post

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

Class description:
Edit existing appointments using the UpdateAppointmentsForm

Method signatures and docstrings:
- def get(self, request, appointment_id): When loading the page, the fields should be filled with their existing values :param request: The user who is requesting the page :param appointment_id: The unique id of the appointment to be edited :return: If the request is valid, a page that has a form loaded with the current appointment information. Otherwise, an HttpResponse with an error message
- def post(self, request, appointment_id): Submitting the for will update the current appointment :param request: The user who is requesting the page :param appointment_id: The unique id of the Appointment to be updated :return: If there form is valid, redirect the user to the views page. If the form is not valid, re-render the page

Implement the Python class `DoctorAppointmentEdit` described below.

Class description:
Edit existing appointments using the UpdateAppointmentsForm

Method signatures and docstrings:
- def get(self, request, appointment_id): When loading the page, the fields should be filled with their existing values :param request: The user who is requesting the page :param appointment_id: The unique id of the appointment to be edited :return: If the request is valid, a page that has a form loaded with the current appointment information. Otherwise, an HttpResponse with an error message
- def post(self, request, appointment_id): Submitting the for will update the current appointment :param request: The user who is requesting the page :param appointment_id: The unique id of the Appointment to be updated :return: If there form is valid, redirect the user to the views page. If the form is not valid, re-render the page

<|skeleton|>
class DoctorAppointmentEdit:
    """Edit existing appointments using the UpdateAppointmentsForm"""

    def get(self, request, appointment_id):
        """When loading the page, the fields should be filled with their existing values :param request: The user who is requesting the page :param appointment_id: The unique id of the appointment to be edited :return: If the request is valid, a page that has a form loaded with the current appointment information. Otherwise, an HttpResponse with an error message"""
        <|body_0|>

    def post(self, request, appointment_id):
        """Submitting the for will update the current appointment :param request: The user who is requesting the page :param appointment_id: The unique id of the Appointment to be updated :return: If there form is valid, redirect the user to the views page. If the form is not valid, re-render the page"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if request.user.is_authenticated():
            appointment = Appointment.objects.get(id=appointment_id)
            user = UserFactory.get_user(request)
            if user != None and appointment.can_view(user):
                appointment_update_form = self.update_form_class(None)
                form_data = {'description': appointment.description, 'location': appointment.location, 'date': appointment.date, 'time': appointment.time, 'endTime': appointment.endTime}
                appointment_update_form = self.update_form_class(initial=form_data)
                return render(request, self.template_name, {'appointment_update_form': appointment_update_form})
            else:
                return HttpResponse('You tried editing an appointment that is not yours')
        return redirect('/userprofile/login')
<|end_body_0|>

<|body_start_1|>
        if request.user.is_authenticated():
            appointment = Appointment.objects.get(id=appointment_id)
            appointment_update_form = self.update_form_class(request.POST, instance=appointment)
            if appointment_update_form.is_valid():
                appointment.save()
                logEntry = Entry()
                logEntry.user = request.user.username
                logEntry.trigger = 'appointment.views.AppointmentEdit'
                logEntry.activity = 'Updated Appointment'
                logEntry.save()
                return redirect('/employee/appointment/view')
            return render(request, self.template_name, {'appointment_update_form': appointment_update_form})
        return redirect('/userprofile/login')
<|end_body_1|>

Solution:
class DoctorAppointmentEdit:
    """Edit existing appointments using the UpdateAppointmentsForm"""

    def get(self, request, appointment_id):
        """When loading the page, the fields should be filled with their existing values :param request: The user who is requesting the page :param appointment_id: The unique id of the appointment to be edited :return: If the request is valid, a page that has a form loaded with the current appointment information. Otherwise, an HttpResponse with an error message"""
        if request.user.is_authenticated():
            appointment = Appointment.objects.get(id=appointment_id)
            user = UserFactory.get_user(request)
            if user != None and appointment.can_view(user):
                appointment_update_form = self.update_form_class(None)
                form_data = {'description': appointment.description, 'location': appointment.location, 'date': appointment.date, 'time': appointment.time, 'endTime': appointment.endTime}
                appointment_update_form = self.update_form_class(initial=form_data)
                return render(request, self.template_name, {'appointment_update_form': appointment_update_form})
            else:
                return HttpResponse('You tried editing an appointment that is not yours')
        return redirect('/userprofile/login')

    def post(self, request, appointment_id):
        """Submitting the for will update the current appointment :param request: The user who is requesting the page :param appointment_id: The unique id of the Appointment to be updated :return: If there form is valid, redirect the user to the views page. If the form is not valid, re-render the page"""
        if request.user.is_authenticated():
            appointment = Appointment.objects.get(id=appointment_id)
            appointment_update_form = self.update_form_class(request.POST, instance=appointment)
            if appointment_update_form.is_valid():
                appointment.save()
                logEntry = Entry()
                logEntry.user = request.user.username
                logEntry.trigger = 'appointment.views.AppointmentEdit'
                logEntry.activity = 'Updated Appointment'
                logEntry.save()
                return redirect('/employee/appointment/view')
            return render(request, self.template_name, {'appointment_update_form': appointment_update_form})
        return redirect('/userprofile/login')


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

Class description:
n-gram generator.

Method signatures and docstrings:
- def __init__(self, model): model -- n-gram model.
- def generate_sent(self): Randomly generate a sentence.
- def generate_token(self, prev_tokens=None): Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1).

Implement the Python class `NGramGenerator` described below.

Class description:
n-gram generator.

Method signatures and docstrings:
- def __init__(self, model): model -- n-gram model.
- def generate_sent(self): Randomly generate a sentence.
- def generate_token(self, prev_tokens=None): Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1).

<|skeleton|>
class NGramGenerator:
    """n-gram generator."""

    def __init__(self, model):
        """model -- n-gram model."""
        <|body_0|>

    def generate_sent(self):
        """Randomly generate a sentence."""
        <|body_1|>

    def generate_token(self, prev_tokens=None):
        """Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1)."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.n = model.n
        self.probs = probs = dict()
        self.sorted_probs = dict()
        pre = [elem for elem in model.counts.keys() if not len(elem) == self.n]
        suf = [elem for elem in model.counts.keys() if len(elem) == self.n]
        for elem in suf:
            prfx = elem[:-1]
            sfx = elem[-1]
            if prfx in probs:
                aux = probs[prfx]
                probs[prfx] = {sfx: model.cond_prob(sfx, prfx)}
                probs[prfx].update(aux)
            else:
                probs[prfx] = {sfx: model.cond_prob(sfx, prfx)}
        sp = [list(probs[x].items()) for x in pre]
        self.sorted_probs = {pre[i]: sorted(sp[i], key=lambda x: (-x[1], x[0])) for i in range(len(sp))}
<|end_body_0|>

<|body_start_1|>
        n = self.n
        sent = ('<s>',) * (n - 1)
        if n == 1:
            sent = ()
        while '</s>' not in sent:
            sent += (self.generate_token(sent[-n + 1:]),)
        return sent[n - 1:-1]
<|end_body_1|>

<|body_start_2|>
        n = self.n
        if n == 1:
            prev_tokens = tuple()
        p = random()
        res = ''
        choices = self.sorted_probs[prev_tokens]
        acc = choices[0][1]
        for i in range(0, len(choices)):
            if p < acc:
                res = choices[i][0]
                break
            else:
                acc += choices[i][1]
        return res
<|end_body_2|>

Solution:
class NGramGenerator:
    """n-gram generator."""

    def __init__(self, model):
        """model -- n-gram model."""
        self.n = model.n
        self.probs = probs = dict()
        self.sorted_probs = dict()
        pre = [elem for elem in model.counts.keys() if not len(elem) == self.n]
        suf = [elem for elem in model.counts.keys() if len(elem) == self.n]
        for elem in suf:
            prfx = elem[:-1]
            sfx = elem[-1]
            if prfx in probs:
                aux = probs[prfx]
                probs[prfx] = {sfx: model.cond_prob(sfx, prfx)}
                probs[prfx].update(aux)
            else:
                probs[prfx] = {sfx: model.cond_prob(sfx, prfx)}
        sp = [list(probs[x].items()) for x in pre]
        self.sorted_probs = {pre[i]: sorted(sp[i], key=lambda x: (-x[1], x[0])) for i in range(len(sp))}

    def generate_sent(self):
        """Randomly generate a sentence."""
        n = self.n
        sent = ('<s>',) * (n - 1)
        if n == 1:
            sent = ()
        while '</s>' not in sent:
            sent += (self.generate_token(sent[-n + 1:]),)
        return sent[n - 1:-1]

    def generate_token(self, prev_tokens=None):
        """Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1)."""
        n = self.n
        if n == 1:
            prev_tokens = tuple()
        p = random()
        res = ''
        choices = self.sorted_probs[prev_tokens]
        acc = choices[0][1]
        for i in range(0, len(choices)):
            if p < acc:
                res = choices[i][0]
                break
            else:
                acc += choices[i][1]
        return res


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

Class description:
Numeric data reader abstraction. Reads a single float, int, str or other from data

Method signatures and docstrings:
- def __init__(self, input_key: str, output_key: Optional[str]=None, dtype: Type=np.float32, default_value: float=None, one_hot_classes: int=None, smoothing: float=None): Args: input_key: input key to use from annotation dict output_key: output key to use to store the result, default: ``input_key`` dtype: datatype of scalar values to use default_value: default value to use if something goes wrong one_hot_classes: number of one-hot classes smoothing (float, optional): if specified applies label smoothing to one_hot classes
- def __call__(self, element): Reads a row from your annotations dict and transfer it to a single value Args: element: elem in your dataset Returns: dtype: Scalar value

Implement the Python class `ScalarReader` described below.

