>

<|body_start_1|>
        num_shares = 1000 / pos_val
        n = 0
        sum = 0
        while n < num_shares:
            'x represents a random number between 0 and 1, which simulates the \n            probability measure that the user wins the bet or loses the bet, based\n            on the value of x'
            x = np.random.random_sample()
            if x >= 0.51:
                sum = sum + pos_val * 2
            n = n + 1
        return sum
<|end_body_1|>

<|body_start_2|>
        position = 1000 / pos_val
        daily_ret = []
        n = 0
        while n < self.num_trials:
            daily_ret.append(self.generate_outcome(pos_val) / 1000.0 - 1)
            n = n + 1
        return daily_ret
<|end_body_2|>

Solution:
class Investment:
    """this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user"""

    def __init__(self, positions, num_trials):
        """Initializes the position list and number of simulations"""
        self.positions = positions
        self.position_vals = []
        self.num_trials = num_trials
        try:
            self.num_trials = int(num_trials)
        except:
            raise TypeError
        else:
            if self.num_trials <= 0:
                raise ValueError
            for i in self.positions:
                self.position_vals.append(int(i) / 1000)

    def generate_outcome(self, pos_val):
        """generates the outcome of betting a total of $1000, given a position value ie. if the position value is 1, it generates the outcome of making 1000 bets of $1"""
        num_shares = 1000 / pos_val
        n = 0
        sum = 0
        while n < num_shares:
            'x represents a random number between 0 and 1, which simulates the \n            probability measure that the user wins the bet or loses the bet, based\n            on the value of x'
            x = np.random.random_sample()
            if x >= 0.51:
                sum = sum + pos_val * 2
            n = n + 1
        return sum

    def repeat_investment(self, pos_val):
        """this function inputs a position value, and outputs a list of the return for each simulation (num_trials)"""
        position = 1000 / pos_val
        daily_ret = []
        n = 0
        while n < self.num_trials:
            daily_ret.append(self.generate_outcome(pos_val) / 1000.0 - 1)
            n = n + 1
        return daily_ret


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

Class description:
Implement the Ammo class.

Method signatures and docstrings:
- def ammo_get(self, ammo_id): Fetch ammo entrie **not ammo file** passing uniq identificator. Args: ammo_id: int Returns: dict: Ammo entrie respresentation. Throws: LunaportClientError
- def ammo_post(self, case, descr=None, files=None, data=None): Upload file to Lunaport service and get new REST resource representation in responce. User requests_toolbelt.MultipartEncoder for big files. Args: data: str, whole file. Returns: dict: New test entrie respresentation. Throws: LunaportClientError

Implement the Python class `Ammo` described below.

Class description:
Implement the Ammo class.

Method signatures and docstrings:
- def ammo_get(self, ammo_id): Fetch ammo entrie **not ammo file** passing uniq identificator. Args: ammo_id: int Returns: dict: Ammo entrie respresentation. Throws: LunaportClientError
- def ammo_post(self, case, descr=None, files=None, data=None): Upload file to Lunaport service and get new REST resource representation in responce. User requests_toolbelt.MultipartEncoder for big files. Args: data: str, whole file. Returns: dict: New test entrie respresentation. Throws: LunaportClientError

<|skeleton|>
class Ammo:

    def ammo_get(self, ammo_id):
        """Fetch ammo entrie **not ammo file** passing uniq identificator. Args: ammo_id: int Returns: dict: Ammo entrie respresentation. Throws: LunaportClientError"""
        <|body_0|>

    def ammo_post(self, case, descr=None, files=None, data=None):
        """Upload file to Lunaport service and get new REST resource representation in responce. User requests_toolbelt.MultipartEncoder for big files. Args: data: str, whole file. Returns: dict: New test entrie respresentation. Throws: LunaportClientError"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        resp = None
        url = '{}ammo/{}'.format(self.epoint, ammo_id)
        try:
            resp = requests.get(url, timeout=self.to)
            resp.raise_for_status()
            return resp.json()
        except Exception as e:
            panic(e=e, resp=resp)
<|end_body_0|>

<|body_start_1|>
        resp = None
        codes_allowed = [201]
        url = '{}ammo/'.format(self.epoint)
        kw = {'data': {}}
        if files:
            kw.update({'files': files})
        elif data:
            kw.update({'data': data})
        else:
            raise ValueError('Malformed kw, define one of: `files` or `data`')
        kw['data'].update({'case': case, 'descr': descr})
        try:
            resp = requests.post(url, **kw)
        except Exception as e:
            panic(e=e, resp=resp)
        if resp.status_code not in codes_allowed:
            panic(resp=resp)
        return resp.json()
<|end_body_1|>

Solution:
class Ammo:
    def ammo_get(self, ammo_id):
        """Fetch ammo entrie **not ammo file** passing uniq identificator. Args: ammo_id: int Returns: dict: Ammo entrie respresentation. Throws: LunaportClientError"""
        resp = None
        url = '{}ammo/{}'.format(self.epoint, ammo_id)
        try:
            resp = requests.get(url, timeout=self.to)
            resp.raise_for_status()
            return resp.json()
        except Exception as e:
            panic(e=e, resp=resp)

    def ammo_post(self, case, descr=None, files=None, data=None):
        """Upload file to Lunaport service and get new REST resource representation in responce. User requests_toolbelt.MultipartEncoder for big files. Args: data: str, whole file. Returns: dict: New test entrie respresentation. Throws: LunaportClientError"""
        resp = None
        codes_allowed = [201]
        url = '{}ammo/'.format(self.epoint)
        kw = {'data': {}}
        if files:
            kw.update({'files': files})
        elif data:
            kw.update({'data': data})
        else:
            raise ValueError('Malformed kw, define one of: `files` or `data`')
        kw['data'].update({'case': case, 'descr': descr})
        try:
            resp = requests.post(url, **kw)
        except Exception as e:
            panic(e=e, resp=resp)
        if resp.status_code not in codes_allowed:
            panic(resp=resp)
        return resp.json()


search

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

Class description:
The Front Multi-Objective Projected Gradient algorithm class The main functions are: - Initialize a FMOPG instance; - Execute the algorithm starting from a point of a given array; - Reset the stopping conditions current values.

