getSubmitSmRespBill

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

Class description:
This is the bill for user to pay when sending a MT SMS

Method signatures and docstrings:
- def __init__(self, user): Defining billables
- def getSubmitSmRespBill(self): Will return a separate Bill for submit_sm_resp

Implement the Python class `SubmitSmBill` described below.

Class description:
This is the bill for user to pay when sending a MT SMS

Method signatures and docstrings:
- def __init__(self, user): Defining billables
- def getSubmitSmRespBill(self): Will return a separate Bill for submit_sm_resp

<|skeleton|>
class SubmitSmBill:
    """This is the bill for user to pay when sending a MT SMS"""

    def __init__(self, user):
        """Defining billables"""
        <|body_0|>

    def getSubmitSmRespBill(self):
        """Will return a separate Bill for submit_sm_resp"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Bill.__init__(self, user)
        self.amounts['submit_sm'] = 0.0
        self.amounts['submit_sm_resp'] = 0.0
        self.actions['decrement_submit_sm_count'] = 0
<|end_body_0|>

<|body_start_1|>
        bill = SubmitSmRespBill(self.user)
        bill.setAmount('submit_sm_resp', self.getAmount('submit_sm_resp'))
        return bill
<|end_body_1|>

Solution:
class SubmitSmBill:
    """This is the bill for user to pay when sending a MT SMS"""

    def __init__(self, user):
        """Defining billables"""
        Bill.__init__(self, user)
        self.amounts['submit_sm'] = 0.0
        self.amounts['submit_sm_resp'] = 0.0
        self.actions['decrement_submit_sm_count'] = 0

    def getSubmitSmRespBill(self):
        """Will return a separate Bill for submit_sm_resp"""
        bill = SubmitSmRespBill(self.user)
        bill.setAmount('submit_sm_resp', self.getAmount('submit_sm_resp'))
        return bill


merge

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def merge(self, nums1, m, nums2, n): :type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead.
- def merge_v2(self, nums1, m, nums2, n): :type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead.

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def merge(self, nums1, m, nums2, n): :type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead.
- def merge_v2(self, nums1, m, nums2, n): :type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead.

<|skeleton|>
class Solution:

    def merge(self, nums1, m, nums2, n):
        """:type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead."""
        <|body_0|>

    def merge_v2(self, nums1, m, nums2, n):
        """:type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        index_m = m - 1
        index_n = n - 1
        while index_m >= 0 and index_n >= 0:
            if nums1[index_m] >= nums2[index_n]:
                nums1[index_m + index_n + 1] = nums1[index_m]
                index_m -= 1
            else:
                nums1[index_m + index_n + 1] = nums2[index_n]
                index_n -= 1
        while index_n >= 0:
            nums1[index_m + index_n + 1] = nums2[index_n]
            index_n -= 1
<|end_body_0|>

<|body_start_1|>
        i1 = m - 1
        i2 = n - 1
        for k in range(m + n - 1, -1, -1):
            if i1 < 0:
                nums1[k] = nums2[i2]
                i2 -= 1
            elif i2 < 0:
                i1 -= 1
                continue
            elif nums2[i2] >= nums1[i1]:
                nums1[k] = nums2[i2]
                i2 -= 1
            else:
                nums1[k] = nums1[i1]
                i1 -= 1
            k -= 1
<|end_body_1|>

Solution:
class Solution:
    def merge(self, nums1, m, nums2, n):
        """:type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead."""
        index_m = m - 1
        index_n = n - 1
        while index_m >= 0 and index_n >= 0:
            if nums1[index_m] >= nums2[index_n]:
                nums1[index_m + index_n + 1] = nums1[index_m]
                index_m -= 1
            else:
                nums1[index_m + index_n + 1] = nums2[index_n]
                index_n -= 1
        while index_n >= 0:
            nums1[index_m + index_n + 1] = nums2[index_n]
            index_n -= 1

    def merge_v2(self, nums1, m, nums2, n):
        """:type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead."""
        i1 = m - 1
        i2 = n - 1
        for k in range(m + n - 1, -1, -1):
            if i1 < 0:
                nums1[k] = nums2[i2]
                i2 -= 1
            elif i2 < 0:
                i1 -= 1
                continue
            elif nums2[i2] >= nums1[i1]:
                nums1[k] = nums2[i2]
                i2 -= 1
            else:
                nums1[k] = nums1[i1]
                i1 -= 1
            k -= 1


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

Class description:
This represents a general FeedForwardNetwork. It can be used in different types of contexts. In general it operates on batches of 1-dimensional data.

Method signatures and docstrings:
- def __init__(self, structure, config): This constructs a new FeedForwardNetwork. It takes the structure of the network and also the configuration. Args: structure: The structure for the hidden layers, a list of integers. config: layer_norm: Activate Layer Normalization activation_fn: Define the activation_fn as lambda tf.Tensor: -> tf.Tensor mask_type: The mask type to use for regularization.
- def eval_graph(self, x, scope, **kwargs): This creates the graph it therefore receives the input tensor and of course the weights for each l. Args: x: The input to the graph. Usually a placeholder scope: The scope of this graph. It is used for creating multiple graph copies Returns: A fully constructed graph using the weights supplied.

Implement the Python class `FeedForwardNetwork` described below.

Class description:
This represents a general FeedForwardNetwork. It can be used in different types of contexts. In general it operates on batches of 1-dimensional data.

Method signatures and docstrings:
- def __init__(self, structure, config): This constructs a new FeedForwardNetwork. It takes the structure of the network and also the configuration. Args: structure: The structure for the hidden layers, a list of integers. config: layer_norm: Activate Layer Normalization activation_fn: Define the activation_fn as lambda tf.Tensor: -> tf.Tensor mask_type: The mask type to use for regularization.
- def eval_graph(self, x, scope, **kwargs): This creates the graph it therefore receives the input tensor and of course the weights for each l. Args: x: The input to the graph. Usually a placeholder scope: The scope of this graph. It is used for creating multiple graph copies Returns: A fully constructed graph using the weights supplied.

<|skeleton|>
class FeedForwardNetwork:
    """This represents a general FeedForwardNetwork. It can be used in different types of contexts. In general it operates on batches of 1-dimensional data."""

    def __init__(self, structure, config):
        """This constructs a new FeedForwardNetwork. It takes the structure of the network and also the configuration. Args: structure: The structure for the hidden layers, a list of integers. config: layer_norm: Activate Layer Normalization activation_fn: Define the activation_fn as lambda tf.Tensor: -> tf.Tensor mask_type: The mask type to use for regularization."""
        <|body_0|>

    def eval_graph(self, x, scope, **kwargs):
        """This creates the graph it therefore receives the input tensor and of course the weights for each l. Args: x: The input to the graph. Usually a placeholder scope: The scope of this graph. It is used for creating multiple graph copies Returns: A fully constructed graph using the weights supplied."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        pe.set_default_val(config, 'layer_norm', True)
        pe.set_default_val(config, 'activation_fn', tfe.leakyrelu(0.1))
        pe.set_default_val(config, 'mask_type', None)
        self.structure = structure
        super().__init__(config)
<|end_body_0|>

<|body_start_1|>
        with tf.variable_scope(scope, reuse=scope in self.log):
            Q = self._eval_fc_network(x, self.structure, regression_layer=True)
        self._collect_scope_vars(scope)
        return Q
<|end_body_1|>

Solution:
class FeedForwardNetwork:
    """This represents a general FeedForwardNetwork. It can be used in different types of contexts. In general it operates on batches of 1-dimensional data."""

    def __init__(self, structure, config):
        """This constructs a new FeedForwardNetwork. It takes the structure of the network and also the configuration. Args: structure: The structure for the hidden layers, a list of integers. config: layer_norm: Activate Layer Normalization activation_fn: Define the activation_fn as lambda tf.Tensor: -> tf.Tensor mask_type: The mask type to use for regularization."""
        pe.set_default_val(config, 'layer_norm', True)
        pe.set_default_val(config, 'activation_fn', tfe.leakyrelu(0.1))
        pe.set_default_val(config, 'mask_type', None)
        self.structure = structure
        super().__init__(config)

    def eval_graph(self, x, scope, **kwargs):
        """This creates the graph it therefore receives the input tensor and of course the weights for each l. Args: x: The input to the graph. Usually a placeholder scope: The scope of this graph. It is used for creating multiple graph copies Returns: A fully constructed graph using the weights supplied."""
        with tf.variable_scope(scope, reuse=scope in self.log):
            Q = self._eval_fc_network(x, self.structure, regression_layer=True)
        self._collect_scope_vars(scope)
        return Q


forward

Prompt:
Implement the Python class `InceptionV3` described below.

Class description:
Pretrained InceptionV3 network returning feature maps

Method signatures and docstrings:
- def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True, requires_grad=False): Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, normalizes the input to the statistics the pre
- def forward(self, inp): Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index

Implement the Python class `InceptionV3` described below.