Class description:
Numeric data reader abstraction. Reads a single float, int, str or other from data

Method signatures and docstrings:
- def __init__(self, input_key: str, output_key: Optional[str]=None, dtype: Type=np.float32, default_value: float=None, one_hot_classes: int=None, smoothing: float=None): Args: input_key: input key to use from annotation dict output_key: output key to use to store the result, default: ``input_key`` dtype: datatype of scalar values to use default_value: default value to use if something goes wrong one_hot_classes: number of one-hot classes smoothing (float, optional): if specified applies label smoothing to one_hot classes
- def __call__(self, element): Reads a row from your annotations dict and transfer it to a single value Args: element: elem in your dataset Returns: dtype: Scalar value

<|skeleton|>
class ScalarReader:
    """Numeric data reader abstraction. Reads a single float, int, str or other from data"""

    def __init__(self, input_key: str, output_key: Optional[str]=None, dtype: Type=np.float32, default_value: float=None, one_hot_classes: int=None, smoothing: float=None):
        """Args: input_key: input key to use from annotation dict output_key: output key to use to store the result, default: ``input_key`` dtype: datatype of scalar values to use default_value: default value to use if something goes wrong one_hot_classes: number of one-hot classes smoothing (float, optional): if specified applies label smoothing to one_hot classes"""
        <|body_0|>

    def __call__(self, element):
        """Reads a row from your annotations dict and transfer it to a single value Args: element: elem in your dataset Returns: dtype: Scalar value"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(input_key, output_key or input_key)
        self.dtype = dtype
        self.default_value = default_value
        self.one_hot_classes = one_hot_classes
        self.smoothing = smoothing
        if self.one_hot_classes is not None and self.smoothing is not None:
            assert 0.0 < smoothing < 1.0, 'If smoothing is specified it must be in (0; 1), ' + f'got {smoothing}'
<|end_body_0|>

<|body_start_1|>
        scalar = self.dtype(element.get(self.input_key, self.default_value))
        if self.one_hot_classes is not None:
            scalar = get_one_hot(scalar, self.one_hot_classes, smoothing=self.smoothing)
        output = {self.output_key: scalar}
        return output
<|end_body_1|>

Solution:
class ScalarReader:
    """Numeric data reader abstraction. Reads a single float, int, str or other from data"""

    def __init__(self, input_key: str, output_key: Optional[str]=None, dtype: Type=np.float32, default_value: float=None, one_hot_classes: int=None, smoothing: float=None):
        """Args: input_key: input key to use from annotation dict output_key: output key to use to store the result, default: ``input_key`` dtype: datatype of scalar values to use default_value: default value to use if something goes wrong one_hot_classes: number of one-hot classes smoothing (float, optional): if specified applies label smoothing to one_hot classes"""
        super().__init__(input_key, output_key or input_key)
        self.dtype = dtype
        self.default_value = default_value
        self.one_hot_classes = one_hot_classes
        self.smoothing = smoothing
        if self.one_hot_classes is not None and self.smoothing is not None:
            assert 0.0 < smoothing < 1.0, 'If smoothing is specified it must be in (0; 1), ' + f'got {smoothing}'

    def __call__(self, element):
        """Reads a row from your annotations dict and transfer it to a single value Args: element: elem in your dataset Returns: dtype: Scalar value"""
        scalar = self.dtype(element.get(self.input_key, self.default_value))
        if self.one_hot_classes is not None:
            scalar = get_one_hot(scalar, self.one_hot_classes, smoothing=self.smoothing)
        output = {self.output_key: scalar}
        return output


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

Class description:
A suite of tests for the ``get_iiq_version`` and ``get_iiqtools_version`` functions

Method signatures and docstrings:
- def test_get_iiq_version(self, fake_get_distribution): None is returned if InsightIQ is not installed
- def test_get_iiqtools_version(self, fake_get_distribution): None is returned if IIQTools is not installed
- def test_get_iiq_version_ok(self, fake_get_distribution): Version is returned if InsightIQ is installed
- def test_get_iiqtools_version_ok(self, fake_get_distribution): Version is returned if IIQTools is installed

Implement the Python class `TestGetVersions` described below.

Class description:
A suite of tests for the ``get_iiq_version`` and ``get_iiqtools_version`` functions

Method signatures and docstrings:
- def test_get_iiq_version(self, fake_get_distribution): None is returned if InsightIQ is not installed
- def test_get_iiqtools_version(self, fake_get_distribution): None is returned if IIQTools is not installed
- def test_get_iiq_version_ok(self, fake_get_distribution): Version is returned if InsightIQ is installed
- def test_get_iiqtools_version_ok(self, fake_get_distribution): Version is returned if IIQTools is installed

<|skeleton|>
class TestGetVersions:
    """A suite of tests for the ``get_iiq_version`` and ``get_iiqtools_version`` functions"""

    def test_get_iiq_version(self, fake_get_distribution):
        """None is returned if InsightIQ is not installed"""
        <|body_0|>

    def test_get_iiqtools_version(self, fake_get_distribution):
        """None is returned if IIQTools is not installed"""
        <|body_1|>

    def test_get_iiq_version_ok(self, fake_get_distribution):
        """Version is returned if InsightIQ is installed"""
        <|body_2|>

    def test_get_iiqtools_version_ok(self, fake_get_distribution):
        """Version is returned if IIQTools is installed"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        fake_get_distribution.side_effect = [versions.DistributionNotFound()]
        v = versions.get_iiq_version()
        self.assertTrue(v is None)
<|end_body_0|>

<|body_start_1|>
        fake_get_distribution.side_effect = [versions.DistributionNotFound()]
        v = versions.get_iiqtools_version()
        self.assertTrue(v is None)
<|end_body_1|>

<|body_start_2|>
        fake_get_distribution.return_value = self.FakeDistVersion('3.3.4')
        v = versions.get_iiq_version()
        self.assertTrue(isinstance(v, versions.Version))
<|end_body_2|>

<|body_start_3|>
        fake_get_distribution.return_value = self.FakeDistVersion('1.2.3')
        v = versions.get_iiqtools_version()
        self.assertTrue(isinstance(v, versions.Version))
<|end_body_3|>

Solution:
class TestGetVersions:
    """A suite of tests for the ``get_iiq_version`` and ``get_iiqtools_version`` functions"""

    def test_get_iiq_version(self, fake_get_distribution):
        """None is returned if InsightIQ is not installed"""
        fake_get_distribution.side_effect = [versions.DistributionNotFound()]
        v = versions.get_iiq_version()
        self.assertTrue(v is None)

    def test_get_iiqtools_version(self, fake_get_distribution):
        """None is returned if IIQTools is not installed"""
        fake_get_distribution.side_effect = [versions.DistributionNotFound()]
        v = versions.get_iiqtools_version()
        self.assertTrue(v is None)

    def test_get_iiq_version_ok(self, fake_get_distribution):
        """Version is returned if InsightIQ is installed"""
        fake_get_distribution.return_value = self.FakeDistVersion('3.3.4')
        v = versions.get_iiq_version()
        self.assertTrue(isinstance(v, versions.Version))

    def test_get_iiqtools_version_ok(self, fake_get_distribution):
        """Version is returned if IIQTools is installed"""
        fake_get_distribution.return_value = self.FakeDistVersion('1.2.3')
        v = versions.get_iiqtools_version()
        self.assertTrue(isinstance(v, versions.Version))


sumRegion

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

Class description:
Implement the NumMatrix class.

Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def update(self, row, col, val): :type row: int :type col: int :type val: int :rtype: void
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype: int

Implement the Python class `NumMatrix` described below.

Class description:
Implement the NumMatrix class.

Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def update(self, row, col, val): :type row: int :type col: int :type val: int :rtype: void
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype: int

<|skeleton|>
class NumMatrix:

    def __init__(self, matrix):
        """:type matrix: List[List[int]]"""
        <|body_0|>

    def update(self, row, col, val):
        """:type row: int :type col: int :type val: int :rtype: void"""
        <|body_1|>

    def sumRegion(self, row1, col1, row2, col2):
        """:type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.matrix = matrix
        self.preSumMatrix = []
        for i in range(len(matrix)):
            cur = 0
            self.preSumMatrix.append([])
            for j in range(len(matrix[0])):
                cur += matrix[i][j]
                self.preSumMatrix[i].append(cur)
<|end_body_0|>

<|body_start_1|>
        diff = val - self.matrix[row][col]
        for i in range(col, len(self.preSumMatrix[0])):
            self.preSumMatrix[row][i] = self.preSumMatrix[row][i] + diff
        self.matrix[row][col] = val
<|end_body_1|>

<|body_start_2|>
        ans = 0
        for i in range(row1, row2 + 1):
            ans += self.preSumMatrix[i][col2]
            if col1 != 0:
                ans -= self.preSumMatrix[i][col1 - 1]
        return ans
<|end_body_2|>

Solution:
class NumMatrix:
    def __init__(self, matrix):
        """:type matrix: List[List[int]]"""
        self.matrix = matrix
        self.preSumMatrix = []
        for i in range(len(matrix)):
            cur = 0
            self.preSumMatrix.append([])
            for j in range(len(matrix[0])):
                cur += matrix[i][j]
                self.preSumMatrix[i].append(cur)

    def update(self, row, col, val):
        """:type row: int :type col: int :type val: int :rtype: void"""
        diff = val - self.matrix[row][col]
        for i in range(col, len(self.preSumMatrix[0])):
            self.preSumMatrix[row][i] = self.preSumMatrix[row][i] + diff
        self.matrix[row][col] = val

    def sumRegion(self, row1, col1, row2, col2):
        """:type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
        ans = 0
        for i in range(row1, row2 + 1):
            ans += self.preSumMatrix[i][col2]
            if col1 != 0:
                ans -= self.preSumMatrix[i][col1 - 1]
        return ans


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCameraCover_wrong(self, root): :type root: TreeNode :rtype: int
- def minCameraCover(self, root): :type root: TreeNode :rtype: int
- def minCameraCover2(self, root): :type root: TreeNode :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCameraCover_wrong(self, root): :type root: TreeNode :rtype: int
- def minCameraCover(self, root): :type root: TreeNode :rtype: int
- def minCameraCover2(self, root): :type root: TreeNode :rtype: int

<|skeleton|>
class Solution:

    def minCameraCover_wrong(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_0|>

    def minCameraCover(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_1|>

    def minCameraCover2(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        dp, all = (collections.defaultdict(int), collections.defaultdict(int))

        def dfs(node):
            if node in dp:
                return dp[node]
            if not node.left and (not node.right):
                dp[node], all[node] = (1, 0)
                return 1
            if node.left:
                all[node] += dfs(node.left)
            if node.right:
                all[node] += dfs(node.right)
            dp[node] = 1 + all[node.left] + all[node.right]
            all[node] = min(dp[node], dp[node.left] + dp[node.right])
            return dp[node]
        dfs(root)
        return all[root] if all[root] else dp[root]
<|end_body_0|>

<|body_start_1|>
        def dfs(node):
            if not node:
                return (0, 0, float('inf'))
            l0, l1, l2 = dfs(node.left)
            r0, r1, r2 = dfs(node.right)
            dp0 = l1 + r1
            dp1 = min(l2 + min(r1, r2), r2 + min(l1, l2))
            dp2 = 1 + min(l0, l1, l2) + min(r0, r1, r2)
            return (dp0, dp1, dp2)
        a, b, c = dfs(root)
        return min(b, c)
<|end_body_1|>

<|body_start_2|>
        ans, monitored = ([0], {None})

        def dfs(node, parent=None):
            if node:
                dfs(node.left, node)
                dfs(node.right, node)
                if parent is None and node not in monitored or node.left not in monitored or node.right not in monitored:
                    ans[0] += 1
                    monitored.update({node, parent, node.left, node.right})
        dfs(root)
        return ans[0]
<|end_body_2|>

Solution:
class Solution:
    def minCameraCover_wrong(self, root):
        """:type root: TreeNode :rtype: int"""
        dp, all = (collections.defaultdict(int), collections.defaultdict(int))

        def dfs(node):
            if node in dp:
                return dp[node]
            if not node.left and (not node.right):
                dp[node], all[node] = (1, 0)
                return 1
            if node.left:
                all[node] += dfs(node.left)
            if node.right:
                all[node] += dfs(node.right)
            dp[node] = 1 + all[node.left] + all[node.right]
            all[node] = min(dp[node], dp[node.left] + dp[node.right])
            return dp[node]
        dfs(root)
        return all[root] if all[root] else dp[root]

    def minCameraCover(self, root):
        """:type root: TreeNode :rtype: int"""
        def dfs(node):
            if not node:
                return (0, 0, float('inf'))
            l0, l1, l2 = dfs(node.left)
            r0, r1, r2 = dfs(node.right)
            dp0 = l1 + r1
            dp1 = min(l2 + min(r1, r2), r2 + min(l1, l2))
            dp2 = 1 + min(l0, l1, l2) + min(r0, r1, r2)
            return (dp0, dp1, dp2)
        a, b, c = dfs(root)
        return min(b, c)

    def minCameraCover2(self, root):
        """:type root: TreeNode :rtype: int"""
        ans, monitored = ([0], {None})

        def dfs(node, parent=None):
            if node:
                dfs(node.left, node)
                dfs(node.right, node)
                if parent is None and node not in monitored or node.left not in monitored or node.right not in monitored:
                    ans[0] += 1
                    monitored.update({node, parent, node.left, node.right})
        dfs(root)
        return ans[0]


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

Class description:
主窗口，继承了QMainWindow类

Method signatures and docstrings:
- def __init__(self, name, title): 初始化类的成员变量
- def init_ui(self, name, title): 初始化UI界面

Implement the Python class `MainWindow` described below.

Class description:
主窗口，继承了QMainWindow类

Method signatures and docstrings:
- def __init__(self, name, title): 初始化类的成员变量
- def init_ui(self, name, title): 初始化UI界面

<|skeleton|>
class MainWindow:
    """主窗口，继承了QMainWindow类"""

    def __init__(self, name, title):
        """初始化类的成员变量"""
        <|body_0|>

    def init_ui(self, name, title):
        """初始化UI界面"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(MainWindow, self).__init__()
        self.w = 0
        self.h = 0
        self.init_ui(name, title)
<|end_body_0|>

<|body_start_1|>
        self.w = 140
        self.h = 100
        self.setObjectName(name)
        self.setWindowTitle(title)
        self.resize(self.w, self.h)
        self.custom_edit = CustomEdit(self, size=(10, 10, 120, 24), name='custom_edit', search=False)
        self.custom_edit.setPlaceholderText('我是自定义输入框')
<|end_body_1|>

Solution:
class MainWindow:
    """主窗口，继承了QMainWindow类"""

    def __init__(self, name, title):
        """初始化类的成员变量"""
        super(MainWindow, self).__init__()
        self.w = 0
        self.h = 0
        self.init_ui(name, title)

    def init_ui(self, name, title):
        """初始化UI界面"""
        self.w = 140
        self.h = 100
        self.setObjectName(name)
        self.setWindowTitle(title)
        self.resize(self.w, self.h)
        self.custom_edit = CustomEdit(self, size=(10, 10, 120, 24), name='custom_edit', search=False)
        self.custom_edit.setPlaceholderText('我是自定义输入框')


predict

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

Class description:
Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer.

Method signatures and docstrings:
- def addPreAttentionLayer(self, merged_input): Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first dimension corresponds to the batch size, the second to the sequence timesteps and the last one to the concatenation of features. Retruns: 3-dimensional Tensor of the same dimension as merged_input
- def label_and_attention(self, model, input_): Classifies the sequences in input_ and returns the attention score. Args: model: a Keras model input_: a list of array representation of sentences. Returns: A tuple where the first element is the attention scores for each sentence, and the second is the model predictions.
- def model_predict(self, model, sentences, return_attention=False, batch_size=32): Model probability distribution over set of labels for sentences. Args: model: a Keras model.
- def predict(self, sentences, return_attention=False, translate_labels=True): Distribution over labels for sentences given by all models. Args: sentences: return_attention: if True, return the attention scores for every prediction. translate_labels: if True, replace the numeric value of the labels using the dataset mappings.

Implement the Python class `TimePreAttArgBiLSTM` described below.

Class description:
Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer.

Method signatures and docstrings:
- def addPreAttentionLayer(self, merged_input): Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first dimension corresponds to the batch size, the second to the sequence timesteps and the last one to the concatenation of features. Retruns: 3-dimensional Tensor of the same dimension as merged_input
- def label_and_attention(self, model, input_): Classifies the sequences in input_ and returns the attention score. Args: model: a Keras model input_: a list of array representation of sentences. Returns: A tuple where the first element is the attention scores for each sentence, and the second is the model predictions.
- def model_predict(self, model, sentences, return_attention=False, batch_size=32): Model probability distribution over set of labels for sentences. Args: model: a Keras model.
- def predict(self, sentences, return_attention=False, translate_labels=True): Distribution over labels for sentences given by all models. Args: sentences: return_attention: if True, return the attention scores for every prediction. translate_labels: if True, replace the numeric value of the labels using the dataset mappings.