Method signatures and docstrings:
- def __init__(self, theta_tol: float, gurobi: bool, gurobi_method: int, gurobi_verbose: bool, ALS_alpha_0: float, ALS_delta: float, ALS_beta: float, ALS_min_alpha: float, max_iter: int=None, max_time: float=None, max_f_evals: int=None): Initialize a FMOPG instance :param theta_tol: it indicates the tolerance after which a point is considered Pareto-stationary; it can be seen as the epsilon value for the epsilon-Pareto-stationarity; for more details, the user is referred to the article :param gurobi: if set to True, the Gurobi Optimizer is used to solve the search direction problem :param gurobi_method: it indicates the method used by the Gurobi Optimizer :param gurobi_verbose: if set to True, it enables the verbosity for the Gurobi optimizer :param ALS_alpha_0: it indicates the initial step size for the Armijo-Type Line Search :param ALS_delta: it indicates the coefficient for the step size contraction :param ALS_beta: it i
- def search(self, p_list: np.array, f_list: np.array, problem: Problem, index_initial_point: int=None, I: tuple=None): Execute the algorithm starting from a point of a given array :param p_list: problem solutions :param f_list: related points in the objectives space :param problem: the considered problem :param index_initial_point: the index of the problem solution to optimize :param I: the subset of objective functions indices to consider (see nsma.py) :return: the new arrays p_list and f_list; an array which contains for each processed point the optimal value of the search direction problem at that point (theta_array) Notes: The index of the point to optimize can change during the iterations. For the stopping conditions 'theta_tolerance', only the last value of theta, i.e., the one related to the last poin
- def reset_stopping_conditions_current_values(self, theta_tol: float): Reset the stopping conditions current values :param theta_tol: the new current value for the stopping condition 'theta_tolerance' Notes: The current values of the stopping conditions 'max_time' and 'max_f_evals' are changed by the memetic algorithm that employs FMOPG.

Implement the Python class `FMOPG` described below.

Class description:
The Front Multi-Objective Projected Gradient algorithm class The main functions are: - Initialize a FMOPG instance; - Execute the algorithm starting from a point of a given array; - Reset the stopping conditions current values.

Method signatures and docstrings:
- def __init__(self, theta_tol: float, gurobi: bool, gurobi_method: int, gurobi_verbose: bool, ALS_alpha_0: float, ALS_delta: float, ALS_beta: float, ALS_min_alpha: float, max_iter: int=None, max_time: float=None, max_f_evals: int=None): Initialize a FMOPG instance :param theta_tol: it indicates the tolerance after which a point is considered Pareto-stationary; it can be seen as the epsilon value for the epsilon-Pareto-stationarity; for more details, the user is referred to the article :param gurobi: if set to True, the Gurobi Optimizer is used to solve the search direction problem :param gurobi_method: it indicates the method used by the Gurobi Optimizer :param gurobi_verbose: if set to True, it enables the verbosity for the Gurobi optimizer :param ALS_alpha_0: it indicates the initial step size for the Armijo-Type Line Search :param ALS_delta: it indicates the coefficient for the step size contraction :param ALS_beta: it i
- def search(self, p_list: np.array, f_list: np.array, problem: Problem, index_initial_point: int=None, I: tuple=None): Execute the algorithm starting from a point of a given array :param p_list: problem solutions :param f_list: related points in the objectives space :param problem: the considered problem :param index_initial_point: the index of the problem solution to optimize :param I: the subset of objective functions indices to consider (see nsma.py) :return: the new arrays p_list and f_list; an array which contains for each processed point the optimal value of the search direction problem at that point (theta_array) Notes: The index of the point to optimize can change during the iterations. For the stopping conditions 'theta_tolerance', only the last value of theta, i.e., the one related to the last poin
- def reset_stopping_conditions_current_values(self, theta_tol: float): Reset the stopping conditions current values :param theta_tol: the new current value for the stopping condition 'theta_tolerance' Notes: The current values of the stopping conditions 'max_time' and 'max_f_evals' are changed by the memetic algorithm that employs FMOPG.

<|skeleton|>
class FMOPG:
    """The Front Multi-Objective Projected Gradient algorithm class The main functions are: - Initialize a FMOPG instance; - Execute the algorithm starting from a point of a given array; - Reset the stopping conditions current values."""

    def __init__(self, theta_tol: float, gurobi: bool, gurobi_method: int, gurobi_verbose: bool, ALS_alpha_0: float, ALS_delta: float, ALS_beta: float, ALS_min_alpha: float, max_iter: int=None, max_time: float=None, max_f_evals: int=None):
        """Initialize a FMOPG instance :param theta_tol: it indicates the tolerance after which a point is considered Pareto-stationary; it can be seen as the epsilon value for the epsilon-Pareto-stationarity; for more details, the user is referred to the article :param gurobi: if set to True, the Gurobi Optimizer is used to solve the search direction problem :param gurobi_method: it indicates the method used by the Gurobi Optimizer :param gurobi_verbose: if set to True, it enables the verbosity for the Gurobi optimizer :param ALS_alpha_0: it indicates the initial step size for the Armijo-Type Line Search :param ALS_delta: it indicates the coefficient for the step size contraction :param ALS_beta: it i"""
        <|body_0|>

    def search(self, p_list: np.array, f_list: np.array, problem: Problem, index_initial_point: int=None, I: tuple=None):
        """Execute the algorithm starting from a point of a given array :param p_list: problem solutions :param f_list: related points in the objectives space :param problem: the considered problem :param index_initial_point: the index of the problem solution to optimize :param I: the subset of objective functions indices to consider (see nsma.py) :return: the new arrays p_list and f_list; an array which contains for each processed point the optimal value of the search direction problem at that point (theta_array) Notes: The index of the point to optimize can change during the iterations. For the stopping conditions 'theta_tolerance', only the last value of theta, i.e., the one related to the last poin"""
        <|body_1|>