Class description:
Pretrained InceptionV3 network returning feature maps

Method signatures and docstrings:
- def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True, requires_grad=False): Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, normalizes the input to the statistics the pre
- def forward(self, inp): Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index

<|skeleton|>
class InceptionV3:
    """Pretrained InceptionV3 network returning feature maps"""

    def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True, requires_grad=False):
        """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, normalizes the input to the statistics the pre"""
        <|body_0|>

    def forward(self, inp):
        """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(InceptionV3, self).__init__()
        self.resize_input = resize_input
        self.normalize_input = normalize_input
        self.output_blocks = sorted(output_blocks)
        self.last_needed_block = max(output_blocks)
        assert self.last_needed_block <= 3, 'Last possible output block index is 3'
        self.blocks = nn.ModuleList()
        inception = models.inception_v3(pretrained=True)
        block0 = [inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2)]
        self.blocks.append(nn.Sequential(*block0))
        if self.last_needed_block >= 1:
            block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)]
            self.blocks.append(nn.Sequential(*block1))
        if self.last_needed_block >= 2:
            block2 = [inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e]
            self.blocks.append(nn.Sequential(*block2))
        if self.last_needed_block >= 3:
            block3 = [inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1))]
            self.blocks.append(nn.Sequential(*block3))
        for param in self.parameters():
            param.requires_grad = requires_grad
<|end_body_0|>

<|body_start_1|>
        outp = []
        x = inp
        if self.resize_input:
            x = F.upsample(x, size=(299, 299), mode='bilinear')
        if self.normalize_input:
            x = x.clone()
            x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
            x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
            x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
        for idx, block in enumerate(self.blocks):
            x = block(x)
            if idx in self.output_blocks:
                outp.append(x)
            if idx == self.last_needed_block:
                break
        return outp
<|end_body_1|>

Solution:
class InceptionV3:
    """Pretrained InceptionV3 network returning feature maps"""

    def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True, requires_grad=False):
        """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, normalizes the input to the statistics the pre"""
        super(InceptionV3, self).__init__()
        self.resize_input = resize_input
        self.normalize_input = normalize_input
        self.output_blocks = sorted(output_blocks)
        self.last_needed_block = max(output_blocks)
        assert self.last_needed_block <= 3, 'Last possible output block index is 3'
        self.blocks = nn.ModuleList()
        inception = models.inception_v3(pretrained=True)
        block0 = [inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2)]
        self.blocks.append(nn.Sequential(*block0))
        if self.last_needed_block >= 1:
            block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)]
            self.blocks.append(nn.Sequential(*block1))
        if self.last_needed_block >= 2:
            block2 = [inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e]
            self.blocks.append(nn.Sequential(*block2))
        if self.last_needed_block >= 3:
            block3 = [inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1))]
            self.blocks.append(nn.Sequential(*block3))
        for param in self.parameters():
            param.requires_grad = requires_grad

    def forward(self, inp):
        """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index"""
        outp = []
        x = inp
        if self.resize_input:
            x = F.upsample(x, size=(299, 299), mode='bilinear')
        if self.normalize_input:
            x = x.clone()
            x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
            x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
            x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
        for idx, block in enumerate(self.blocks):
            x = block(x)
            if idx in self.output_blocks:
                outp.append(x)
            if idx == self.last_needed_block:
                break
        return outp


edit

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

Class description:
Implement the QuestionManager class.

Method signatures and docstrings:
- def accept_answer(self, answer): Accepts an @answer as the correct answer to a question. Each question can only have max 1 accepted answer.
- def get_accepted_answer(self, question): Returns the accepted answer for target @question if it has one, otherwise return None.
- def get_answers(self, question, option='highest_rated'): Returns a QuerySet of answers associated with target @question sorted by @option.
- def add(self, user, data): Create a new question owned by @user based on @data.
- def edit(self, question, user, data): Modifies target @question with new @data. Sends edit signal.

Implement the Python class `QuestionManager` described below.

Class description:
Implement the QuestionManager class.

Method signatures and docstrings:
- def accept_answer(self, answer): Accepts an @answer as the correct answer to a question. Each question can only have max 1 accepted answer.
- def get_accepted_answer(self, question): Returns the accepted answer for target @question if it has one, otherwise return None.
- def get_answers(self, question, option='highest_rated'): Returns a QuerySet of answers associated with target @question sorted by @option.
- def add(self, user, data): Create a new question owned by @user based on @data.
- def edit(self, question, user, data): Modifies target @question with new @data. Sends edit signal.

<|skeleton|>
class QuestionManager:

    def accept_answer(self, answer):
        """Accepts an @answer as the correct answer to a question. Each question can only have max 1 accepted answer."""
        <|body_0|>

    def get_accepted_answer(self, question):
        """Returns the accepted answer for target @question if it has one, otherwise return None."""
        <|body_1|>

    def get_answers(self, question, option='highest_rated'):
        """Returns a QuerySet of answers associated with target @question sorted by @option."""
        <|body_2|>

    def add(self, user, data):
        """Create a new question owned by @user based on @data."""
        <|body_3|>

    def edit(self, question, user, data):
        """Modifies target @question with new @data. Sends edit signal."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        if not self.get_accepted_answer(answer.question):
            answer.accepted = True
            answer.save()
            answer_accepted.send(sender=Answer, instance=answer)
        else:
            answer = None
        return answer
<|end_body_0|>

<|body_start_1|>
        try:
            answer = Answer.objects.get(question=question, accepted=True)
        except ObjectDoesNotExist:
            answer = None
        return answer
<|end_body_1|>

<|body_start_2|>
        if option == 'most_recent':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        elif option == 'most_discussed':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        elif option == 'highest_rated':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        return answers
<|end_body_2|>

<|body_start_3|>
        if self.can_add(user):
            name = data.get('title', None)
            body = data.get('question', None)
            question = self.model(name=name, question=body, user=user)
            question.save()
            signals.edit.send(self.model, original=None, current=question, editor=user)
        else:
            question = None
        return question
<|end_body_3|>

<|body_start_4|>
        name = data.get('title', None)
        body = data.get('question', None)
        original = self.model.objects.get(id=question.id)
        question.name = name
        question.question = body
        question.save()
        signals.edit.send(sender=self.model, original=original, current=question, editor=user)
        return question
<|end_body_4|>

Solution:
class QuestionManager:
    def accept_answer(self, answer):
        """Accepts an @answer as the correct answer to a question. Each question can only have max 1 accepted answer."""
        if not self.get_accepted_answer(answer.question):
            answer.accepted = True
            answer.save()
            answer_accepted.send(sender=Answer, instance=answer)
        else:
            answer = None
        return answer

    def get_accepted_answer(self, question):
        """Returns the accepted answer for target @question if it has one, otherwise return None."""
        try:
            answer = Answer.objects.get(question=question, accepted=True)
        except ObjectDoesNotExist:
            answer = None
        return answer

    def get_answers(self, question, option='highest_rated'):
        """Returns a QuerySet of answers associated with target @question sorted by @option."""
        if option == 'most_recent':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        elif option == 'most_discussed':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        elif option == 'highest_rated':
            answers = Answer.objects.get_sorted_objects(option).filter(question=question)
        return answers

    def add(self, user, data):
        """Create a new question owned by @user based on @data."""
        if self.can_add(user):
            name = data.get('title', None)
            body = data.get('question', None)
            question = self.model(name=name, question=body, user=user)
            question.save()
            signals.edit.send(self.model, original=None, current=question, editor=user)
        else:
            question = None
        return question

    def edit(self, question, user, data):
        """Modifies target @question with new @data. Sends edit signal."""
        name = data.get('title', None)
        body = data.get('question', None)
        original = self.model.objects.get(id=question.id)
        question.name = name
        question.question = body
        question.save()
        signals.edit.send(sender=self.model, original=original, current=question, editor=user)
        return question


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCut(self, s): :type s: str :rtype: int
- def minCut_v2(self, s): :type s: str :rtype: int
- def minCut_TLE(self, s): :type s: str :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minCut(self, s): :type s: str :rtype: int
- def minCut_v2(self, s): :type s: str :rtype: int
- def minCut_TLE(self, s): :type s: str :rtype: int

<|skeleton|>
class Solution:

    def minCut(self, s):
        """:type s: str :rtype: int"""
        <|body_0|>

    def minCut_v2(self, s):
        """:type s: str :rtype: int"""
        <|body_1|>

    def minCut_TLE(self, s):
        """:type s: str :rtype: int"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        def search(left, right):
            while left >= 0 and right < len(s) and (s[left] == s[right]):
                if left == 0:
                    cuts[right] = 0
                else:
                    cuts[right] = min(cuts[right], cuts[left - 1] + 1)
                left -= 1
                right += 1
        cuts = [x for x in range(len(s))]
        for i in range(len(s)):
            search(i, i)
            search(i, i + 1)
        return cuts[-1]
<|end_body_0|>

<|body_start_1|>
        cut = [x for x in range(-1, len(s))]
        for i in range(1, len(s)):
            for left, right in [(i, i), (i - 1, i)]:
                while left >= 0 and right < len(s) and (s[left] == s[right]):
                    cut[right + 1] = min(cut[right + 1], cut[left] + 1)
                    left -= 1
                    right += 1
        return cut[-1]
<|end_body_1|>