<|skeleton|>
class TimePreAttArgBiLSTM:
    """Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer."""

    def addPreAttentionLayer(self, merged_input):
        """Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first dimension corresponds to the batch size, the second to the sequence timesteps and the last one to the concatenation of features. Retruns: 3-dimensional Tensor of the same dimension as merged_input"""
        <|body_0|>

    def label_and_attention(self, model, input_):
        """Classifies the sequences in input_ and returns the attention score. Args: model: a Keras model input_: a list of array representation of sentences. Returns: A tuple where the first element is the attention scores for each sentence, and the second is the model predictions."""
        <|body_1|>

    def model_predict(self, model, sentences, return_attention=False, batch_size=32):
        """Model probability distribution over set of labels for sentences. Args: model: a Keras model."""
        <|body_2|>

    def predict(self, sentences, return_attention=False, translate_labels=True):
        """Distribution over labels for sentences given by all models. Args: sentences: return_attention: if True, return the attention scores for every prediction. translate_labels: if True, replace the numeric value of the labels using the dataset mappings."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        activation = self.params.get('attentionActivation', None)
        if activation == 'None':
            activation = None
        feature_vector_size = K.int_shape(merged_input)[-1]
        att_layer = layers.TimeDistributed(layers.Dense(feature_vector_size, activation=activation), name='attention_matrix_score')(merged_input)
        att_layer = layers.Lambda(lambda x: K.mean(x, axis=2), name='attention_vector_score')(att_layer)
        att_layer = layers.Permute((2, 1))(layers.RepeatVector(feature_vector_size)(att_layer))
        merged_input = layers.multiply([att_layer, merged_input])
        return merged_input
<|end_body_0|>

<|body_start_1|>
        layer = model.get_layer('attention_vector_score')
        attention_model = Model(inputs=model.input, outputs=[layer.output, model.output])
        return attention_model.predict(input_)
<|end_body_1|>

<|body_start_2|>
        pred_labels = []
        att_scores = []
        sentenceLengths = self.getSentenceLengths(sentences)
        for start in range(0, len(sentences), batch_size):
            end = start + batch_size
            instances = []
            for feature_name in self.params['featureNames']:
                input_data = pad_sequences([numpy.asarray(instance[feature_name]) for instance in sentences[start:end]], self.max_sentece_length)
                instances.append(input_data)
            if not return_attention:
                predictions = model.predict(instances, verbose=False)
            else:
                attention, predictions = self.label_and_attention(model, instances)
            predictions = predictions.argmax(axis=-1)
            for index, (pred, sentence) in enumerate(zip(predictions, sentences[start:end])):
                sentence_len = len(sentence[feature_name])
                pred_labels.append(pred[-sentence_len:])
                if return_attention:
                    att_scores.append(attention[index, -sentence_len:])
        return (numpy.asarray(pred_labels), numpy.asarray(att_scores))
<|end_body_2|>

<|body_start_3|>
        if 'characters' in self.params['featureNames']:
            self.padCharacters(sentences)
        labels = {}
        attention = {}
        for model_name, model in self.models.items():
            padded_pred_labels, padded_att_scores = self.model_predict(model, sentences, return_attention)
            pred_labels = []
            att_scores = []
            for index, (padded_pred, sentence) in enumerate(zip(padded_pred_labels, sentences)):
                no_pad_tokens = numpy.where(numpy.asarray(sentence['tokens']))[0]
                if no_pad_tokens.max() > padded_pred.shape[0]:
                    missing = no_pad_tokens.max() - padded_pred.shape[0]
                    pred_labels.append(numpy.pad(padded_pred, (0, missing), 'constant'))
                    if return_attention:
                        att_scores.append(numpy.pad(padded_att_scores[index], (0, missing), 'constant'))
                else:
                    pred_labels.append(padded_pred[no_pad_tokens])
                    if return_attention:
                        att_scores.append(padded_att_scores[index][no_pad_tokens])
            attention[model_name] = att_scores
            if not translate_labels:
                labels[model_name] = pred_labels
                continue
            idx2Label = self.idx2Labels[model_name]
            labels[model_name] = [[idx2Label[tag] for tag in tagSentence] for tagSentence in pred_labels]
        if return_attention:
            return (labels, attention)
        return labels
<|end_body_3|>

Solution:
class TimePreAttArgBiLSTM:
    """Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer."""

    def addPreAttentionLayer(self, merged_input):
        """Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first dimension corresponds to the batch size, the second to the sequence timesteps and the last one to the concatenation of features. Retruns: 3-dimensional Tensor of the same dimension as merged_input"""
        activation = self.params.get('attentionActivation', None)
        if activation == 'None':
            activation = None
        feature_vector_size = K.int_shape(merged_input)[-1]
        att_layer = layers.TimeDistributed(layers.Dense(feature_vector_size, activation=activation), name='attention_matrix_score')(merged_input)
        att_layer = layers.Lambda(lambda x: K.mean(x, axis=2), name='attention_vector_score')(att_layer)
        att_layer = layers.Permute((2, 1))(layers.RepeatVector(feature_vector_size)(att_layer))
        merged_input = layers.multiply([att_layer, merged_input])
        return merged_input

    def label_and_attention(self, model, input_):
        """Classifies the sequences in input_ and returns the attention score. Args: model: a Keras model input_: a list of array representation of sentences. Returns: A tuple where the first element is the attention scores for each sentence, and the second is the model predictions."""
        layer = model.get_layer('attention_vector_score')
        attention_model = Model(inputs=model.input, outputs=[layer.output, model.output])
        return attention_model.predict(input_)

    def model_predict(self, model, sentences, return_attention=False, batch_size=32):
        """Model probability distribution over set of labels for sentences. Args: model: a Keras model."""
        pred_labels = []
        att_scores = []
        sentenceLengths = self.getSentenceLengths(sentences)
        for start in range(0, len(sentences), batch_size):
            end = start + batch_size
            instances = []
            for feature_name in self.params['featureNames']:
                input_data = pad_sequences([numpy.asarray(instance[feature_name]) for instance in sentences[start:end]], self.max_sentece_length)
                instances.append(input_data)
            if not return_attention:
                predictions = model.predict(instances, verbose=False)
            else:
                attention, predictions = self.label_and_attention(model, instances)
            predictions = predictions.argmax(axis=-1)
            for index, (pred, sentence) in enumerate(zip(predictions, sentences[start:end])):
                sentence_len = len(sentence[feature_name])
                pred_labels.append(pred[-sentence_len:])
                if return_attention:
                    att_scores.append(attention[index, -sentence_len:])
        return (numpy.asarray(pred_labels), numpy.asarray(att_scores))

    def predict(self, sentences, return_attention=False, translate_labels=True):
        """Distribution over labels for sentences given by all models. Args: sentences: return_attention: if True, return the attention scores for every prediction. translate_labels: if True, replace the numeric value of the labels using the dataset mappings."""
        if 'characters' in self.params['featureNames']:
            self.padCharacters(sentences)
        labels = {}
        attention = {}
        for model_name, model in self.models.items():
            padded_pred_labels, padded_att_scores = self.model_predict(model, sentences, return_attention)
            pred_labels = []
            att_scores = []
            for index, (padded_pred, sentence) in enumerate(zip(padded_pred_labels, sentences)):
                no_pad_tokens = numpy.where(numpy.asarray(sentence['tokens']))[0]
                if no_pad_tokens.max() > padded_pred.shape[0]:
                    missing = no_pad_tokens.max() - padded_pred.shape[0]
                    pred_labels.append(numpy.pad(padded_pred, (0, missing), 'constant'))
                    if return_attention:
                        att_scores.append(numpy.pad(padded_att_scores[index], (0, missing), 'constant'))
                else:
                    pred_labels.append(padded_pred[no_pad_tokens])
                    if return_attention:
                        att_scores.append(padded_att_scores[index][no_pad_tokens])
            attention[model_name] = att_scores
            if not translate_labels:
                labels[model_name] = pred_labels
                continue
            idx2Label = self.idx2Labels[model_name]
            labels[model_name] = [[idx2Label[tag] for tag in tagSentence] for tagSentence in pred_labels]
        if return_attention:
            return (labels, attention)
        return labels


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

Class description:
A bot to Commons Category sitelinks

Method signatures and docstrings:
- def __init__(self, generator): Arguments: * generator - A generator that yields ItemPage objects.
- def run(self): Starts the robot.

Implement the Python class `MissingCommonsSitelinkBot` described below.

Class description:
A bot to Commons Category sitelinks

Method signatures and docstrings:
- def __init__(self, generator): Arguments: * generator - A generator that yields ItemPage objects.
- def run(self): Starts the robot.