    def reset_stopping_conditions_current_values(self, theta_tol: float):
        """Reset the stopping conditions current values :param theta_tol: the new current value for the stopping condition 'theta_tolerance' Notes: The current values of the stopping conditions 'max_time' and 'max_f_evals' are changed by the memetic algorithm that employs FMOPG."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        GradientBasedAlgorithm.__init__(self, max_iter, max_time, max_f_evals, False, 0, False, False, 0, theta_tol, gurobi, gurobi_method, gurobi_verbose, ALS_alpha_0, ALS_delta, ALS_beta, ALS_min_alpha, name_DDS='Boundconstrained_Projected_Gradient_DDS', name_ALS='BoundconstrainedFrontALS')
        self.__theta_array = np.array([-np.inf], dtype=float)
        GradientBasedAlgorithm.add_stopping_condition(self, 'theta_tolerance', theta_tol, self.__theta_array[0], equal_required=True)
        self.__alpha_array = np.array([1], dtype=float)
        GradientBasedAlgorithm.add_stopping_condition(self, 'min_alpha', 0, self.__alpha_array[0], smaller_value_required=True, equal_required=True)
<|end_body_0|>

<|body_start_1|>
        n, m = (p_list.shape[1], f_list.shape[1])
        index_point = index_initial_point
        while not self.evaluate_stopping_conditions():
            n_iteration = self.get_stopping_condition_current_value('max_iter')
            J = problem.evaluate_functions_jacobian(p_list[index_point, :])
            self.add_to_stopping_condition_current_value('max_f_evals', n)
            if self.evaluate_stopping_conditions():
                break
            v, theta = self._direction_solver.compute_direction(problem, J[I,], x_p=p_list[index_point, :])
            self.__theta_array[n_iteration] = theta
            self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[n_iteration])
            if theta < self._theta_tol:
                new_p, new_f, alpha, f_eval_ls = self._line_search.search(problem, p_list[index_point, :], f_list, v, theta, np.array(list(I)))
                self.add_to_stopping_condition_current_value('max_f_evals', f_eval_ls)
                self.__alpha_array[n_iteration] = alpha
                self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[n_iteration])
                if new_p is not None:
                    p_list = np.concatenate((p_list, new_p.reshape((1, n))), axis=0)
                    f_list = np.concatenate((f_list, new_f.reshape((1, m))), axis=0)
                    index_point = p_list.shape[0] - 1
                    self.__theta_array = np.concatenate((self.__theta_array, np.array([-np.inf])), axis=0)
                    self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[n_iteration + 1])
                    self.__alpha_array = np.concatenate((self.__alpha_array, np.array([1])), axis=0)
                    self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[n_iteration + 1])
            self.add_to_stopping_condition_current_value('max_iter', 1)
        return (p_list, f_list, self.__theta_array)
<|end_body_1|>

<|body_start_2|>
        self.update_stopping_condition_current_value('max_iter', 0)
        self._theta_tol = theta_tol
        self.__theta_array = np.array([-np.inf], dtype=float)
        self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[0])
        self.update_stopping_condition_reference_value('theta_tolerance', theta_tol)
        self.__alpha_array = np.array([1], dtype=float)
        self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[0])
<|end_body_2|>

Solution:
class FMOPG:
    """The Front Multi-Objective Projected Gradient algorithm class The main functions are: - Initialize a FMOPG instance; - Execute the algorithm starting from a point of a given array; - Reset the stopping conditions current values."""

    def __init__(self, theta_tol: float, gurobi: bool, gurobi_method: int, gurobi_verbose: bool, ALS_alpha_0: float, ALS_delta: float, ALS_beta: float, ALS_min_alpha: float, max_iter: int=None, max_time: float=None, max_f_evals: int=None):
        """Initialize a FMOPG instance :param theta_tol: it indicates the tolerance after which a point is considered Pareto-stationary; it can be seen as the epsilon value for the epsilon-Pareto-stationarity; for more details, the user is referred to the article :param gurobi: if set to True, the Gurobi Optimizer is used to solve the search direction problem :param gurobi_method: it indicates the method used by the Gurobi Optimizer :param gurobi_verbose: if set to True, it enables the verbosity for the Gurobi optimizer :param ALS_alpha_0: it indicates the initial step size for the Armijo-Type Line Search :param ALS_delta: it indicates the coefficient for the step size contraction :param ALS_beta: it i"""
        GradientBasedAlgorithm.__init__(self, max_iter, max_time, max_f_evals, False, 0, False, False, 0, theta_tol, gurobi, gurobi_method, gurobi_verbose, ALS_alpha_0, ALS_delta, ALS_beta, ALS_min_alpha, name_DDS='Boundconstrained_Projected_Gradient_DDS', name_ALS='BoundconstrainedFrontALS')
        self.__theta_array = np.array([-np.inf], dtype=float)
        GradientBasedAlgorithm.add_stopping_condition(self, 'theta_tolerance', theta_tol, self.__theta_array[0], equal_required=True)
        self.__alpha_array = np.array([1], dtype=float)
        GradientBasedAlgorithm.add_stopping_condition(self, 'min_alpha', 0, self.__alpha_array[0], smaller_value_required=True, equal_required=True)