<|body_start_2|>
        dp = [[0] * len(s) for _ in range(len(s))]
        for size in range(len(s)):
            for i in range(len(s)):
                j = i + size
                if j >= len(s):
                    break
                elif i == j:
                    continue
                elif j - i == 1:
                    if s[i] == s[j]:
                        dp[i][j] = 0
                    else:
                        dp[i][j] = 1
                elif s[i] == s[j] and dp[i + 1][j - 1] == 0:
                    dp[i][j] = 0
                else:
                    min_cut = float('inf')
                    for k in range(i, j):
                        min_cut = min(min_cut, 1 + dp[i][k] + dp[k + 1][j])
                    dp[i][j] = min_cut
        return dp[0][-1]
<|end_body_2|>

Solution:
class Solution:
    def minCut(self, s):
        """:type s: str :rtype: int"""
        def search(left, right):
            while left >= 0 and right < len(s) and (s[left] == s[right]):
                if left == 0:
                    cuts[right] = 0
                else:
                    cuts[right] = min(cuts[right], cuts[left - 1] + 1)
                left -= 1
                right += 1
        cuts = [x for x in range(len(s))]
        for i in range(len(s)):
            search(i, i)
            search(i, i + 1)
        return cuts[-1]

    def minCut_v2(self, s):
        """:type s: str :rtype: int"""
        cut = [x for x in range(-1, len(s))]
        for i in range(1, len(s)):
            for left, right in [(i, i), (i - 1, i)]:
                while left >= 0 and right < len(s) and (s[left] == s[right]):
                    cut[right + 1] = min(cut[right + 1], cut[left] + 1)
                    left -= 1
                    right += 1
        return cut[-1]

    def minCut_TLE(self, s):
        """:type s: str :rtype: int"""
        dp = [[0] * len(s) for _ in range(len(s))]
        for size in range(len(s)):
            for i in range(len(s)):
                j = i + size
                if j >= len(s):
                    break
                elif i == j:
                    continue
                elif j - i == 1:
                    if s[i] == s[j]:
                        dp[i][j] = 0
                    else:
                        dp[i][j] = 1
                elif s[i] == s[j] and dp[i + 1][j - 1] == 0:
                    dp[i][j] = 0
                else:
                    min_cut = float('inf')
                    for k in range(i, j):
                        min_cut = min(min_cut, 1 + dp[i][k] + dp[k + 1][j])
                    dp[i][j] = min_cut
        return dp[0][-1]


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

Class description:
Implement the QuestionModelTests class.

Method signatures and docstrings:
- def test_reply_empty_string(self): seen be False when response is empty
- def test_reply_not_empty_string(self): seen be True when response is not empty
- def test_unreply(self): seen be True when response is not empty

Implement the Python class `QuestionModelTests` described below.

Class description:
Implement the QuestionModelTests class.

Method signatures and docstrings:
- def test_reply_empty_string(self): seen be False when response is empty
- def test_reply_not_empty_string(self): seen be True when response is not empty
- def test_unreply(self): seen be True when response is not empty

<|skeleton|>
class QuestionModelTests:

    def test_reply_empty_string(self):
        """seen be False when response is empty"""
        <|body_0|>

    def test_reply_not_empty_string(self):
        """seen be True when response is not empty"""
        <|body_1|>

    def test_unreply(self):
        """seen be True when response is not empty"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        empty_reply = create_question()
        empty_reply.reply('')
        self.assertIs(empty_reply.seen, False)
<|end_body_0|>

<|body_start_1|>
        not_empty_reply = create_question()
        not_empty_reply.reply('Wong')
        self.assertIs(not_empty_reply.seen, True)
<|end_body_1|>

<|body_start_2|>
        unreply_question = create_question()
        unreply_question.submit()
        self.assertIs(unreply_question.seen, False)
<|end_body_2|>

Solution:
class QuestionModelTests:
    def test_reply_empty_string(self):
        """seen be False when response is empty"""
        empty_reply = create_question()
        empty_reply.reply('')
        self.assertIs(empty_reply.seen, False)

    def test_reply_not_empty_string(self):
        """seen be True when response is not empty"""
        not_empty_reply = create_question()
        not_empty_reply.reply('Wong')
        self.assertIs(not_empty_reply.seen, True)

    def test_unreply(self):
        """seen be True when response is not empty"""
        unreply_question = create_question()
        unreply_question.submit()
        self.assertIs(unreply_question.seen, False)


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

Class description:
The interaction plane class computes the plane reaction force on a rod-like object. For more details regarding the contact module refer to Eqn 4.8 of Gazzola et al. RSoS (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. surface_tol: float Penetration tolerance between the plane and the rod-like object.

Method signatures and docstrings:
- def __init__(self, k, nu, plane_origin, plane_normal): Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane.
- def apply_normal_force(self, system): In the case of contact with the plane, this function computes the plane reaction force on the element. Parameters ---------- system: object Rod-like object. Returns ------- plane_response_force_mag : numpy.ndarray 1D (blocksize) array containing data with 'float' type. Magnitude of plane response force acting on rod-like object. no_contact_point_idx : numpy.ndarray 1D (blocksize) array containing data with 'int' type. Index of rod-like object elements that are not in contact with the plane.

Implement the Python class `InteractionPlane` described below.

Class description:
The interaction plane class computes the plane reaction force on a rod-like object. For more details regarding the contact module refer to Eqn 4.8 of Gazzola et al. RSoS (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. surface_tol: float Penetration tolerance between the plane and the rod-like object.

Method signatures and docstrings:
- def __init__(self, k, nu, plane_origin, plane_normal): Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane.
- def apply_normal_force(self, system): In the case of contact with the plane, this function computes the plane reaction force on the element. Parameters ---------- system: object Rod-like object. Returns ------- plane_response_force_mag : numpy.ndarray 1D (blocksize) array containing data with 'float' type. Magnitude of plane response force acting on rod-like object. no_contact_point_idx : numpy.ndarray 1D (blocksize) array containing data with 'int' type. Index of rod-like object elements that are not in contact with the plane.

<|skeleton|>
class InteractionPlane:
    """The interaction plane class computes the plane reaction force on a rod-like object. For more details regarding the contact module refer to Eqn 4.8 of Gazzola et al. RSoS (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. surface_tol: float Penetration tolerance between the plane and the rod-like object."""

    def __init__(self, k, nu, plane_origin, plane_normal):
        """Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane."""
        <|body_0|>

    def apply_normal_force(self, system):
        """In the case of contact with the plane, this function computes the plane reaction force on the element. Parameters ---------- system: object Rod-like object. Returns ------- plane_response_force_mag : numpy.ndarray 1D (blocksize) array containing data with 'float' type. Magnitude of plane response force acting on rod-like object. no_contact_point_idx : numpy.ndarray 1D (blocksize) array containing data with 'int' type. Index of rod-like object elements that are not in contact with the plane."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.k = k
        self.nu = nu
        self.plane_origin = plane_origin.reshape(3, 1)
        self.plane_normal = plane_normal.reshape(3)
        self.surface_tol = 0.0001
<|end_body_0|>

<|body_start_1|>
        nodal_total_forces = system.internal_forces + system.external_forces
        element_total_forces = nodes_to_elements(nodal_total_forces)
        force_component_along_normal_direction = np.einsum('i, ij->j', self.plane_normal, element_total_forces)
        forces_along_normal_direction = np.einsum('i, j->ij', self.plane_normal, force_component_along_normal_direction)
        forces_along_normal_direction[..., np.where(force_component_along_normal_direction > 0)] = 0.0
        plane_response_force = -forces_along_normal_direction
        element_position = 0.5 * (system.position_collection[..., :-1] + system.position_collection[..., 1:])
        distance_from_plane = np.einsum('i, ij->j', self.plane_normal, element_position - self.plane_origin)
        plane_penetration = np.minimum(distance_from_plane - system.radius, 0.0)
        elastic_force = -self.k * np.einsum('i, j->ij', self.plane_normal, plane_penetration)
        element_velocity = 0.5 * (system.velocity_collection[..., :-1] + system.velocity_collection[..., 1:])
        normal_component_of_element_velocity = np.einsum('i, ij->j', self.plane_normal, element_velocity)
        damping_force = -self.nu * np.einsum('i, j->ij', self.plane_normal, normal_component_of_element_velocity)
        plane_response_force_total = plane_response_force + elastic_force + damping_force
        no_contact_point_idx = np.where(distance_from_plane - system.radius > self.surface_tol)
        plane_response_force[..., no_contact_point_idx] = 0.0
        plane_response_force_total[..., no_contact_point_idx] = 0.0
        system.external_forces[..., :-1] += 0.5 * plane_response_force_total
        system.external_forces[..., 1:] += 0.5 * plane_response_force_total
        return (np.sqrt(np.einsum('ij, ij->j', plane_response_force, plane_response_force)), no_contact_point_idx)
<|end_body_1|>