<|skeleton|>
class MissingCommonsSitelinkBot:
    """A bot to Commons Category sitelinks"""

    def __init__(self, generator):
        """Arguments: * generator - A generator that yields ItemPage objects."""
        <|body_0|>

    def run(self):
        """Starts the robot."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.generator = generator
        self.commons = pywikibot.Site('commons', 'commons')
        self.repo = pywikibot.Site().data_repository()
<|end_body_0|>

<|body_start_1|>
        for itempage in self.generator:
            pywikibot.output(u'Working on %s' % (itempage.title(),))
            if not itempage.exists():
                pywikibot.output(u'Item does not exist, skipping')
                continue
            if itempage.isRedirectPage():
                itempage = itempage.getRedirectTarget()
            data = itempage.get()
            claims = data.get('claims')
            sitelinks = data.get('sitelinks')
            if u'P910' in claims:
                pywikibot.output(u"Item has topic's main category (P910), skipping")
                continue
            if u'commonswiki' in sitelinks:
                pywikibot.output(u'Item already has a sitelink to Commons, skipping')
                continue
            if u'P373' not in claims:
                pywikibot.output(u'Item seems to be missing Commons category (P373), skipping')
                continue
            commonscategorytitle = claims.get('P373')[0].getTarget()
            commonscategory = pywikibot.Category(self.commons, title=commonscategorytitle)
            if not commonscategory.exists():
                pywikibot.output(u'Commons category %s does not exist, skipping' % (commonscategory.title(),))
                continue
            summary = 'Add sitelink to %s based on Commons category (P373)' % commonscategory.title(asLink=True, insite=self.repo)
            pywikibot.output(summary)
            try:
                itempage.setSitelink(commonscategory, summary=summary)
            except pywikibot.exceptions.OtherPageSaveError:
                pywikibot.output(u'Item save failed, probably a conflicting sitelink, skipping')
<|end_body_1|>

Solution:
class MissingCommonsSitelinkBot:
    """A bot to Commons Category sitelinks"""

    def __init__(self, generator):
        """Arguments: * generator - A generator that yields ItemPage objects."""
        self.generator = generator
        self.commons = pywikibot.Site('commons', 'commons')
        self.repo = pywikibot.Site().data_repository()

    def run(self):
        """Starts the robot."""
        for itempage in self.generator:
            pywikibot.output(u'Working on %s' % (itempage.title(),))
            if not itempage.exists():
                pywikibot.output(u'Item does not exist, skipping')
                continue
            if itempage.isRedirectPage():
                itempage = itempage.getRedirectTarget()
            data = itempage.get()
            claims = data.get('claims')
            sitelinks = data.get('sitelinks')
            if u'P910' in claims:
                pywikibot.output(u"Item has topic's main category (P910), skipping")
                continue
            if u'commonswiki' in sitelinks:
                pywikibot.output(u'Item already has a sitelink to Commons, skipping')
                continue
            if u'P373' not in claims:
                pywikibot.output(u'Item seems to be missing Commons category (P373), skipping')
                continue
            commonscategorytitle = claims.get('P373')[0].getTarget()
            commonscategory = pywikibot.Category(self.commons, title=commonscategorytitle)
            if not commonscategory.exists():
                pywikibot.output(u'Commons category %s does not exist, skipping' % (commonscategory.title(),))
                continue
            summary = 'Add sitelink to %s based on Commons category (P373)' % commonscategory.title(asLink=True, insite=self.repo)
            pywikibot.output(summary)
            try:
                itempage.setSitelink(commonscategory, summary=summary)
            except pywikibot.exceptions.OtherPageSaveError:
                pywikibot.output(u'Item save failed, probably a conflicting sitelink, skipping')


longestPalindrome

中级的动态规划——二维关系 时间复杂度 O(n^2) 空间复杂度O(n^2)

中心扩展的方法 时间复杂度 O(n^2) 空间复杂度O(1)

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: 中级的动态规划——二维关系 时间复杂度 O(n^2) 空间复杂度O(n^2)
- def longestPalindrome1(self, s: str) -> str: 中心扩展的方法 时间复杂度 O(n^2) 空间复杂度O(1)

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: 中级的动态规划——二维关系 时间复杂度 O(n^2) 空间复杂度O(n^2)
- def longestPalindrome1(self, s: str) -> str: 中心扩展的方法 时间复杂度 O(n^2) 空间复杂度O(1)

<|skeleton|>
class Solution:

    def longestPalindrome(self, s: str) -> str:
        """中级的动态规划——二维关系 时间复杂度 O(n^2) 空间复杂度O(n^2)"""
        <|body_0|>

    def longestPalindrome1(self, s: str) -> str:
        """中心扩展的方法 时间复杂度 O(n^2) 空间复杂度O(1)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n = len(s)
        if n == 0:
            return s
        dp = [[0] * n for i in range(n)]
        left = 0
        right = 0
        for i in range(n - 2, -1, -1):
            dp[i][i] = 1
            for j in range(i + 1, n):
                dp[i][j] = s[i] == s[j] and (j - i < 3 or dp[i + 1][j - 1])
                if dp[i][j] and right - left < j - i:
                    left = i
                    right = j
        return s[left:right + 1]
<|end_body_0|>

<|body_start_1|>
        n = len(s)
        if n <= 1:
            return s
        for i in range(1, n - 2):
            p1 = i
<|end_body_1|>

Solution:
class Solution:
    def longestPalindrome(self, s: str) -> str:
        """中级的动态规划——二维关系 时间复杂度 O(n^2) 空间复杂度O(n^2)"""
        n = len(s)
        if n == 0:
            return s
        dp = [[0] * n for i in range(n)]
        left = 0
        right = 0
        for i in range(n - 2, -1, -1):
            dp[i][i] = 1
            for j in range(i + 1, n):
                dp[i][j] = s[i] == s[j] and (j - i < 3 or dp[i + 1][j - 1])
                if dp[i][j] and right - left < j - i:
                    left = i
                    right = j
        return s[left:right + 1]

    def longestPalindrome1(self, s: str) -> str:
        """中心扩展的方法 时间复杂度 O(n^2) 空间复杂度O(1)"""
        n = len(s)
        if n <= 1:
            return s
        for i in range(1, n - 2):
            p1 = i


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def canWin(self, s): :type s: str :rtype: bool
- def firstMissingPositive(self, nums): :type nums: List[int] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def canWin(self, s): :type s: str :rtype: bool
- def firstMissingPositive(self, nums): :type nums: List[int] :rtype: int

<|skeleton|>
class Solution:

    def canWin(self, s):
        """:type s: str :rtype: bool"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        number = []
        now = 0
        for i in s:
            if i == '+':
                now += 1
            elif now > 1:
                number.append(now)
                now = 0
            else:
                now = 0
        if now > 1:
            number.append(now)
        if not number:
            return False
        n = max(number)
        print(number)
        table = [0, 0, 1, 1]
        for i in range(4, n + 1):
            nums = []
            for j in range((i + 1) // 2):
                nums.append(table[j] ^ table[i - j - 2])
            table.append(self.firstMissingPositive(nums))
        result = 0
        print(table)
        for i in number:
            result ^= table[i]
        if result == 0:
            return False
        else:
            return True
<|end_body_0|>

<|body_start_1|>
        if not nums:
            return 1
        index = 0
        n = len(nums)
        while index < n:
            if nums[index] >= 0 and nums[index] < n and (nums[index] != index) and (nums[nums[index]] != nums[index]):
                nums[nums[index]], nums[index] = (nums[index], nums[nums[index]])
            else:
                index += 1
        for i, v in enumerate(nums):
            if i != v:
                return i
        return n
<|end_body_1|>

Solution:
class Solution:
    def canWin(self, s):
        """:type s: str :rtype: bool"""
        number = []
        now = 0
        for i in s:
            if i == '+':
                now += 1
            elif now > 1:
                number.append(now)
                now = 0
            else:
                now = 0
        if now > 1:
            number.append(now)
        if not number:
            return False
        n = max(number)
        print(number)
        table = [0, 0, 1, 1]
        for i in range(4, n + 1):
            nums = []
            for j in range((i + 1) // 2):
                nums.append(table[j] ^ table[i - j - 2])
            table.append(self.firstMissingPositive(nums))
        result = 0
        print(table)
        for i in number:
            result ^= table[i]
        if result == 0:
            return False
        else:
            return True

    def firstMissingPositive(self, nums):
        """:type nums: List[int] :rtype: int"""
        if not nums:
            return 1
        index = 0
        n = len(nums)
        while index < n:
            if nums[index] >= 0 and nums[index] < n and (nums[index] != index) and (nums[nums[index]] != nums[index]):
                nums[nums[index]], nums[index] = (nums[index], nums[nums[index]])
            else:
                index += 1
        for i, v in enumerate(nums):
            if i != v:
                return i
        return n


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

Class description:
Implement the EnsembleExperiment class.

Method signatures and docstrings:
- def __init__(self, args, logger, on_episode_end=None, log_start_t=0): LeaguePlay performs training of a single multi-agent and offers loading of new adversarial agents. :param args: :param logger: :param on_episode_end:
- def load_ensemble(self, native: OrderedDict, foreign: OrderedDict): Build an dual ensemble where parts of the native agent infer with the foreign agent :param native: state dict of a foreign network :param foreign: state dict of the home network :return:

Implement the Python class `EnsembleExperiment` described below.

Class description:
Implement the EnsembleExperiment class.