    def search(self, p_list: np.array, f_list: np.array, problem: Problem, index_initial_point: int=None, I: tuple=None):
        """Execute the algorithm starting from a point of a given array :param p_list: problem solutions :param f_list: related points in the objectives space :param problem: the considered problem :param index_initial_point: the index of the problem solution to optimize :param I: the subset of objective functions indices to consider (see nsma.py) :return: the new arrays p_list and f_list; an array which contains for each processed point the optimal value of the search direction problem at that point (theta_array) Notes: The index of the point to optimize can change during the iterations. For the stopping conditions 'theta_tolerance', only the last value of theta, i.e., the one related to the last poin"""
        n, m = (p_list.shape[1], f_list.shape[1])
        index_point = index_initial_point
        while not self.evaluate_stopping_conditions():
            n_iteration = self.get_stopping_condition_current_value('max_iter')
            J = problem.evaluate_functions_jacobian(p_list[index_point, :])
            self.add_to_stopping_condition_current_value('max_f_evals', n)
            if self.evaluate_stopping_conditions():
                break
            v, theta = self._direction_solver.compute_direction(problem, J[I,], x_p=p_list[index_point, :])
            self.__theta_array[n_iteration] = theta
            self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[n_iteration])
            if theta < self._theta_tol:
                new_p, new_f, alpha, f_eval_ls = self._line_search.search(problem, p_list[index_point, :], f_list, v, theta, np.array(list(I)))
                self.add_to_stopping_condition_current_value('max_f_evals', f_eval_ls)
                self.__alpha_array[n_iteration] = alpha
                self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[n_iteration])
                if new_p is not None:
                    p_list = np.concatenate((p_list, new_p.reshape((1, n))), axis=0)
                    f_list = np.concatenate((f_list, new_f.reshape((1, m))), axis=0)
                    index_point = p_list.shape[0] - 1
                    self.__theta_array = np.concatenate((self.__theta_array, np.array([-np.inf])), axis=0)
                    self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[n_iteration + 1])
                    self.__alpha_array = np.concatenate((self.__alpha_array, np.array([1])), axis=0)
                    self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[n_iteration + 1])
            self.add_to_stopping_condition_current_value('max_iter', 1)
        return (p_list, f_list, self.__theta_array)

    def reset_stopping_conditions_current_values(self, theta_tol: float):
        """Reset the stopping conditions current values :param theta_tol: the new current value for the stopping condition 'theta_tolerance' Notes: The current values of the stopping conditions 'max_time' and 'max_f_evals' are changed by the memetic algorithm that employs FMOPG."""
        self.update_stopping_condition_current_value('max_iter', 0)
        self._theta_tol = theta_tol
        self.__theta_array = np.array([-np.inf], dtype=float)
        self.update_stopping_condition_current_value('theta_tolerance', self.__theta_array[0])
        self.update_stopping_condition_reference_value('theta_tolerance', theta_tol)
        self.__alpha_array = np.array([1], dtype=float)
        self.update_stopping_condition_current_value('min_alpha', self.__alpha_array[0])


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

Class description:
A XML-RPC servlet that can be registered in the Pelix HTTP service Calls the dispatch method given in the constructor

Method signatures and docstrings:
- def __init__(self, dispatch_method, encoding=None): Sets up the servlet
- def _simple_dispatch(self, name, params): Dispatch method
- def do_POST(self, request, response): Handles a HTTP POST request :param request: The HTTP request bean :param response: The HTTP response handler

Implement the Python class `_XmlRpcServlet` described below.

Class description:
A XML-RPC servlet that can be registered in the Pelix HTTP service Calls the dispatch method given in the constructor

Method signatures and docstrings:
- def __init__(self, dispatch_method, encoding=None): Sets up the servlet
- def _simple_dispatch(self, name, params): Dispatch method
- def do_POST(self, request, response): Handles a HTTP POST request :param request: The HTTP request bean :param response: The HTTP response handler

<|skeleton|>
class _XmlRpcServlet:
    """A XML-RPC servlet that can be registered in the Pelix HTTP service Calls the dispatch method given in the constructor"""

    def __init__(self, dispatch_method, encoding=None):
        """Sets up the servlet"""
        <|body_0|>

    def _simple_dispatch(self, name, params):
        """Dispatch method"""
        <|body_1|>

    def do_POST(self, request, response):
        """Handles a HTTP POST request :param request: The HTTP request bean :param response: The HTTP response handler"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        SimpleXMLRPCDispatcher.__init__(self, allow_none=True, encoding=encoding)
        self.register_introspection_functions()
        self._dispatch_method = dispatch_method
<|end_body_0|>

<|body_start_1|>
        try:
            return self.funcs[name](*params)
        except KeyError:
            pass
        return self._dispatch_method(name, params)
<|end_body_1|>

<|body_start_2|>
        data = to_str(request.read_data())
        result = self._marshaled_dispatch(data, self._simple_dispatch)
        response.send_content(200, result, 'text/xml')
<|end_body_2|>

Solution:
class _XmlRpcServlet:
    """A XML-RPC servlet that can be registered in the Pelix HTTP service Calls the dispatch method given in the constructor"""

    def __init__(self, dispatch_method, encoding=None):
        """Sets up the servlet"""
        SimpleXMLRPCDispatcher.__init__(self, allow_none=True, encoding=encoding)
        self.register_introspection_functions()
        self._dispatch_method = dispatch_method

    def _simple_dispatch(self, name, params):
        """Dispatch method"""
        try:
            return self.funcs[name](*params)
        except KeyError:
            pass
        return self._dispatch_method(name, params)

    def do_POST(self, request, response):
        """Handles a HTTP POST request :param request: The HTTP request bean :param response: The HTTP response handler"""
        data = to_str(request.read_data())
        result = self._marshaled_dispatch(data, self._simple_dispatch)
        response.send_content(200, result, 'text/xml')


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

Class description:
Tests the various ways of invoking the login page.