Solution:
class InteractionPlane:
    """The interaction plane class computes the plane reaction force on a rod-like object. For more details regarding the contact module refer to Eqn 4.8 of Gazzola et al. RSoS (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. surface_tol: float Penetration tolerance between the plane and the rod-like object."""

    def __init__(self, k, nu, plane_origin, plane_normal):
        """Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane."""
        self.k = k
        self.nu = nu
        self.plane_origin = plane_origin.reshape(3, 1)
        self.plane_normal = plane_normal.reshape(3)
        self.surface_tol = 0.0001

    def apply_normal_force(self, system):
        """In the case of contact with the plane, this function computes the plane reaction force on the element. Parameters ---------- system: object Rod-like object. Returns ------- plane_response_force_mag : numpy.ndarray 1D (blocksize) array containing data with 'float' type. Magnitude of plane response force acting on rod-like object. no_contact_point_idx : numpy.ndarray 1D (blocksize) array containing data with 'int' type. Index of rod-like object elements that are not in contact with the plane."""
        nodal_total_forces = system.internal_forces + system.external_forces
        element_total_forces = nodes_to_elements(nodal_total_forces)
        force_component_along_normal_direction = np.einsum('i, ij->j', self.plane_normal, element_total_forces)
        forces_along_normal_direction = np.einsum('i, j->ij', self.plane_normal, force_component_along_normal_direction)
        forces_along_normal_direction[..., np.where(force_component_along_normal_direction > 0)] = 0.0
        plane_response_force = -forces_along_normal_direction
        element_position = 0.5 * (system.position_collection[..., :-1] + system.position_collection[..., 1:])
        distance_from_plane = np.einsum('i, ij->j', self.plane_normal, element_position - self.plane_origin)
        plane_penetration = np.minimum(distance_from_plane - system.radius, 0.0)
        elastic_force = -self.k * np.einsum('i, j->ij', self.plane_normal, plane_penetration)
        element_velocity = 0.5 * (system.velocity_collection[..., :-1] + system.velocity_collection[..., 1:])
        normal_component_of_element_velocity = np.einsum('i, ij->j', self.plane_normal, element_velocity)
        damping_force = -self.nu * np.einsum('i, j->ij', self.plane_normal, normal_component_of_element_velocity)
        plane_response_force_total = plane_response_force + elastic_force + damping_force
        no_contact_point_idx = np.where(distance_from_plane - system.radius > self.surface_tol)
        plane_response_force[..., no_contact_point_idx] = 0.0
        plane_response_force_total[..., no_contact_point_idx] = 0.0
        system.external_forces[..., :-1] += 0.5 * plane_response_force_total
        system.external_forces[..., 1:] += 0.5 * plane_response_force_total
        return (np.sqrt(np.einsum('ij, ij->j', plane_response_force, plane_response_force)), no_contact_point_idx)


create

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

Class description:
Implement the BookManagementView class.

Method signatures and docstrings:
- def list(self, request, *args, **kwargs): Find book info from OpenBD API by ISBN Code. :param request: ISBN Code :param args: :param kwargs: :return: book info(format:json)
- def create(self, request, *args, **kwargs): create book info :param request: book info :param args: :param kwargs: :return: The registered book info if successful. Otherwise an error message

Implement the Python class `BookManagementView` described below.

Class description:
Implement the BookManagementView class.

Method signatures and docstrings:
- def list(self, request, *args, **kwargs): Find book info from OpenBD API by ISBN Code. :param request: ISBN Code :param args: :param kwargs: :return: book info(format:json)
- def create(self, request, *args, **kwargs): create book info :param request: book info :param args: :param kwargs: :return: The registered book info if successful. Otherwise an error message

<|skeleton|>
class BookManagementView:

    def list(self, request, *args, **kwargs):
        """Find book info from OpenBD API by ISBN Code. :param request: ISBN Code :param args: :param kwargs: :return: book info(format:json)"""
        <|body_0|>

    def create(self, request, *args, **kwargs):
        """create book info :param request: book info :param args: :param kwargs: :return: The registered book info if successful. Otherwise an error message"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            isbn = request.data['isbn'].replace('-', '')
            if Book.objects.filter(isbn=isbn).count() == 0:
                data = OpenBD().get_json(isbn)
                logger.debug('Success BookManagementView.list')
                logger.debug(data)
                return Response(data=data, status=HTTP_200_OK)
            else:
                book = Book.objects.filter(isbn=request.data['isbn']).get()
                serializer = BookManagementSerializer(data=book)
                if not serializer.is_valid():
                    raise ValueError(serializer.errors)
                logger.debug('Success BookManagementView.list')
                logger.debug(serializer.data)
                return Response(data=serializer.data, status=HTTP_200_OK)
        except Exception as e:
            logger.error('Exception BookManagementView.list')
            logger.error(e)
            return Response(data={'error': e.args[0]}, status=HTTP_400_BAD_REQUEST)
<|end_body_0|>

<|body_start_1|>
        try:
            serializer = BookManagementSerializer(data=request.data)
            if not serializer.is_valid():
                raise ValueError(serializer.errors)
            publisher_serializer = PublisherSerializer(data={''})
            publisher_serializer.save()
            author_serializer = AuthorSerializer(data=serializer.validated_data)
            author_serializer.save()
            book_manage_serializer = BookManagementSerializer(data=serializer.validated_data)
            book_manage_serializer.save()
            if serializer.validated_data['series'] != '':
                series = Series.objects.filter(name=serializer.validated_data['series']).get()
                if len(series) == 0:
                    series_serializer = SeriesSerializer(data=serializer.validated_data)
                    series_serializer.save()
                series_book_serializer = SeriesBookSerializer(data=serializer.validated_data)
                series_book_serializer.save()
            serializer = BookManagementSerializer(data=serializer.validated_data)
            return Response(data=serializer.data, status=HTTP_201_CREATED)
        except RegisterError as e:
            logger.error('Exception BookManageViewSet.create')
            logger.error(e)
            return Response(data={'error': e.args[0]}, status=HTTP_400_BAD_REQUEST)
<|end_body_1|>

Solution:
class BookManagementView:
    def list(self, request, *args, **kwargs):
        """Find book info from OpenBD API by ISBN Code. :param request: ISBN Code :param args: :param kwargs: :return: book info(format:json)"""
        try:
            isbn = request.data['isbn'].replace('-', '')
            if Book.objects.filter(isbn=isbn).count() == 0:
                data = OpenBD().get_json(isbn)
                logger.debug('Success BookManagementView.list')
                logger.debug(data)
                return Response(data=data, status=HTTP_200_OK)
            else:
                book = Book.objects.filter(isbn=request.data['isbn']).get()
                serializer = BookManagementSerializer(data=book)
                if not serializer.is_valid():
                    raise ValueError(serializer.errors)
                logger.debug('Success BookManagementView.list')
                logger.debug(serializer.data)
                return Response(data=serializer.data, status=HTTP_200_OK)
        except Exception as e:
            logger.error('Exception BookManagementView.list')
            logger.error(e)
            return Response(data={'error': e.args[0]}, status=HTTP_400_BAD_REQUEST)

    def create(self, request, *args, **kwargs):
        """create book info :param request: book info :param args: :param kwargs: :return: The registered book info if successful. Otherwise an error message"""
        try:
            serializer = BookManagementSerializer(data=request.data)
            if not serializer.is_valid():
                raise ValueError(serializer.errors)
            publisher_serializer = PublisherSerializer(data={''})
            publisher_serializer.save()
            author_serializer = AuthorSerializer(data=serializer.validated_data)
            author_serializer.save()
            book_manage_serializer = BookManagementSerializer(data=serializer.validated_data)
            book_manage_serializer.save()
            if serializer.validated_data['series'] != '':
                series = Series.objects.filter(name=serializer.validated_data['series']).get()
                if len(series) == 0:
                    series_serializer = SeriesSerializer(data=serializer.validated_data)
                    series_serializer.save()
                series_book_serializer = SeriesBookSerializer(data=serializer.validated_data)
                series_book_serializer.save()
            serializer = BookManagementSerializer(data=serializer.validated_data)
            return Response(data=serializer.data, status=HTTP_201_CREATED)
        except RegisterError as e:
            logger.error('Exception BookManageViewSet.create')
            logger.error(e)
            return Response(data={'error': e.args[0]}, status=HTTP_400_BAD_REQUEST)


deserialize

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

Class description:
Implement the Codec class.

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

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

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

<|skeleton|>
class Codec:

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

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

<|end_skeleton|>

<|body_start_0|>
        res = []
        if not root:
            return res
        res.append(root.val)
        for node in root.children:
            res.append(self.serialize(node))
        return res
<|end_body_0|>

<|body_start_1|>
        if not data:
            return None
        v = data[0]
        children = []
        for c in data[1:]:
            children.append(self.deserialize(c))
        return Node(v, children)
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root: Node):
        """Encodes a tree to a single string. :type root: Node :rtype: str"""
        res = []
        if not root:
            return res
        res.append(root.val)
        for node in root.children:
            res.append(self.serialize(node))
        return res

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: Node"""
        if not data:
            return None
        v = data[0]
        children = []
        for c in data[1:]:
            children.append(self.deserialize(c))
        return Node(v, children)


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

Class description:
BsyncrPipeline is a class for preparing, running, and post processing the bsyncr analysis by implementing the AnalysisPipeline's abstract methods.