Method signatures and docstrings:
- def __init__(self, args, logger, on_episode_end=None, log_start_t=0): LeaguePlay performs training of a single multi-agent and offers loading of new adversarial agents. :param args: :param logger: :param on_episode_end:
- def load_ensemble(self, native: OrderedDict, foreign: OrderedDict): Build an dual ensemble where parts of the native agent infer with the foreign agent :param native: state dict of a foreign network :param foreign: state dict of the home network :return:

<|skeleton|>
class EnsembleExperiment:

    def __init__(self, args, logger, on_episode_end=None, log_start_t=0):
        """LeaguePlay performs training of a single multi-agent and offers loading of new adversarial agents. :param args: :param logger: :param on_episode_end:"""
        <|body_0|>

    def load_ensemble(self, native: OrderedDict, foreign: OrderedDict):
        """Build an dual ensemble where parts of the native agent infer with the foreign agent :param native: state dict of a foreign network :param foreign: state dict of the home network :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(args, logger, on_episode_end, log_start_t)
        assert isinstance(self.home_mac, EnsembleMAC), 'Ensemble experiment enforces "mac"=ensemble in configuration'
        self.home_mac: EnsembleMAC = self.home_mac
<|end_body_0|>

<|body_start_1|>
        self.home_mac.load_state_dict(agent=native)
        self.home_mac.load_state_dict(ensemble={0: foreign})
        self.home_learner.build_optimizer()
        self.home_learner.update_targets()
<|end_body_1|>

Solution:
class EnsembleExperiment:
    def __init__(self, args, logger, on_episode_end=None, log_start_t=0):
        """LeaguePlay performs training of a single multi-agent and offers loading of new adversarial agents. :param args: :param logger: :param on_episode_end:"""
        super().__init__(args, logger, on_episode_end, log_start_t)
        assert isinstance(self.home_mac, EnsembleMAC), 'Ensemble experiment enforces "mac"=ensemble in configuration'
        self.home_mac: EnsembleMAC = self.home_mac

    def load_ensemble(self, native: OrderedDict, foreign: OrderedDict):
        """Build an dual ensemble where parts of the native agent infer with the foreign agent :param native: state dict of a foreign network :param foreign: state dict of the home network :return:"""
        self.home_mac.load_state_dict(agent=native)
        self.home_mac.load_state_dict(ensemble={0: foreign})
        self.home_learner.build_optimizer()
        self.home_learner.update_targets()


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

Class description:
Implement the TestStringMethods class.

Method signatures and docstrings:
- def test_getArray_success_with_valid_values(self): This is testing for normal files
- def test_getArray_error_with_not_existing_file(self): This is test for Non Existing file
- def test_getArray_error_with_empty_value(self): This is for testing empty files
- def test_getArray_error_inlude_strings(self): This is for testing array including strings
- def test_getArray_success_contain_blanks(self): This is for testing blank lines
- def test_sort_success_return_sortedArray(self): This is the test for getting sortedArray

Implement the Python class `TestStringMethods` described below.

Class description:
Implement the TestStringMethods class.

Method signatures and docstrings:
- def test_getArray_success_with_valid_values(self): This is testing for normal files
- def test_getArray_error_with_not_existing_file(self): This is test for Non Existing file
- def test_getArray_error_with_empty_value(self): This is for testing empty files
- def test_getArray_error_inlude_strings(self): This is for testing array including strings
- def test_getArray_success_contain_blanks(self): This is for testing blank lines
- def test_sort_success_return_sortedArray(self): This is the test for getting sortedArray

<|skeleton|>
class TestStringMethods:

    def test_getArray_success_with_valid_values(self):
        """This is testing for normal files"""
        <|body_0|>

    def test_getArray_error_with_not_existing_file(self):
        """This is test for Non Existing file"""
        <|body_1|>

    def test_getArray_error_with_empty_value(self):
        """This is for testing empty files"""
        <|body_2|>

    def test_getArray_error_inlude_strings(self):
        """This is for testing array including strings"""
        <|body_3|>

    def test_getArray_success_contain_blanks(self):
        """This is for testing blank lines"""
        <|body_4|>

    def test_sort_success_return_sortedArray(self):
        """This is the test for getting sortedArray"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        fileName = '10Lines'
        expectedResult = [12.0, 13.5, 1.0, 5.5, 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(expectedResult, actuatlResponse)
<|end_body_0|>

<|body_start_1|>
        fileName = '10Lines1'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)
<|end_body_1|>

<|body_start_2|>
        fileName = 'empty'
        expectedResult = '\n The file is empty \n'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)
<|end_body_2|>

<|body_start_3|>
        fileName = '10ContStrings'
        expectedResult = '\n The Lines contain Strings \n'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)
<|end_body_3|>

<|body_start_4|>
        fileName = '10ContBlanks'
        expectedResult = [2.0, 3.4, 5.9, 6.5, 12.0, 13.0]
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(expectedResult, actuatlResponse)
<|end_body_4|>

<|body_start_5|>
        unsortedArray = [12.0, 13.5, 1.0, 5.5, 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]
        expectedResult = [1.0, 5.0, 5.5, 9.0, 12.0, 12.0, 13.5, 19.5, 23.5, 51.0]
        actuatlResponse = PSPQuickSortProcess.sort(unsortedArray)
        self.assertEqual(expectedResult, actuatlResponse)
<|end_body_5|>

Solution:
class TestStringMethods:
    def test_getArray_success_with_valid_values(self):
        """This is testing for normal files"""
        fileName = '10Lines'
        expectedResult = [12.0, 13.5, 1.0, 5.5, 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(expectedResult, actuatlResponse)

    def test_getArray_error_with_not_existing_file(self):
        """This is test for Non Existing file"""
        fileName = '10Lines1'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)

    def test_getArray_error_with_empty_value(self):
        """This is for testing empty files"""
        fileName = 'empty'
        expectedResult = '\n The file is empty \n'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)

    def test_getArray_error_inlude_strings(self):
        """This is for testing array including strings"""
        fileName = '10ContStrings'
        expectedResult = '\n The Lines contain Strings \n'
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(actuatlResponse)

    def test_getArray_success_contain_blanks(self):
        """This is for testing blank lines"""
        fileName = '10ContBlanks'
        expectedResult = [2.0, 3.4, 5.9, 6.5, 12.0, 13.0]
        actuatlResponse = PSPQuickSortInput.getArray(fileName)
        self.assertTrue(expectedResult, actuatlResponse)

    def test_sort_success_return_sortedArray(self):
        """This is the test for getting sortedArray"""
        unsortedArray = [12.0, 13.5, 1.0, 5.5, 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]
        expectedResult = [1.0, 5.0, 5.5, 9.0, 12.0, 12.0, 13.5, 19.5, 23.5, 51.0]
        actuatlResponse = PSPQuickSortProcess.sort(unsortedArray)
        self.assertEqual(expectedResult, actuatlResponse)


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

Class description:
Task that generates memory from the corpus using an encoder.

Method signatures and docstrings:
- def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]: Creates task prediction function for inference.
- def make_preprocess_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]: Produces function to preprocess samples. See BaseTask. Here we add a text identifier hash to the standard MentionEncoderTask preprocessing pipeline. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses samples to be usable for the model (mod casting from tf to jnp dtype).
- def make_collater_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]: Produces function to preprocess batches. See BaseTask. Batches text identifiers after standard mention preprocessing. Also masks out mentions that are too close to a passage boundary, and for which we may not have enough context to generate a meaningful encoding. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses batches to be usable for the model (mod casting from tf to jnp dtype).
- def dummy_input(config: ml_collections.ConfigDict) -> Dict[str, Any]: Produces model-specific dummy input batch. See BaseTask.
- def load_weights(config: ml_collections.ConfigDict) -> Dict[str, Any]: Load model weights.

Implement the Python class `MemoryGenerationTask` described below.

Class description:
Task that generates memory from the corpus using an encoder.

Method signatures and docstrings:
- def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]: Creates task prediction function for inference.
- def make_preprocess_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]: Produces function to preprocess samples. See BaseTask. Here we add a text identifier hash to the standard MentionEncoderTask preprocessing pipeline. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses samples to be usable for the model (mod casting from tf to jnp dtype).
- def make_collater_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]: Produces function to preprocess batches. See BaseTask. Batches text identifiers after standard mention preprocessing. Also masks out mentions that are too close to a passage boundary, and for which we may not have enough context to generate a meaningful encoding. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses batches to be usable for the model (mod casting from tf to jnp dtype).
- def dummy_input(config: ml_collections.ConfigDict) -> Dict[str, Any]: Produces model-specific dummy input batch. See BaseTask.
- def load_weights(config: ml_collections.ConfigDict) -> Dict[str, Any]: Load model weights.

<|skeleton|>
class MemoryGenerationTask:
    """Task that generates memory from the corpus using an encoder."""

    def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]:
        """Creates task prediction function for inference."""
        <|body_0|>

    def make_preprocess_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]:
        """Produces function to preprocess samples. See BaseTask. Here we add a text identifier hash to the standard MentionEncoderTask preprocessing pipeline. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses samples to be usable for the model (mod casting from tf to jnp dtype)."""
        <|body_1|>

    def make_collater_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]:
        """Produces function to preprocess batches. See BaseTask. Batches text identifiers after standard mention preprocessing. Also masks out mentions that are too close to a passage boundary, and for which we may not have enough context to generate a meaningful encoding. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses batches to be usable for the model (mod casting from tf to jnp dtype)."""
        <|body_2|>

    def dummy_input(config: ml_collections.ConfigDict) -> Dict[str, Any]:
        """Produces model-specific dummy input batch. See BaseTask."""
        <|body_3|>

    def load_weights(config: ml_collections.ConfigDict) -> Dict[str, Any]:
        """Load model weights."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        def predict_fn(model_config: ml_collections.FrozenConfigDict, model_params: Dict[str, Any], model_vars: Dict[str, Any], batch: Dict[str, Any]) -> Dict[str, Array]:
            """Model-specific prediction function.

              Args:
                model_config: contains model config hyperparameters.
                model_params: contains model parameters.
                model_vars: contains model variables (not optimized).
                batch: model input.