Method signatures and docstrings:
- def test_no_params(self): Tests just accessing the login URL with no params.
- def test_login(self): Tests when setting the user info with and without continue URL.
- def test_logout(self): Tests when logging out with and without continue URL.
- def test_passive(self): Tests when the user is already logged in.
- def _run_test(self, host, path_info='/', cookie_dict=None, action=None, set_email=None, set_admin=None, continue_url=None, method='GET'): Runs the login HTTP handler, returning information about the response. Args: host: The value of the HTTP Host header. path_info: The absolute path of the request. cookie_dict: A cookie dictionary with the existing cookies. action: Value of the 'action' query argument. set_email: Value of the 'email' query argument. set_admin: Value of the 'admin' query argument. continue_url: Value of the 'continue' query argument. method: The HTTP method (e.g., 'GET'). Returns: Tuple (status, location, set_cookie, content_type) where each value is the value of the corresponding header from the response; if no header exists, the value will be None. In the case of status, it will just return the integer statu

Implement the Python class `LoginPageTest` described below.

Class description:
Tests the various ways of invoking the login page.

Method signatures and docstrings:
- def test_no_params(self): Tests just accessing the login URL with no params.
- def test_login(self): Tests when setting the user info with and without continue URL.
- def test_logout(self): Tests when logging out with and without continue URL.
- def test_passive(self): Tests when the user is already logged in.
- def _run_test(self, host, path_info='/', cookie_dict=None, action=None, set_email=None, set_admin=None, continue_url=None, method='GET'): Runs the login HTTP handler, returning information about the response. Args: host: The value of the HTTP Host header. path_info: The absolute path of the request. cookie_dict: A cookie dictionary with the existing cookies. action: Value of the 'action' query argument. set_email: Value of the 'email' query argument. set_admin: Value of the 'admin' query argument. continue_url: Value of the 'continue' query argument. method: The HTTP method (e.g., 'GET'). Returns: Tuple (status, location, set_cookie, content_type) where each value is the value of the corresponding header from the response; if no header exists, the value will be None. In the case of status, it will just return the integer statu

<|skeleton|>
class LoginPageTest:
    """Tests the various ways of invoking the login page."""

    def test_no_params(self):
        """Tests just accessing the login URL with no params."""
        <|body_0|>

    def test_login(self):
        """Tests when setting the user info with and without continue URL."""
        <|body_1|>

    def test_logout(self):
        """Tests when logging out with and without continue URL."""
        <|body_2|>

    def test_passive(self):
        """Tests when the user is already logged in."""
        <|body_3|>

    def _run_test(self, host, path_info='/', cookie_dict=None, action=None, set_email=None, set_admin=None, continue_url=None, method='GET'):
        """Runs the login HTTP handler, returning information about the response. Args: host: The value of the HTTP Host header. path_info: The absolute path of the request. cookie_dict: A cookie dictionary with the existing cookies. action: Value of the 'action' query argument. set_email: Value of the 'email' query argument. set_admin: Value of the 'admin' query argument. continue_url: Value of the 'continue' query argument. method: The HTTP method (e.g., 'GET'). Returns: Tuple (status, location, set_cookie, content_type) where each value is the value of the corresponding header from the response; if no header exists, the value will be None. In the case of status, it will just return the integer statu"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {}
        action = ''
        set_email = ''
        set_admin = False
        continue_url = ''
        status, location, set_cookie, content_type = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertFalse(set_cookie)
        self.assertEqual('text/html; charset=utf-8', content_type)
<|end_body_0|>

<|body_start_1|>
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {}
        action = 'Login'
        set_email = EMAIL
        set_admin = False
        continue_url = ''
        expected_set = login._set_user_info_cookie(set_email, set_admin).strip()
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual('http://%s%s' % (host, path_info), location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual(continue_url, location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
<|end_body_1|>

<|body_start_2|>
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {'dev_appserver_login': '%s:False:%s' % (EMAIL, USER_ID)}
        action = 'Logout'
        set_email = ''
        set_admin = False
        continue_url = ''
        expected_set = login._clear_user_info_cookie().strip()
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual('http://%s%s' % (host, path_info), location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual(continue_url, location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
<|end_body_2|>

<|body_start_3|>
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {'dev_appserver_login': '%s:False:%s' % (EMAIL, USER_ID)}
        action = ''
        set_email = ''
        set_admin = False
        continue_url = '/my/fancy/url'
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, content_type = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertFalse(set_cookie)
        self.assertEqual('text/html; charset=utf-8', content_type)
        self.assertIsInstance(content_type, str)
<|end_body_3|>

<|body_start_4|>
        environ = {}
        wsgiref.util.setup_testing_defaults(environ)
        environ['SERVER_NAME'] = 'do_not_use'
        environ['SERVER_PORT'] = '666'
        environ['SERVER_PROTOCOL'] = 'HTTP/1.1'
        environ['HTTP_HOST'] = host
        environ['PATH_INFO'] = path_info
        environ['REQUEST_METHOD'] = method
        if cookie_dict:
            cookie = Cookie.SimpleCookie(cookie_dict)
            cookie_value = ';'.join((m.OutputString() for m in cookie.values()))
            environ['HTTP_COOKIE'] = cookie_value
        query_dict = {}
        if action:
            query_dict['action'] = action
        if set_email:
            query_dict['email'] = set_email
        if set_admin:
            query_dict['admin'] = set_admin
        if continue_url:
            query_dict['continue'] = continue_url
        if query_dict:
            environ['QUERY_STRING'] = urllib.urlencode(query_dict)
        response_dict = {}

        def start_response(status, headers):
            response_dict['status'] = int(status.split(' ', 1)[0])
            response_dict['headers'] = dict(((k.lower(), v) for k, v in headers))
        login.application(environ, start_response)
        return (response_dict['status'], response_dict['headers'].get('location'), response_dict['headers'].get('set-cookie'), response_dict['headers'].get('content-type'))
<|end_body_4|>

Solution:
class LoginPageTest:
    """Tests the various ways of invoking the login page."""