Method signatures and docstrings:
- def _prepare_analysis(self, property_view_ids, start_analysis=False): Internal implementation for preparing bsyncr analysis
- def _start_analysis(self): Internal implementation for starting the bsyncr analysis

Implement the Python class `BsyncrPipeline` described below.

Class description:
BsyncrPipeline is a class for preparing, running, and post processing the bsyncr analysis by implementing the AnalysisPipeline's abstract methods.

Method signatures and docstrings:
- def _prepare_analysis(self, property_view_ids, start_analysis=False): Internal implementation for preparing bsyncr analysis
- def _start_analysis(self): Internal implementation for starting the bsyncr analysis

<|skeleton|>
class BsyncrPipeline:
    """BsyncrPipeline is a class for preparing, running, and post processing the bsyncr analysis by implementing the AnalysisPipeline's abstract methods."""

    def _prepare_analysis(self, property_view_ids, start_analysis=False):
        """Internal implementation for preparing bsyncr analysis"""
        <|body_0|>

    def _start_analysis(self):
        """Internal implementation for starting the bsyncr analysis"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not settings.BSYNCR_SERVER_HOST:
            message = 'SEED instance is not configured to run bsyncr analysis. Please contact the server administrator.'
            self.fail(message, logger)
            raise AnalysisPipelineException(message)
        validation_errors = _validate_bsyncr_config(Analysis.objects.get(id=self._analysis_id))
        if validation_errors:
            raise AnalysisPipelineException(f"Unexpected error(s) while validating analysis configuration: {'; '.join(validation_errors)}")
        progress_data = self.get_progress_data()
        progress_data.total = 3
        progress_data.save()
        chain(task_create_analysis_property_views.si(self._analysis_id, property_view_ids), _prepare_all_properties.s(self._analysis_id), _finish_preparation.si(self._analysis_id, start_analysis)).apply_async()
<|end_body_0|>

<|body_start_1|>
        progress_data = self.get_progress_data()
        progress_data.total = 3
        progress_data.save()
        chain(_start_analysis.si(self._analysis_id), _process_results.s(self._analysis_id), _finish_analysis.si(self._analysis_id)).apply_async()
<|end_body_1|>

Solution:
class BsyncrPipeline:
    """BsyncrPipeline is a class for preparing, running, and post processing the bsyncr analysis by implementing the AnalysisPipeline's abstract methods."""

    def _prepare_analysis(self, property_view_ids, start_analysis=False):
        """Internal implementation for preparing bsyncr analysis"""
        if not settings.BSYNCR_SERVER_HOST:
            message = 'SEED instance is not configured to run bsyncr analysis. Please contact the server administrator.'
            self.fail(message, logger)
            raise AnalysisPipelineException(message)
        validation_errors = _validate_bsyncr_config(Analysis.objects.get(id=self._analysis_id))
        if validation_errors:
            raise AnalysisPipelineException(f"Unexpected error(s) while validating analysis configuration: {'; '.join(validation_errors)}")
        progress_data = self.get_progress_data()
        progress_data.total = 3
        progress_data.save()
        chain(task_create_analysis_property_views.si(self._analysis_id, property_view_ids), _prepare_all_properties.s(self._analysis_id), _finish_preparation.si(self._analysis_id, start_analysis)).apply_async()

    def _start_analysis(self):
        """Internal implementation for starting the bsyncr analysis"""
        progress_data = self.get_progress_data()
        progress_data.total = 3
        progress_data.save()
        chain(_start_analysis.si(self._analysis_id), _process_results.s(self._analysis_id), _finish_analysis.si(self._analysis_id)).apply_async()


parse

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

Class description:
An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!

Method signatures and docstrings:
- def __init__(self, base_url=None, doc_id=None): doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclusions fail immediately rather than after the first round of recursion.
- def parse(self, text, **kwargs): Wrap SUMO's parse() to support additional wiki-only features.
- def _hook_include(self, parser, space, title): Returns the document's parsed content.
- def _hook_template(self, parser, space, title): Handles Template:Template name, formatting the content using given args

Implement the Python class `WikiParser` described below.

Class description:
An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!

Method signatures and docstrings:
- def __init__(self, base_url=None, doc_id=None): doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclusions fail immediately rather than after the first round of recursion.
- def parse(self, text, **kwargs): Wrap SUMO's parse() to support additional wiki-only features.
- def _hook_include(self, parser, space, title): Returns the document's parsed content.
- def _hook_template(self, parser, space, title): Handles Template:Template name, formatting the content using given args

<|skeleton|>
class WikiParser:
    """An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!"""

    def __init__(self, base_url=None, doc_id=None):
        """doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclusions fail immediately rather than after the first round of recursion."""
        <|body_0|>

    def parse(self, text, **kwargs):
        """Wrap SUMO's parse() to support additional wiki-only features."""
        <|body_1|>

    def _hook_include(self, parser, space, title):
        """Returns the document's parsed content."""
        <|body_2|>

    def _hook_template(self, parser, space, title):
        """Handles Template:Template name, formatting the content using given args"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super(WikiParser, self).__init__(base_url)
        self.inclusions = [doc_id] if doc_id else []
        self.registerInternalLinkHook('Include', self._hook_include)
        self.registerInternalLinkHook('I', self._hook_include)
        self.registerInternalLinkHook('Template', self._hook_template)
        self.registerInternalLinkHook('T', self._hook_template)
<|end_body_0|>

<|body_start_1|>
        text, data = ForParser.strip_fors(text)
        text = parse_simple_syntax(text)
        html = super(WikiParser, self).parse(text, youtube_embeds=False, ui_component_embeds=False, **kwargs)
        html = ForParser.unstrip_fors(html, data)
        for_parser = ForParser(html)
        for_parser.expand_fors()
        html = for_parser.serialize(**kwargs)
        html = self.add_youtube_embeds(html)
        html = self.add_ui_component_embeds(html)
        return html
<|end_body_1|>

<|body_start_2|>
        message = _('The document "%s" does not exist.') % title
        include = get_object_fallback(Document, title, locale=self.locale)
        if not include or not include.current_revision:
            return message
        if include.id in parser.inclusions:
            return RECURSION_MESSAGE % title
        else:
            parser.inclusions.append(include.id)
        ret = parser.parse(include.current_revision.content, show_toc=False, locale=self.locale)
        parser.inclusions.pop()
        return ret
<|end_body_2|>

<|body_start_3|>
        params = title.split('|')
        short_title = params.pop(0)
        template_title = 'Template:' + short_title
        message = _('The template "%s" does not exist or has no approved revision.') % short_title
        template = get_object_fallback(Document, template_title, locale=self.locale, is_template=True)
        if not template or not template.current_revision:
            return message
        if template.id in parser.inclusions:
            return RECURSION_MESSAGE % template_title
        else:
            parser.inclusions.append(template.id)
        c = template.current_revision.content.rstrip()
        parsed = parser.parse(c, show_toc=False, attributes=ALLOWED_ATTRIBUTES, locale=self.locale)
        parser.inclusions.pop()
        if '\n' not in c:
            parsed = parsed.replace('<p>', '')
            parsed = parsed.replace('</p>', '')
        return _format_template_content(parsed, _build_template_params(params))
<|end_body_3|>

Solution:
class WikiParser:
    """An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!"""

    def __init__(self, base_url=None, doc_id=None):
        """doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclusions fail immediately rather than after the first round of recursion."""
        super(WikiParser, self).__init__(base_url)
        self.inclusions = [doc_id] if doc_id else []
        self.registerInternalLinkHook('Include', self._hook_include)
        self.registerInternalLinkHook('I', self._hook_include)
        self.registerInternalLinkHook('Template', self._hook_template)
        self.registerInternalLinkHook('T', self._hook_template)

    def parse(self, text, **kwargs):
        """Wrap SUMO's parse() to support additional wiki-only features."""
        text, data = ForParser.strip_fors(text)
        text = parse_simple_syntax(text)
        html = super(WikiParser, self).parse(text, youtube_embeds=False, ui_component_embeds=False, **kwargs)
        html = ForParser.unstrip_fors(html, data)
        for_parser = ForParser(html)
        for_parser.expand_fors()
        html = for_parser.serialize(**kwargs)
        html = self.add_youtube_embeds(html)
        html = self.add_ui_component_embeds(html)
        return html

    def _hook_include(self, parser, space, title):
        """Returns the document's parsed content."""
        message = _('The document "%s" does not exist.') % title
        include = get_object_fallback(Document, title, locale=self.locale)
        if not include or not include.current_revision:
            return message
        if include.id in parser.inclusions:
            return RECURSION_MESSAGE % title
        else:
            parser.inclusions.append(include.id)
        ret = parser.parse(include.current_revision.content, show_toc=False, locale=self.locale)
        parser.inclusions.pop()
        return ret

    def _hook_template(self, parser, space, title):
        """Handles Template:Template name, formatting the content using given args"""
        params = title.split('|')
        short_title = params.pop(0)
        template_title = 'Template:' + short_title
        message = _('The template "%s" does not exist or has no approved revision.') % short_title
        template = get_object_fallback(Document, template_title, locale=self.locale, is_template=True)
        if not template or not template.current_revision:
            return message
        if template.id in parser.inclusions:
            return RECURSION_MESSAGE % template_title
        else:
            parser.inclusions.append(template.id)
        c = template.current_revision.content.rstrip()
        parsed = parser.parse(c, show_toc=False, attributes=ALLOWED_ATTRIBUTES, locale=self.locale)
        parser.inclusions.pop()
        if '\n' not in c:
            parsed = parsed.replace('<p>', '')
            parsed = parsed.replace('</p>', '')
        return _format_template_content(parsed, _build_template_params(params))


post

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

Class description:
Implement the Views class.