              Returns:
                Dict[str, Array]. predictions.
              """
            variable_dict = {'params': model_params}
            variable_dict.update(model_vars)
            loss_helpers, _ = cls.build_model(model_config).apply(variable_dict, batch, deterministic=True, rngs=None)
            return loss_helpers['memory_generation']
        return predict_fn
<|end_body_0|>

<|body_start_1|>
        max_length = config.model_config.encoder_config.max_length
        mention_preprocessing_fn = mention_encoder_task.MentionEncoderTask.make_preprocess_fn(config)

        def preprocess_fn(example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
            """Performs preprocessing for individual sample."""
            new_example = mention_preprocessing_fn(example)
            new_example['text_identifiers'] = mention_preprocess_utils.text_hash_tf(example['text_ids'], max_length)
            return new_example
        return preprocess_fn
<|end_body_1|>

<|body_start_2|>
        mention_collater_fn = mention_encoder_task.MentionEncoderTask.make_collater_fn(config)
        min_distance_from_passage_boundary = config.min_distance_from_passage_boundary
        bsz = config.per_device_batch_size
        max_mentions_per_sample = config.max_mentions_per_sample

        def collater_fn(batch: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
            new_batch = mention_collater_fn(batch)
            new_batch['target_text_identifiers'] = tf.gather(new_batch['text_identifiers'], new_batch['mention_target_batch_positions'])
            new_batch['target_mention_hashes'] = mention_preprocess_utils.modified_cantor_pairing(new_batch['mention_target_start_positions'], new_batch['target_text_identifiers'])
            seq_len = tf.shape(batch['text_ids'])[1]
            starts_far_from_passage_boundary = tf.greater_equal(new_batch['mention_target_start_positions'], min_distance_from_passage_boundary)
            ends_far_from_passage_boundary = tf.less(new_batch['mention_target_end_positions'], tf.cast(seq_len, new_batch['mention_target_end_positions'].dtype) - min_distance_from_passage_boundary)
            far_from_passage_boundary = tf.logical_and(starts_far_from_passage_boundary, ends_far_from_passage_boundary)
            far_from_passage_boundary = tf.cast(far_from_passage_boundary, dtype=new_batch['mention_target_weights'].dtype)
            new_batch['mention_target_weights'] = new_batch['mention_target_weights'] * far_from_passage_boundary
            unique_mention_ids = []
            dense_mention_ids = batch['dense_mention_ids'] * batch['dense_mention_mask']
            for i in range(bsz):
                unique_mention_ids_per_i = tf.unique(dense_mention_ids[i]).y
                unique_mention_ids_per_i = tf.cast(unique_mention_ids_per_i, tf.int32)
                unique_mention_ids_per_i = mention_preprocess_utils.dynamic_padding_1d(unique_mention_ids_per_i, max_mentions_per_sample)
                unique_mention_ids.append(unique_mention_ids_per_i)
            new_batch['unique_mention_ids'] = tf.stack(unique_mention_ids)
            return new_batch
        return collater_fn
<|end_body_2|>

<|body_start_3|>
        dummy_input = mention_encoder_task.MentionEncoderTask.dummy_input(config)
        mention_position_shape = config.max_mentions * config.per_device_batch_size
        int_type = jnp.int32
        dummy_input['target_text_identifiers'] = jnp.ones(mention_position_shape, int_type)
        dummy_input['target_mention_hashes'] = jnp.ones(mention_position_shape, int_type)
        dummy_input['unique_mention_ids'] = jnp.ones((config.per_device_batch_size, config.max_mentions_per_sample), int_type)
        return dummy_input
<|end_body_3|>

<|body_start_4|>
        encoder_name = config.model_config.encoder_name
        encoder_class = encoder_registry.get_registered_encoder(encoder_name)
        encoder_variables = encoder_class.load_weights(config)
        model_variables = {}
        for group_key in encoder_variables:
            model_variables[group_key] = {'encoder': encoder_variables[group_key]}
        return model_variables
<|end_body_4|>

Solution:
class MemoryGenerationTask:
    """Task that generates memory from the corpus using an encoder."""

    def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]:
        """Creates task prediction function for inference."""
        def predict_fn(model_config: ml_collections.FrozenConfigDict, model_params: Dict[str, Any], model_vars: Dict[str, Any], batch: Dict[str, Any]) -> Dict[str, Array]:
            """Model-specific prediction function.

              Args:
                model_config: contains model config hyperparameters.
                model_params: contains model parameters.
                model_vars: contains model variables (not optimized).
                batch: model input.

              Returns:
                Dict[str, Array]. predictions.
              """
            variable_dict = {'params': model_params}
            variable_dict.update(model_vars)
            loss_helpers, _ = cls.build_model(model_config).apply(variable_dict, batch, deterministic=True, rngs=None)
            return loss_helpers['memory_generation']
        return predict_fn

    def make_preprocess_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]:
        """Produces function to preprocess samples. See BaseTask. Here we add a text identifier hash to the standard MentionEncoderTask preprocessing pipeline. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses samples to be usable for the model (mod casting from tf to jnp dtype)."""
        max_length = config.model_config.encoder_config.max_length
        mention_preprocessing_fn = mention_encoder_task.MentionEncoderTask.make_preprocess_fn(config)

        def preprocess_fn(example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
            """Performs preprocessing for individual sample."""
            new_example = mention_preprocessing_fn(example)
            new_example['text_identifiers'] = mention_preprocess_utils.text_hash_tf(example['text_ids'], max_length)
            return new_example
        return preprocess_fn

    def make_collater_fn(config: ml_collections.ConfigDict) -> Callable[[Dict[str, tf.Tensor]], Dict[str, tf.Tensor]]:
        """Produces function to preprocess batches. See BaseTask. Batches text identifiers after standard mention preprocessing. Also masks out mentions that are too close to a passage boundary, and for which we may not have enough context to generate a meaningful encoding. Args: config: contains experiment hyperparameters. Returns: Function that preprocesses batches to be usable for the model (mod casting from tf to jnp dtype)."""
        mention_collater_fn = mention_encoder_task.MentionEncoderTask.make_collater_fn(config)
        min_distance_from_passage_boundary = config.min_distance_from_passage_boundary
        bsz = config.per_device_batch_size
        max_mentions_per_sample = config.max_mentions_per_sample

        def collater_fn(batch: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
            new_batch = mention_collater_fn(batch)
            new_batch['target_text_identifiers'] = tf.gather(new_batch['text_identifiers'], new_batch['mention_target_batch_positions'])
            new_batch['target_mention_hashes'] = mention_preprocess_utils.modified_cantor_pairing(new_batch['mention_target_start_positions'], new_batch['target_text_identifiers'])
            seq_len = tf.shape(batch['text_ids'])[1]
            starts_far_from_passage_boundary = tf.greater_equal(new_batch['mention_target_start_positions'], min_distance_from_passage_boundary)
            ends_far_from_passage_boundary = tf.less(new_batch['mention_target_end_positions'], tf.cast(seq_len, new_batch['mention_target_end_positions'].dtype) - min_distance_from_passage_boundary)
            far_from_passage_boundary = tf.logical_and(starts_far_from_passage_boundary, ends_far_from_passage_boundary)
            far_from_passage_boundary = tf.cast(far_from_passage_boundary, dtype=new_batch['mention_target_weights'].dtype)
            new_batch['mention_target_weights'] = new_batch['mention_target_weights'] * far_from_passage_boundary
            unique_mention_ids = []
            dense_mention_ids = batch['dense_mention_ids'] * batch['dense_mention_mask']
            for i in range(bsz):
                unique_mention_ids_per_i = tf.unique(dense_mention_ids[i]).y
                unique_mention_ids_per_i = tf.cast(unique_mention_ids_per_i, tf.int32)
                unique_mention_ids_per_i = mention_preprocess_utils.dynamic_padding_1d(unique_mention_ids_per_i, max_mentions_per_sample)
                unique_mention_ids.append(unique_mention_ids_per_i)
            new_batch['unique_mention_ids'] = tf.stack(unique_mention_ids)
            return new_batch
        return collater_fn

    def dummy_input(config: ml_collections.ConfigDict) -> Dict[str, Any]:
        """Produces model-specific dummy input batch. See BaseTask."""
        dummy_input = mention_encoder_task.MentionEncoderTask.dummy_input(config)
        mention_position_shape = config.max_mentions * config.per_device_batch_size
        int_type = jnp.int32
        dummy_input['target_text_identifiers'] = jnp.ones(mention_position_shape, int_type)
        dummy_input['target_mention_hashes'] = jnp.ones(mention_position_shape, int_type)
        dummy_input['unique_mention_ids'] = jnp.ones((config.per_device_batch_size, config.max_mentions_per_sample), int_type)
        return dummy_input

    def load_weights(config: ml_collections.ConfigDict) -> Dict[str, Any]:
        """Load model weights."""
        encoder_name = config.model_config.encoder_name
        encoder_class = encoder_registry.get_registered_encoder(encoder_name)
        encoder_variables = encoder_class.load_weights(config)
        model_variables = {}
        for group_key in encoder_variables:
            model_variables[group_key] = {'encoder': encoder_variables[group_key]}
        return model_variables


checkHash

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

Class description:
Implement the Hasher class.