    def test_no_params(self):
        """Tests just accessing the login URL with no params."""
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {}
        action = ''
        set_email = ''
        set_admin = False
        continue_url = ''
        status, location, set_cookie, content_type = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertFalse(set_cookie)
        self.assertEqual('text/html; charset=utf-8', content_type)

    def test_login(self):
        """Tests when setting the user info with and without continue URL."""
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {}
        action = 'Login'
        set_email = EMAIL
        set_admin = False
        continue_url = ''
        expected_set = login._set_user_info_cookie(set_email, set_admin).strip()
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual('http://%s%s' % (host, path_info), location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual(continue_url, location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)

    def test_logout(self):
        """Tests when logging out with and without continue URL."""
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {'dev_appserver_login': '%s:False:%s' % (EMAIL, USER_ID)}
        action = 'Logout'
        set_email = ''
        set_admin = False
        continue_url = ''
        expected_set = login._clear_user_info_cookie().strip()
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual('http://%s%s' % (host, path_info), location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, _ = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertEqual(continue_url, location)
        self.assertEqual(expected_set, set_cookie)
        self.assertIsInstance(location, str)
        self.assertIsInstance(set_cookie, str)

    def test_passive(self):
        """Tests when the user is already logged in."""
        host = 'foo.com:1234'
        path_info = '/_ah/login'
        cookie_dict = {'dev_appserver_login': '%s:False:%s' % (EMAIL, USER_ID)}
        action = ''
        set_email = ''
        set_admin = False
        continue_url = '/my/fancy/url'
        continue_url = 'http://foo.com/blah'
        status, location, set_cookie, content_type = self._run_test(host, path_info, cookie_dict, action, set_email, set_admin, continue_url)
        self.assertEqual(302, status)
        self.assertFalse(set_cookie)
        self.assertEqual('text/html; charset=utf-8', content_type)
        self.assertIsInstance(content_type, str)

    def _run_test(self, host, path_info='/', cookie_dict=None, action=None, set_email=None, set_admin=None, continue_url=None, method='GET'):
        """Runs the login HTTP handler, returning information about the response. Args: host: The value of the HTTP Host header. path_info: The absolute path of the request. cookie_dict: A cookie dictionary with the existing cookies. action: Value of the 'action' query argument. set_email: Value of the 'email' query argument. set_admin: Value of the 'admin' query argument. continue_url: Value of the 'continue' query argument. method: The HTTP method (e.g., 'GET'). Returns: Tuple (status, location, set_cookie, content_type) where each value is the value of the corresponding header from the response; if no header exists, the value will be None. In the case of status, it will just return the integer statu"""
        environ = {}
        wsgiref.util.setup_testing_defaults(environ)
        environ['SERVER_NAME'] = 'do_not_use'
        environ['SERVER_PORT'] = '666'
        environ['SERVER_PROTOCOL'] = 'HTTP/1.1'
        environ['HTTP_HOST'] = host
        environ['PATH_INFO'] = path_info
        environ['REQUEST_METHOD'] = method
        if cookie_dict:
            cookie = Cookie.SimpleCookie(cookie_dict)
            cookie_value = ';'.join((m.OutputString() for m in cookie.values()))
            environ['HTTP_COOKIE'] = cookie_value
        query_dict = {}
        if action:
            query_dict['action'] = action
        if set_email:
            query_dict['email'] = set_email
        if set_admin:
            query_dict['admin'] = set_admin
        if continue_url:
            query_dict['continue'] = continue_url
        if query_dict:
            environ['QUERY_STRING'] = urllib.urlencode(query_dict)
        response_dict = {}

        def start_response(status, headers):
            response_dict['status'] = int(status.split(' ', 1)[0])
            response_dict['headers'] = dict(((k.lower(), v) for k, v in headers))
        login.application(environ, start_response)
        return (response_dict['status'], response_dict['headers'].get('location'), response_dict['headers'].get('set-cookie'), response_dict['headers'].get('content-type'))


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

Class description:
Docstring for ConfigManager.

Method signatures and docstrings:
- def __init__(self, filename): TODO :param filename: TODO
- def load_config(self, target): Return the corresponding string representing the websites. :param str target: TODO :returns: an ``AnimeWebsite`` object, depending on the input :rtype: AnimeWebsite
- def fallback_file(cls, filename): Return the fallback file for config. It will first try given file, then ~/.config/hiromi.json, then ~/.hiromi :param str filename: the given file :returns: a usable filename :rtype: AnimeWebsite

Implement the Python class `ConfigManager` described below.

Class description:
Docstring for ConfigManager.

Method signatures and docstrings:
- def __init__(self, filename): TODO :param filename: TODO
- def load_config(self, target): Return the corresponding string representing the websites. :param str target: TODO :returns: an ``AnimeWebsite`` object, depending on the input :rtype: AnimeWebsite
- def fallback_file(cls, filename): Return the fallback file for config. It will first try given file, then ~/.config/hiromi.json, then ~/.hiromi :param str filename: the given file :returns: a usable filename :rtype: AnimeWebsite

<|skeleton|>
class ConfigManager:
    """Docstring for ConfigManager."""

    def __init__(self, filename):
        """TODO :param filename: TODO"""
        <|body_0|>

    def load_config(self, target):
        """Return the corresponding string representing the websites. :param str target: TODO :returns: an ``AnimeWebsite`` object, depending on the input :rtype: AnimeWebsite"""
        <|body_1|>

    def fallback_file(cls, filename):
        """Return the fallback file for config. It will first try given file, then ~/.config/hiromi.json, then ~/.hiromi :param str filename: the given file :returns: a usable filename :rtype: AnimeWebsite"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        _filename = ConfigManager.fallback_file(filename)
        with open(_filename) as f:
            self._config = json.loads(f.read())
<|end_body_0|>

<|body_start_1|>
        def new_bgm():
            return Bangumi(self._config['bgm']['account'], self._config['bgm']['password'])

        def new_mal():
            return MyAnimeList(self._config['mal']['account'], self._config['mal']['password'])
        target = to_symbol(target)
        if target == 'bgm':
            result = new_bgm()
        elif target == 'mal':
            result = new_mal()
        else:
            result = Hiromi(new_bgm, new_mal)
        return result
<|end_body_1|>