Method signatures and docstrings:
- def get(self, request, repo_base, repo_name, format=None): Views in a repo
- def post(self, request, repo_base, repo_name, format=None): Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string description: name of the the view to be created required: true - name: query in: body type: string description: select query to create the view from required: true

Implement the Python class `Views` described below.

Class description:
Implement the Views class.

Method signatures and docstrings:
- def get(self, request, repo_base, repo_name, format=None): Views in a repo
- def post(self, request, repo_base, repo_name, format=None): Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string description: name of the the view to be created required: true - name: query in: body type: string description: select query to create the view from required: true

<|skeleton|>
class Views:

    def get(self, request, repo_base, repo_name, format=None):
        """Views in a repo"""
        <|body_0|>

    def post(self, request, repo_base, repo_name, format=None):
        """Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string description: name of the the view to be created required: true - name: query in: body type: string description: select query to create the view from required: true"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        username = request.user.get_username()
        serializer = ViewSerializer(username=username, repo_base=repo_base, request=request)
        views = serializer.list_views(repo_name)
        return Response(views, status=status.HTTP_200_OK)
<|end_body_0|>

<|body_start_1|>
        username = request.user.get_username()
        serializer = ViewSerializer(username=username, repo_base=repo_base)
        view_name = request.data['view_name']
        query = request.data['query']
        serializer.create_view(repo_name, view_name, query)
        view = serializer.describe_view(repo_name, view_name)
        return Response(view, status=status.HTTP_201_CREATED)
<|end_body_1|>

Solution:
class Views:
    def get(self, request, repo_base, repo_name, format=None):
        """Views in a repo"""
        username = request.user.get_username()
        serializer = ViewSerializer(username=username, repo_base=repo_base, request=request)
        views = serializer.list_views(repo_name)
        return Response(views, status=status.HTTP_200_OK)

    def post(self, request, repo_base, repo_name, format=None):
        """Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string description: name of the the view to be created required: true - name: query in: body type: string description: select query to create the view from required: true"""
        username = request.user.get_username()
        serializer = ViewSerializer(username=username, repo_base=repo_base)
        view_name = request.data['view_name']
        query = request.data['query']
        serializer.create_view(repo_name, view_name, query)
        view = serializer.describe_view(repo_name, view_name)
        return Response(view, status=status.HTTP_201_CREATED)


containsNearbyDuplicate

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool

<|skeleton|>
class Solution:

    def containsNearbyDuplicate(self, nums, k):
        """:type nums: List[int] :type k: int :rtype: bool"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        dict_nums = set()
        for i in range(k):
            if nums[i] in dict_nums:
                return True
            dict_nums.add(nums[i])
            print(i, k, dict_nums)
        for i in range(k, len(nums)):
            if nums[i] in dict_nums:
                return True
            dict_nums.add(nums[i])
            print(i, k, dict_nums)
            dict_nums.discard(nums[i - k])
            print(i, k, dict_nums)
        return False
<|end_body_0|>

<|body_start_1|>
        memo = set()
        for i, n in enumerate(nums):
            if i - k > 0:
                memo.remove(nums[i - k - 1])
            if n in memo:
                return True
            memo.add(n)
        return False
<|end_body_1|>

Solution:
class Solution:
    def containsNearbyDuplicate(self, nums, k):
        """:type nums: List[int] :type k: int :rtype: bool"""
        dict_nums = set()
        for i in range(k):
            if nums[i] in dict_nums:
                return True
            dict_nums.add(nums[i])
            print(i, k, dict_nums)
        for i in range(k, len(nums)):
            if nums[i] in dict_nums:
                return True
            dict_nums.add(nums[i])
            print(i, k, dict_nums)
            dict_nums.discard(nums[i - k])
            print(i, k, dict_nums)
        return False

    def containsNearbyDuplicate(self, nums, k):
        """:type nums: List[int] :type k: int :rtype: bool"""
        memo = set()
        for i, n in enumerate(nums):
            if i - k > 0:
                memo.remove(nums[i - k - 1])
            if n in memo:
                return True
            memo.add(n)
        return False


evaluate

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

Class description:
Implement the Kernel class.

Method signatures and docstrings:
- def __init__(self, function, var=1, support=3): Initialize a new kernel function. function: callable, numpy.arr -> numpy.arr, should integrate to 1 expected_value : peak, typically 0 support: support of the function. Example ------- >>> from scipy.special import gamma >>> # Normalized function of x >>> def exp(x, dims=1): ... normalization = gamma(dims) * dims ... return np.exp(-x) / normalization >>> kernel = Kernel(exp, var=4, support=np.inf) >>> # The function is scaled so that the standard deviation (bw) = 1 >>> kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0] True >>> np.allclose(kernel(np.array([0, 1, 2])), kernel([0, 1, 2])) True >>> np.allclose(kernel(0), kernel([0])) True >>> np.allclose(kernel(0), kernel.evaluate(0)) True
- def practical_support(self, bw, atol=0.0001): Return the support for practical purposes. Used to find a support value for computations for kernel functions without finite (bounded) support. Examples -------- >>> kernel = Kernel(gaussian, var=1, support=np.inf) >>> kernel.practical_support(bw=1) 3.8994... >>> kernel.practical_support(bw=2) 7.4331...
- def evaluate(self, x, bw=1, norm=2): Evaluate the kernel. Parameters ---------- x : array-like Should have shape (obs, dims). bw : array-like Must have shape (obs, ), or float.

Implement the Python class `Kernel` described below.

Class description:
Implement the Kernel class.

Method signatures and docstrings:
- def __init__(self, function, var=1, support=3): Initialize a new kernel function. function: callable, numpy.arr -> numpy.arr, should integrate to 1 expected_value : peak, typically 0 support: support of the function. Example ------- >>> from scipy.special import gamma >>> # Normalized function of x >>> def exp(x, dims=1): ... normalization = gamma(dims) * dims ... return np.exp(-x) / normalization >>> kernel = Kernel(exp, var=4, support=np.inf) >>> # The function is scaled so that the standard deviation (bw) = 1 >>> kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0] True >>> np.allclose(kernel(np.array([0, 1, 2])), kernel([0, 1, 2])) True >>> np.allclose(kernel(0), kernel([0])) True >>> np.allclose(kernel(0), kernel.evaluate(0)) True
- def practical_support(self, bw, atol=0.0001): Return the support for practical purposes. Used to find a support value for computations for kernel functions without finite (bounded) support. Examples -------- >>> kernel = Kernel(gaussian, var=1, support=np.inf) >>> kernel.practical_support(bw=1) 3.8994... >>> kernel.practical_support(bw=2) 7.4331...
- def evaluate(self, x, bw=1, norm=2): Evaluate the kernel. Parameters ---------- x : array-like Should have shape (obs, dims). bw : array-like Must have shape (obs, ), or float.

<|skeleton|>
class Kernel:

    def __init__(self, function, var=1, support=3):
        """Initialize a new kernel function. function: callable, numpy.arr -> numpy.arr, should integrate to 1 expected_value : peak, typically 0 support: support of the function. Example ------- >>> from scipy.special import gamma >>> # Normalized function of x >>> def exp(x, dims=1): ... normalization = gamma(dims) * dims ... return np.exp(-x) / normalization >>> kernel = Kernel(exp, var=4, support=np.inf) >>> # The function is scaled so that the standard deviation (bw) = 1 >>> kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0] True >>> np.allclose(kernel(np.array([0, 1, 2])), kernel([0, 1, 2])) True >>> np.allclose(kernel(0), kernel([0])) True >>> np.allclose(kernel(0), kernel.evaluate(0)) True"""
        <|body_0|>

    def practical_support(self, bw, atol=0.0001):
        """Return the support for practical purposes. Used to find a support value for computations for kernel functions without finite (bounded) support. Examples -------- >>> kernel = Kernel(gaussian, var=1, support=np.inf) >>> kernel.practical_support(bw=1) 3.8994... >>> kernel.practical_support(bw=2) 7.4331..."""
        <|body_1|>

    def evaluate(self, x, bw=1, norm=2):
        """Evaluate the kernel. Parameters ---------- x : array-like Should have shape (obs, dims). bw : array-like Must have shape (obs, ), or float."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.function = function
        self.var = var
        self.finite_support = np.isfinite(support)
        self.support = support / np.sqrt(self.var)
<|end_body_0|>