Method signatures and docstrings:
- def makeHash(string): returns the hash value of string input
- def checkHash(string, candidate): string : hash value candidate : the input to check with hash value returns True if equal else False

Implement the Python class `Hasher` described below.

Class description:
Implement the Hasher class.

Method signatures and docstrings:
- def makeHash(string): returns the hash value of string input
- def checkHash(string, candidate): string : hash value candidate : the input to check with hash value returns True if equal else False

<|skeleton|>
class Hasher:

    def makeHash(string):
        """returns the hash value of string input"""
        <|body_0|>

    def checkHash(string, candidate):
        """string : hash value candidate : the input to check with hash value returns True if equal else False"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        hashed = hashlib.sha256(string.encode())
        hex_of_hashed = hashed.hexdigest()
        return hex_of_hashed
<|end_body_0|>

<|body_start_1|>
        hashed = hashlib.sha256(string.encode())
        hex_of_string = hashed.hexdigest()
        hashed = hashlib.sha256(candidate.encode())
        hex_of_candidate = hashed.hexdigest()
        return hex_of_string == hex_of_string
<|end_body_1|>

Solution:
class Hasher:
    def makeHash(string):
        """returns the hash value of string input"""
        hashed = hashlib.sha256(string.encode())
        hex_of_hashed = hashed.hexdigest()
        return hex_of_hashed

    def checkHash(string, candidate):
        """string : hash value candidate : the input to check with hash value returns True if equal else False"""
        hashed = hashlib.sha256(string.encode())
        hex_of_string = hashed.hexdigest()
        hashed = hashlib.sha256(candidate.encode())
        hex_of_candidate = hashed.hexdigest()
        return hex_of_string == hex_of_string


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

Class description:
Implement the Actions class.

Method signatures and docstrings:
- def notes_gui_toggle(): Toggle the notes gui
- def create_note(): Create a new note
- def delete_note(n: int): Delete note number n
- def show_note(n: int): Show note number n

Implement the Python class `Actions` described below.

Class description:
Implement the Actions class.

Method signatures and docstrings:
- def notes_gui_toggle(): Toggle the notes gui
- def create_note(): Create a new note
- def delete_note(n: int): Delete note number n
- def show_note(n: int): Show note number n

<|skeleton|>
class Actions:

    def notes_gui_toggle():
        """Toggle the notes gui"""
        <|body_0|>

    def create_note():
        """Create a new note"""
        <|body_1|>

    def delete_note(n: int):
        """Delete note number n"""
        <|body_2|>

    def show_note(n: int):
        """Show note number n"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if notes_gui.showing:
            ctx.tags = []
            notes_gui.hide()
        else:
            update_notes()
            ctx.tags = ['user.notes_showing']
            notes_gui.show()
<|end_body_0|>

<|body_start_1|>
        curtime = datetime.now().strftime('%Y-%m-%d %H%M%S')
        file_path = NOTES_DIR / f'{curtime}.txt'
        file_path.touch()
        subprocess.Popen(['notepad', str(file_path)])
        update_notes()
<|end_body_1|>

<|body_start_2|>
        global notes_by_number
        global notes_by_filename
        assert n <= len(notes_by_number)
        note = notes_by_number[n]
        archive_name = f'{note.path.stem} - {note.heading}.txt'
        shutil.move(note.path, ARCHIVE_DIR / archive_name)
        note.path.unlink()
        del notes_by_number[n]
        del notes_by_filename[note.path.name]
<|end_body_2|>

<|body_start_3|>
        global notes_by_number
        assert n <= len(notes_by_number)
        subprocess.Popen(['notepad', str(notes_by_number[n].path)])
<|end_body_3|>

Solution:
class Actions:
    def notes_gui_toggle():
        """Toggle the notes gui"""
        if notes_gui.showing:
            ctx.tags = []
            notes_gui.hide()
        else:
            update_notes()
            ctx.tags = ['user.notes_showing']
            notes_gui.show()

    def create_note():
        """Create a new note"""
        curtime = datetime.now().strftime('%Y-%m-%d %H%M%S')
        file_path = NOTES_DIR / f'{curtime}.txt'
        file_path.touch()
        subprocess.Popen(['notepad', str(file_path)])
        update_notes()

    def delete_note(n: int):
        """Delete note number n"""
        global notes_by_number
        global notes_by_filename
        assert n <= len(notes_by_number)
        note = notes_by_number[n]
        archive_name = f'{note.path.stem} - {note.heading}.txt'
        shutil.move(note.path, ARCHIVE_DIR / archive_name)
        note.path.unlink()
        del notes_by_number[n]
        del notes_by_filename[note.path.name]

    def show_note(n: int):
        """Show note number n"""
        global notes_by_number
        assert n <= len(notes_by_number)
        subprocess.Popen(['notepad', str(notes_by_number[n].path)])


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

Class description:
Implement the TestSetWindowPos class.

Method signatures and docstrings:
- def test_action_case_1(self): sentinel exsits in stack, but no secondaries
- def test_action_case_2(self): sentinel exsits in stack, plus one secondary
- def test_action_case_3(self): nothing in 1st dump, sentinel and secondary in upload_file_minidump_flash2 dump
- def test_action_case_4(self): nothing in 1st dump, sentinel but no secondary in upload_file_minidump_flash2 dump
- def test_action_case_5(self): nothing in either dump

Implement the Python class `TestSetWindowPos` described below.

Class description:
Implement the TestSetWindowPos class.

Method signatures and docstrings:
- def test_action_case_1(self): sentinel exsits in stack, but no secondaries
- def test_action_case_2(self): sentinel exsits in stack, plus one secondary
- def test_action_case_3(self): nothing in 1st dump, sentinel and secondary in upload_file_minidump_flash2 dump
- def test_action_case_4(self): nothing in 1st dump, sentinel but no secondary in upload_file_minidump_flash2 dump
- def test_action_case_5(self): nothing in either dump

<|skeleton|>
class TestSetWindowPos:

    def test_action_case_1(self):
        """sentinel exsits in stack, but no secondaries"""
        <|body_0|>

    def test_action_case_2(self):
        """sentinel exsits in stack, plus one secondary"""
        <|body_1|>

    def test_action_case_3(self):
        """nothing in 1st dump, sentinel and secondary in upload_file_minidump_flash2 dump"""
        <|body_2|>

    def test_action_case_4(self):
        """nothing in 1st dump, sentinel but no secondary in upload_file_minidump_flash2 dump"""
        <|body_3|>

    def test_action_case_5(self):
        """nothing in either dump"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        pc = DotDict()
        pc.process_type = 'plugin'
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.json_dump = pijd
        pc.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | other')
<|end_body_0|>

<|body_start_1|>
        pc = DotDict()
        pc.process_type = 'plugin'
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.json_dump = pijd
        pc.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        pc.json_dump['threads'][0]['frames'][4]['function'] = 'F_1378698112'
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | F_1378698112')
<|end_body_1|>

<|body_start_2|>
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][4]['function'] = 'F455544145'
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | F455544145')
<|end_body_2|>

<|body_start_3|>
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | other')
<|end_body_3|>

<|body_start_4|>
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertFalse(action_result)
        self.assertFalse('classifications' in pc)
<|end_body_4|>

Solution:
class TestSetWindowPos:
    def test_action_case_1(self):
        """sentinel exsits in stack, but no secondaries"""
        pc = DotDict()
        pc.process_type = 'plugin'
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.json_dump = pijd
        pc.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | other')

    def test_action_case_2(self):
        """sentinel exsits in stack, plus one secondary"""
        pc = DotDict()
        pc.process_type = 'plugin'
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.json_dump = pijd
        pc.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        pc.json_dump['threads'][0]['frames'][4]['function'] = 'F_1378698112'
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | F_1378698112')

    def test_action_case_3(self):
        """nothing in 1st dump, sentinel and secondary in upload_file_minidump_flash2 dump"""
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][4]['function'] = 'F455544145'
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | F455544145')

    def test_action_case_4(self):
        """nothing in 1st dump, sentinel but no secondary in upload_file_minidump_flash2 dump"""
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        pc.upload_file_minidump_flash2.json_dump['threads'][0]['frames'][2]['function'] = 'NtUserSetWindowPos'
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertTrue(action_result)
        self.assertTrue('classifications' in pc)
        self.assertTrue('skunk_works' in pc.classifications)
        self.assertEqual(pc.classifications.skunk_works.classification, 'NtUserSetWindowPos | other')

    def test_action_case_5(self):
        """nothing in either dump"""
        pc = DotDict()
        pc.dump = DotDict()
        pijd = copy.deepcopy(cannonical_json_dump)
        pc.dump.json_dump = pijd
        f2jd = copy.deepcopy(cannonical_json_dump)
        pc.upload_file_minidump_flash2 = DotDict()
        pc.upload_file_minidump_flash2.json_dump = f2jd
        fake_processor = create_basic_fake_processor()
        rc = DotDict()
        rule = SetWindowPos()
        action_result = rule.action(rc, pc, fake_processor)
        self.assertFalse(action_result)
        self.assertFalse('classifications' in pc)