<|body_start_2|>
        file_list = (filename, path.join(path.expanduser('~'), '.config', 'hiromi.json'), path.join(path.expanduser('~'), '.hiromi'))
        for a_file in file_list:
            if path.exists(a_file):
                return a_file
        print('Please given a legal config file, or make a config file at~/.hiromi or ~/.config/hiromi.json')
        raise ConfigNotFoundException()
<|end_body_2|>

Solution:
class ConfigManager:
    """Docstring for ConfigManager."""

    def __init__(self, filename):
        """TODO :param filename: TODO"""
        _filename = ConfigManager.fallback_file(filename)
        with open(_filename) as f:
            self._config = json.loads(f.read())

    def load_config(self, target):
        """Return the corresponding string representing the websites. :param str target: TODO :returns: an ``AnimeWebsite`` object, depending on the input :rtype: AnimeWebsite"""
        def new_bgm():
            return Bangumi(self._config['bgm']['account'], self._config['bgm']['password'])

        def new_mal():
            return MyAnimeList(self._config['mal']['account'], self._config['mal']['password'])
        target = to_symbol(target)
        if target == 'bgm':
            result = new_bgm()
        elif target == 'mal':
            result = new_mal()
        else:
            result = Hiromi(new_bgm, new_mal)
        return result

    def fallback_file(cls, filename):
        """Return the fallback file for config. It will first try given file, then ~/.config/hiromi.json, then ~/.hiromi :param str filename: the given file :returns: a usable filename :rtype: AnimeWebsite"""
        file_list = (filename, path.join(path.expanduser('~'), '.config', 'hiromi.json'), path.join(path.expanduser('~'), '.hiromi'))
        for a_file in file_list:
            if path.exists(a_file):
                return a_file
        print('Please given a legal config file, or make a config file at~/.hiromi or ~/.config/hiromi.json')
        raise ConfigNotFoundException()


serialize

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

Class description:
Implement the ProcessEvidence class.

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProcessEvidence: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: ProcessEvidence
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

Implement the Python class `ProcessEvidence` described below.

Class description:
Implement the ProcessEvidence class.

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProcessEvidence: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: ProcessEvidence
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

<|skeleton|>
class ProcessEvidence:

    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProcessEvidence:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: ProcessEvidence"""
        <|body_0|>

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        <|body_1|>

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return ProcessEvidence()
<|end_body_0|>

<|body_start_1|>
        from .alert_evidence import AlertEvidence
        from .detection_status import DetectionStatus
        from .file_details import FileDetails
        from .user_account import UserAccount
        from .alert_evidence import AlertEvidence
        from .detection_status import DetectionStatus
        from .file_details import FileDetails
        from .user_account import UserAccount
        fields: Dict[str, Callable[[Any], None]] = {'detectionStatus': lambda n: setattr(self, 'detection_status', n.get_enum_value(DetectionStatus)), 'imageFile': lambda n: setattr(self, 'image_file', n.get_object_value(FileDetails)), 'mdeDeviceId': lambda n: setattr(self, 'mde_device_id', n.get_str_value()), 'parentProcessCreationDateTime': lambda n: setattr(self, 'parent_process_creation_date_time', n.get_datetime_value()), 'parentProcessId': lambda n: setattr(self, 'parent_process_id', n.get_int_value()), 'parentProcessImageFile': lambda n: setattr(self, 'parent_process_image_file', n.get_object_value(FileDetails)), 'processCommandLine': lambda n: setattr(self, 'process_command_line', n.get_str_value()), 'processCreationDateTime': lambda n: setattr(self, 'process_creation_date_time', n.get_datetime_value()), 'processId': lambda n: setattr(self, 'process_id', n.get_int_value()), 'userAccount': lambda n: setattr(self, 'user_account', n.get_object_value(UserAccount))}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_enum_value('detectionStatus', self.detection_status)
        writer.write_object_value('imageFile', self.image_file)
        writer.write_str_value('mdeDeviceId', self.mde_device_id)
        writer.write_datetime_value('parentProcessCreationDateTime', self.parent_process_creation_date_time)
        writer.write_int_value('parentProcessId', self.parent_process_id)
        writer.write_object_value('parentProcessImageFile', self.parent_process_image_file)
        writer.write_str_value('processCommandLine', self.process_command_line)
        writer.write_datetime_value('processCreationDateTime', self.process_creation_date_time)
        writer.write_int_value('processId', self.process_id)
        writer.write_object_value('userAccount', self.user_account)
<|end_body_2|>

Solution:
class ProcessEvidence:
    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProcessEvidence:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: ProcessEvidence"""
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return ProcessEvidence()

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .alert_evidence import AlertEvidence
        from .detection_status import DetectionStatus
        from .file_details import FileDetails
        from .user_account import UserAccount
        from .alert_evidence import AlertEvidence
        from .detection_status import DetectionStatus
        from .file_details import FileDetails
        from .user_account import UserAccount
        fields: Dict[str, Callable[[Any], None]] = {'detectionStatus': lambda n: setattr(self, 'detection_status', n.get_enum_value(DetectionStatus)), 'imageFile': lambda n: setattr(self, 'image_file', n.get_object_value(FileDetails)), 'mdeDeviceId': lambda n: setattr(self, 'mde_device_id', n.get_str_value()), 'parentProcessCreationDateTime': lambda n: setattr(self, 'parent_process_creation_date_time', n.get_datetime_value()), 'parentProcessId': lambda n: setattr(self, 'parent_process_id', n.get_int_value()), 'parentProcessImageFile': lambda n: setattr(self, 'parent_process_image_file', n.get_object_value(FileDetails)), 'processCommandLine': lambda n: setattr(self, 'process_command_line', n.get_str_value()), 'processCreationDateTime': lambda n: setattr(self, 'process_creation_date_time', n.get_datetime_value()), 'processId': lambda n: setattr(self, 'process_id', n.get_int_value()), 'userAccount': lambda n: setattr(self, 'user_account', n.get_object_value(UserAccount))}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_enum_value('detectionStatus', self.detection_status)
        writer.write_object_value('imageFile', self.image_file)
        writer.write_str_value('mdeDeviceId', self.mde_device_id)
        writer.write_datetime_value('parentProcessCreationDateTime', self.parent_process_creation_date_time)
        writer.write_int_value('parentProcessId', self.parent_process_id)
        writer.write_object_value('parentProcessImageFile', self.parent_process_image_file)
        writer.write_str_value('processCommandLine', self.process_command_line)
        writer.write_datetime_value('processCreationDateTime', self.process_creation_date_time)
        writer.write_int_value('processId', self.process_id)
        writer.write_object_value('userAccount', self.user_account)