<|body_start_1|>
        if self.finite_support:
            return self.support * bw
        else:

            def f(x):
                return self.evaluate(x, bw=bw) - atol
            try:
                xtol = 0.001
                ans = brentq(f, a=0, b=8 * bw, xtol=xtol, full_output=False)
                return ans + xtol
            except ValueError:
                msg = 'Unable to solve for support numerically. Use a ' + 'kernel with finite support or scale data to smaller bw.'
                raise ValueError(msg)
<|end_body_1|>

<|body_start_2|>
        if isinstance(x, numbers.Number):
            x = np.asarray_chkfinite([x])
        else:
            x = np.asarray_chkfinite(x)
        if len(x.shape) == 1:
            x = x.reshape(-1, 1)
        real_bw = bw / np.sqrt(self.var)
        obs, dims = x.shape
        volume_func = functools.partial(volume_unit_ball, p=norm)
        if dims > 1:
            distances = p_norm(x, norm).ravel()
        else:
            distances = np.abs(x).ravel()
        return self.function(distances / real_bw, dims) / (real_bw ** dims * volume_func(dims))
<|end_body_2|>

Solution:
class Kernel:
    def __init__(self, function, var=1, support=3):
        """Initialize a new kernel function. function: callable, numpy.arr -> numpy.arr, should integrate to 1 expected_value : peak, typically 0 support: support of the function. Example ------- >>> from scipy.special import gamma >>> # Normalized function of x >>> def exp(x, dims=1): ... normalization = gamma(dims) * dims ... return np.exp(-x) / normalization >>> kernel = Kernel(exp, var=4, support=np.inf) >>> # The function is scaled so that the standard deviation (bw) = 1 >>> kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0] True >>> np.allclose(kernel(np.array([0, 1, 2])), kernel([0, 1, 2])) True >>> np.allclose(kernel(0), kernel([0])) True >>> np.allclose(kernel(0), kernel.evaluate(0)) True"""
        self.function = function
        self.var = var
        self.finite_support = np.isfinite(support)
        self.support = support / np.sqrt(self.var)

    def practical_support(self, bw, atol=0.0001):
        """Return the support for practical purposes. Used to find a support value for computations for kernel functions without finite (bounded) support. Examples -------- >>> kernel = Kernel(gaussian, var=1, support=np.inf) >>> kernel.practical_support(bw=1) 3.8994... >>> kernel.practical_support(bw=2) 7.4331..."""
        if self.finite_support:
            return self.support * bw
        else:

            def f(x):
                return self.evaluate(x, bw=bw) - atol
            try:
                xtol = 0.001
                ans = brentq(f, a=0, b=8 * bw, xtol=xtol, full_output=False)
                return ans + xtol
            except ValueError:
                msg = 'Unable to solve for support numerically. Use a ' + 'kernel with finite support or scale data to smaller bw.'
                raise ValueError(msg)

    def evaluate(self, x, bw=1, norm=2):
        """Evaluate the kernel. Parameters ---------- x : array-like Should have shape (obs, dims). bw : array-like Must have shape (obs, ), or float."""
        if isinstance(x, numbers.Number):
            x = np.asarray_chkfinite([x])
        else:
            x = np.asarray_chkfinite(x)
        if len(x.shape) == 1:
            x = x.reshape(-1, 1)
        real_bw = bw / np.sqrt(self.var)
        obs, dims = x.shape
        volume_func = functools.partial(volume_unit_ball, p=norm)
        if dims > 1:
            distances = p_norm(x, norm).ravel()
        else:
            distances = np.abs(x).ravel()
        return self.function(distances / real_bw, dims) / (real_bw ** dims * volume_func(dims))


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

Class description:
A Metric.

Method signatures and docstrings:
- def get(self, name): Get a metric.
- def post(self, name): Update a metric.

Implement the Python class `MetricResource` described below.

Class description:
A Metric.

Method signatures and docstrings:
- def get(self, name): Get a metric.
- def post(self, name): Update a metric.

<|skeleton|>
class MetricResource:
    """A Metric."""

    def get(self, name):
        """Get a metric."""
        <|body_0|>

    def post(self, name):
        """Update a metric."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        m = context.accessor.get_metric(name)
        if not m:
            rp.abort(404)
        return m.as_string_dict()
<|end_body_0|>

<|body_start_1|>
        if not context.accessor.has_metric(name):
            return ("Unknown metric: '%s'" % name, 404)
        payload = request.json
        metadata = bg_metric.MetricMetadata.create(aggregator=bg_metric.Aggregator.from_config_name(payload['aggregator']), retention=bg_metric.Retention.from_string(payload['retention']), carbon_xfilesfactor=payload['carbon_xfilesfactor'])
        context.accessor.update_metric(name, metadata)
        return ('', 204)
<|end_body_1|>

Solution:
class MetricResource:
    """A Metric."""

    def get(self, name):
        """Get a metric."""
        m = context.accessor.get_metric(name)
        if not m:
            rp.abort(404)
        return m.as_string_dict()

    def post(self, name):
        """Update a metric."""
        if not context.accessor.has_metric(name):
            return ("Unknown metric: '%s'" % name, 404)
        payload = request.json
        metadata = bg_metric.MetricMetadata.create(aggregator=bg_metric.Aggregator.from_config_name(payload['aggregator']), retention=bg_metric.Retention.from_string(payload['retention']), carbon_xfilesfactor=payload['carbon_xfilesfactor'])
        context.accessor.update_metric(name, metadata)
        return ('', 204)


addSpecialCharacters

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

Class description:
class that contains basic hermite data a hermite is a subsection of the level

Method signatures and docstrings:
- def __init__(self, value, type): :param value: Value of the harmite, represents pathways. similar to neighbor sensitive tiles :param type: Type of hermite. see above for types.
- def getText(self): just a simple function to retrieve and return a text document containing the layout of the hermite. also adds an entrance/exit if the hermite is that kind of hermite
- def addSpecialCharacters(self, textList): based on the type of hermite that calls this function is, will add a special character that is needed for that hermite

Implement the Python class `Hermite` described below.

Class description:
class that contains basic hermite data a hermite is a subsection of the level

Method signatures and docstrings:
- def __init__(self, value, type): :param value: Value of the harmite, represents pathways. similar to neighbor sensitive tiles :param type: Type of hermite. see above for types.
- def getText(self): just a simple function to retrieve and return a text document containing the layout of the hermite. also adds an entrance/exit if the hermite is that kind of hermite
- def addSpecialCharacters(self, textList): based on the type of hermite that calls this function is, will add a special character that is needed for that hermite

<|skeleton|>
class Hermite:
    """class that contains basic hermite data a hermite is a subsection of the level"""

    def __init__(self, value, type):
        """:param value: Value of the harmite, represents pathways. similar to neighbor sensitive tiles :param type: Type of hermite. see above for types."""
        <|body_0|>

    def getText(self):
        """just a simple function to retrieve and return a text document containing the layout of the hermite. also adds an entrance/exit if the hermite is that kind of hermite"""
        <|body_1|>

    def addSpecialCharacters(self, textList):
        """based on the type of hermite that calls this function is, will add a special character that is needed for that hermite"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.mValue = value
        self.mType = type
        self.mSpecialChar = MapReader.FLOOR_CODE
        self.mIsSpecial = True
        if self.mType == ENTRANCE_CODE:
            self.mSpecialChar = 'e'
        elif self.mType == EXIT_CODE:
            self.mSpecialChar = 'x'
        else:
            self.mIsSpecial = False
        self.mTextList = self.getText()
<|end_body_0|>

<|body_start_1|>
        filepath = 'hermite files/' + str(self.mValue) + '/' + str(random.randint(0, len(listdir('hermite files/' + str(self.mValue))) - 1)) + '.txt'
        hermiteFile = open(filepath)
        hermiteText = []
        for line in hermiteFile:
            hermiteText.append(list(line.strip('\n\r')))
        hermiteFile.close()
        if self.mIsSpecial:
            hermiteText = self.addSpecialCharacters(hermiteText)
        return hermiteText
<|end_body_1|>

<|body_start_2|>
        lastPos = VectorN((0, 0))
        placed = False
        for r in range(len(textList)):
            for c in range(len(textList[r])):
                if textList[r][c] != MapReader.WALL_CODE:
                    lastPos = VectorN((r, c))
                    if random.random() < 0.1:
                        textList[r][c] = self.mSpecialChar
                        placed = True
                        break
            if placed:
                break
        if not placed:
            textList[lastPos.iTuple()[0]][lastPos.iTuple()[1]] = self.mSpecialChar
        return textList
<|end_body_2|>