post

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

Class description:
升级处理

Method signatures and docstrings:
- def get(self, request): 升级列表 :param request: :return:
- def post(self, request): 添加升级 { { "file_id": 1, "u_type": "center", "targets": [ {"uuid": "uuid-1", "id": 1}, {"uuid": "uuid-2", "id": 2} ] } } :param request: :return:

Implement the Python class `SystemUpgradeList` described below.

Class description:
升级处理

Method signatures and docstrings:
- def get(self, request): 升级列表 :param request: :return:
- def post(self, request): 添加升级 { { "file_id": 1, "u_type": "center", "targets": [ {"uuid": "uuid-1", "id": 1}, {"uuid": "uuid-2", "id": 2} ] } } :param request: :return:

<|skeleton|>
class SystemUpgradeList:
    """升级处理"""

    def get(self, request):
        """升级列表 :param request: :return:"""
        <|body_0|>

    def post(self, request):
        """添加升级 { { "file_id": 1, "u_type": "center", "targets": [ {"uuid": "uuid-1", "id": 1}, {"uuid": "uuid-2", "id": 2} ] } } :param request: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        agent_now = request.user.userinfo.agent
        u_type = request.query_params.get('u_type')
        filter_data = dict()
        if u_type and u_type in ['center', 'monitor', 'scan', 'defense', 'cloud_defense', 'hids', 'nids']:
            filter_data['u_type'] = u_type
        upgrade_tasks = models.SystemUpgradeTask.objects.filter(agent=agent_now, **filter_data)
        '\n            - task_id           升级任务ID\n            - name              中心名称\n            - current_version   当前版本\n            - upgrade_version   升级版本\n            - status            状态，0-未完成，1-完成，2-错误\n            - percent           完成百分比\n        '
        upgrade_list = []
        for u_task in upgrade_tasks:
            upgrade_list.append({'task_id': u_task.id, 'name': u_task.target_name, 'current_version': u_task.target_version, 'upgrade_version': u_task.file_version, 'status': u_task.status, 'percent': u_task.percent})
        status_list = upgrade_tasks.values_list('status', flat=True)
        status = 0
        if 0 in status_list:
            status = 1
        context = {'status': 200, 'msg': '获取列表成功', 'data': {'status': status, 'list': upgrade_list}}
        return Response(context)
<|end_body_0|>

<|body_start_1|>
        agent_now = request.user.userinfo.agent
        upgrade_ser = serializers.SystemUpgradeSerializers(data=request.data, context={'request': request, 'agent': agent_now})
        if upgrade_ser.is_valid():
            upgrade_detail = upgrade_ser.save()
        else:
            msg = '添加升级任务错误'
            for error in upgrade_ser.errors:
                msg = upgrade_ser.errors[error][0]
                break
            context = {'status': 500, 'msg': msg, 'error': upgrade_ser.errors}
            return Response(context)
        upgrade_targets(agent_now, upgrade_detail)
        return Response({'msg': '添加升级任务成功', 'status': 200})
<|end_body_1|>

Solution:
class SystemUpgradeList:
    """升级处理"""

    def get(self, request):
        """升级列表 :param request: :return:"""
        agent_now = request.user.userinfo.agent
        u_type = request.query_params.get('u_type')
        filter_data = dict()
        if u_type and u_type in ['center', 'monitor', 'scan', 'defense', 'cloud_defense', 'hids', 'nids']:
            filter_data['u_type'] = u_type
        upgrade_tasks = models.SystemUpgradeTask.objects.filter(agent=agent_now, **filter_data)
        '\n            - task_id           升级任务ID\n            - name              中心名称\n            - current_version   当前版本\n            - upgrade_version   升级版本\n            - status            状态，0-未完成，1-完成，2-错误\n            - percent           完成百分比\n        '
        upgrade_list = []
        for u_task in upgrade_tasks:
            upgrade_list.append({'task_id': u_task.id, 'name': u_task.target_name, 'current_version': u_task.target_version, 'upgrade_version': u_task.file_version, 'status': u_task.status, 'percent': u_task.percent})
        status_list = upgrade_tasks.values_list('status', flat=True)
        status = 0
        if 0 in status_list:
            status = 1
        context = {'status': 200, 'msg': '获取列表成功', 'data': {'status': status, 'list': upgrade_list}}
        return Response(context)

    def post(self, request):
        """添加升级 { { "file_id": 1, "u_type": "center", "targets": [ {"uuid": "uuid-1", "id": 1}, {"uuid": "uuid-2", "id": 2} ] } } :param request: :return:"""
        agent_now = request.user.userinfo.agent
        upgrade_ser = serializers.SystemUpgradeSerializers(data=request.data, context={'request': request, 'agent': agent_now})
        if upgrade_ser.is_valid():
            upgrade_detail = upgrade_ser.save()
        else:
            msg = '添加升级任务错误'
            for error in upgrade_ser.errors:
                msg = upgrade_ser.errors[error][0]
                break
            context = {'status': 500, 'msg': msg, 'error': upgrade_ser.errors}
            return Response(context)
        upgrade_targets(agent_now, upgrade_detail)
        return Response({'msg': '添加升级任务成功', 'status': 200})