Solution:
class Hermite:
    """class that contains basic hermite data a hermite is a subsection of the level"""

    def __init__(self, value, type):
        """:param value: Value of the harmite, represents pathways. similar to neighbor sensitive tiles :param type: Type of hermite. see above for types."""
        self.mValue = value
        self.mType = type
        self.mSpecialChar = MapReader.FLOOR_CODE
        self.mIsSpecial = True
        if self.mType == ENTRANCE_CODE:
            self.mSpecialChar = 'e'
        elif self.mType == EXIT_CODE:
            self.mSpecialChar = 'x'
        else:
            self.mIsSpecial = False
        self.mTextList = self.getText()

    def getText(self):
        """just a simple function to retrieve and return a text document containing the layout of the hermite. also adds an entrance/exit if the hermite is that kind of hermite"""
        filepath = 'hermite files/' + str(self.mValue) + '/' + str(random.randint(0, len(listdir('hermite files/' + str(self.mValue))) - 1)) + '.txt'
        hermiteFile = open(filepath)
        hermiteText = []
        for line in hermiteFile:
            hermiteText.append(list(line.strip('\n\r')))
        hermiteFile.close()
        if self.mIsSpecial:
            hermiteText = self.addSpecialCharacters(hermiteText)
        return hermiteText

    def addSpecialCharacters(self, textList):
        """based on the type of hermite that calls this function is, will add a special character that is needed for that hermite"""
        lastPos = VectorN((0, 0))
        placed = False
        for r in range(len(textList)):
            for c in range(len(textList[r])):
                if textList[r][c] != MapReader.WALL_CODE:
                    lastPos = VectorN((r, c))
                    if random.random() < 0.1:
                        textList[r][c] = self.mSpecialChar
                        placed = True
                        break
            if placed:
                break
        if not placed:
            textList[lastPos.iTuple()[0]][lastPos.iTuple()[1]] = self.mSpecialChar
        return textList


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

Class description:
Implement the Test class.

Method signatures and docstrings:
- def test1(self): head = [3,2,0,-4], pos = 1 为了表示给定链表中的环，我们使用整数 pos 来表示链表尾连接到链表中的位置（索引从 0 开始） 如果 pos 是 -1，则在该链表中没有环
- def test2(self): [-21,10,17,8,4,26,5,35,33,-7,-16,27,-12,6,29,-12,5,9,20,14,14,2,13,-24,21,23,-21,5] -1

Implement the Python class `Test` described below.

Class description:
Implement the Test class.

Method signatures and docstrings:
- def test1(self): head = [3,2,0,-4], pos = 1 为了表示给定链表中的环，我们使用整数 pos 来表示链表尾连接到链表中的位置（索引从 0 开始） 如果 pos 是 -1，则在该链表中没有环
- def test2(self): [-21,10,17,8,4,26,5,35,33,-7,-16,27,-12,6,29,-12,5,9,20,14,14,2,13,-24,21,23,-21,5] -1

<|skeleton|>
class Test:

    def test1(self):
        """head = [3,2,0,-4], pos = 1 为了表示给定链表中的环，我们使用整数 pos 来表示链表尾连接到链表中的位置（索引从 0 开始） 如果 pos 是 -1，则在该链表中没有环"""
        <|body_0|>

    def test2(self):
        """[-21,10,17,8,4,26,5,35,33,-7,-16,27,-12,6,29,-12,5,9,20,14,14,2,13,-24,21,23,-21,5] -1"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        _1 = ListNode(3)
        _2 = ListNode(2)
        _3 = ListNode(0)
        _4 = ListNode(-4)
        _1.next = _2
        _2.next = _3
        _3.next = _4
        _4.next = _2
        s = Solution()
        self.assertTrue(s.hasCycle(_1))
<|end_body_0|>

<|body_start_1|>
        l = [-21, 10, 17, 8, 4, 26, 5, 35, 33, -7, -16, 27, -12, 6, 29, -12, 5, 9, 20, 14, 14, 2, 13, -24, 21, 23, -21, 5]
        n = len(l)
        head = ListNode(l[0])
        node = head
        for i in range(n - 1):
            node.next = ListNode(l[i + 1])
            node = node.next
        s = Solution()
        self.assertFalse(s.hasCycle(head))
<|end_body_1|>

Solution:
class Test:
    def test1(self):
        """head = [3,2,0,-4], pos = 1 为了表示给定链表中的环，我们使用整数 pos 来表示链表尾连接到链表中的位置（索引从 0 开始） 如果 pos 是 -1，则在该链表中没有环"""
        _1 = ListNode(3)
        _2 = ListNode(2)
        _3 = ListNode(0)
        _4 = ListNode(-4)
        _1.next = _2
        _2.next = _3
        _3.next = _4
        _4.next = _2
        s = Solution()
        self.assertTrue(s.hasCycle(_1))

    def test2(self):
        """[-21,10,17,8,4,26,5,35,33,-7,-16,27,-12,6,29,-12,5,9,20,14,14,2,13,-24,21,23,-21,5] -1"""
        l = [-21, 10, 17, 8, 4, 26, 5, 35, 33, -7, -16, 27, -12, 6, 29, -12, 5, 9, 20, 14, 14, 2, 13, -24, 21, 23, -21, 5]
        n = len(l)
        head = ListNode(l[0])
        node = head
        for i in range(n - 1):
            node.next = ListNode(l[i + 1])
            node = node.next
        s = Solution()
        self.assertFalse(s.hasCycle(head))


tokenize

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

Class description:
Runs basic tokenization with Janome morphological parser.

Method signatures and docstrings:
- def __init__(self, *, do_lower_case=False, never_split=None, normalize_text=True): Construct a JanomeTokenizer. :arg do_lower_case: (`optional`) boolean (default True) Whether to lower case the input. :arg never_split: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of token not to split. :arg normalize_text: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization.
- def tokenize(self, text: str, *, never_split=None, **kwargs): Tokenizes a piece of text.

Implement the Python class `JanomeTokenizer` described below.

Class description:
Runs basic tokenization with Janome morphological parser.

Method signatures and docstrings:
- def __init__(self, *, do_lower_case=False, never_split=None, normalize_text=True): Construct a JanomeTokenizer. :arg do_lower_case: (`optional`) boolean (default True) Whether to lower case the input. :arg never_split: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of token not to split. :arg normalize_text: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization.
- def tokenize(self, text: str, *, never_split=None, **kwargs): Tokenizes a piece of text.

<|skeleton|>
class JanomeTokenizer:
    """Runs basic tokenization with Janome morphological parser."""

    def __init__(self, *, do_lower_case=False, never_split=None, normalize_text=True):
        """Construct a JanomeTokenizer. :arg do_lower_case: (`optional`) boolean (default True) Whether to lower case the input. :arg never_split: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of token not to split. :arg normalize_text: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization."""
        <|body_0|>

    def tokenize(self, text: str, *, never_split=None, **kwargs):
        """Tokenizes a piece of text."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.do_lower_case = do_lower_case
        self.never_split = never_split if never_split is not None else []
        self.normalize_text = normalize_text
        self.janome_tokenizer = Tokenizer()
<|end_body_0|>

<|body_start_1|>
        if self.normalize_text:
            text = unicodedata.normalize('NFKC', text)
        never_split = self.never_split + (never_split if never_split is not None else [])
        tokens = self.janome_tokenizer.tokenize(text)
        __tokens = []
        last_index = 0
        for t in tokens:
            token = t.surface
            token_start = text.index(token, last_index)
            if last_index != token_start:
                __tokens.append(text[last_index:token_start])
            if self.do_lower_case and token not in never_split:
                token = token.lower()
                __tokens.append(token.lower())
            else:
                __tokens.append(token)
            last_index = token_start + len(token)
        if len(text) != last_index:
            __tokens.append(text[last_index:])
        assert text == ''.join(__tokens), f"[{text}] != [{''.join(__tokens)}]"
        return __tokens
<|end_body_1|>

Solution:
class JanomeTokenizer:
    """Runs basic tokenization with Janome morphological parser."""

    def __init__(self, *, do_lower_case=False, never_split=None, normalize_text=True):
        """Construct a JanomeTokenizer. :arg do_lower_case: (`optional`) boolean (default True) Whether to lower case the input. :arg never_split: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of token not to split. :arg normalize_text: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization."""
        self.do_lower_case = do_lower_case
        self.never_split = never_split if never_split is not None else []
        self.normalize_text = normalize_text
        self.janome_tokenizer = Tokenizer()

    def tokenize(self, text: str, *, never_split=None, **kwargs):
        """Tokenizes a piece of text."""
        if self.normalize_text:
            text = unicodedata.normalize('NFKC', text)
        never_split = self.never_split + (never_split if never_split is not None else [])
        tokens = self.janome_tokenizer.tokenize(text)
        __tokens = []
        last_index = 0
        for t in tokens:
            token = t.surface
            token_start = text.index(token, last_index)
            if last_index != token_start:
                __tokens.append(text[last_index:token_start])
            if self.do_lower_case and token not in never_split:
                token = token.lower()
                __tokens.append(token.lower())
            else:
                __tokens.append(token)
            last_index = token_start + len(token)
        if len(text) != last_index:
            __tokens.append(text[last_index:])
        assert text == ''.join(__tokens), f"[{text}] != [{''.join(__tokens)}]"
        return __tokens