provenance

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

Class description:
Implement the clean_trees class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets and store in mongodb collections.
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

Implement the Python class `clean_trees` described below.

Class description:
Implement the clean_trees class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets and store in mongodb collections.
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

<|skeleton|>
class clean_trees:

    def execute(trial=False):
        """Retrieve some data sets and store in mongodb collections."""
        <|body_0|>

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        repo.dropCollection('ajr10_williami.cleaned_trees_cambridge')
        repo.createCollection('ajr10_williami.cleaned_trees_cambridge')
        repo.dropCollection('ajr10_williami.cleaned_trees_boston')
        repo.createCollection('ajr10_williami.cleaned_trees_boston')
        trees_cambridge = repo['ajr10_williami.trees_cambridge'].find()
        trees_boston = repo['ajr10_williami.trees_boston'].find().limit(50)
        for cambridge_tree in trees_cambridge:
            coords = cambridge_tree['the_geom']['coordinates']
            new_tree = {}
            new_tree['longitude'] = coords[0]
            new_tree['latitude'] = coords[1]
            repo['ajr10_williami.cleaned_trees_cambridge'].insert(new_tree)
        for boston_tree in trees_boston:
            coords = boston_tree['geometry']['coordinates']
            new_tree = {}
            new_tree['longitude'] = coords[0]
            new_tree['latitude'] = coords[1]
            repo['ajr10_williami.cleaned_trees_boston'].insert(new_tree)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}
<|end_body_0|>

<|body_start_1|>
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('awc', 'ajr10_williami')
        this_script = doc.agent('alg:ajr10_williami#clean_trees', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        trees_cambridge_resource = doc.entity('awc:trees_cambridge', {'prov:label': 'cleaned_trees_cambridge, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        trees_boston_resource = doc.entity('awc:trees_boston', {'prov:label': 'cleaned_trees_boston, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        get_trees_cambridge = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        get_trees_boston = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_trees_cambridge, this_script)
        doc.wasAssociatedWith(get_trees_boston, this_script)
        doc.usage(get_trees_cambridge, trees_cambridge_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Clean+Trees+Cambridge'})
        doc.usage(get_trees_boston, trees_boston_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Clean+Trees+Boston'})
        clean_trees_cambridge = doc.entity('dat:ajr10_williami#cleaned_trees_cambridge', {prov.model.PROV_LABEL: 'Cleaned Trees Cambridge', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(clean_trees_cambridge, this_script)
        doc.wasGeneratedBy(clean_trees_cambridge, get_trees_cambridge, endTime)
        doc.wasDerivedFrom(clean_trees_cambridge, trees_cambridge_resource, get_trees_cambridge, get_trees_cambridge, get_trees_cambridge)
        clean_trees_boston = doc.entity('dat:ajr10_williami#cleaned_trees_boston', {prov.model.PROV_LABEL: 'Cleaned Trees Boston', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(clean_trees_boston, this_script)
        doc.wasGeneratedBy(clean_trees_boston, get_trees_boston, endTime)
        doc.wasDerivedFrom(clean_trees_boston, trees_boston_resource, get_trees_boston, get_trees_boston, get_trees_boston)
        repo.logout()
        return doc
<|end_body_1|>

Solution:
class clean_trees:
    def execute(trial=False):
        """Retrieve some data sets and store in mongodb collections."""
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        repo.dropCollection('ajr10_williami.cleaned_trees_cambridge')
        repo.createCollection('ajr10_williami.cleaned_trees_cambridge')
        repo.dropCollection('ajr10_williami.cleaned_trees_boston')
        repo.createCollection('ajr10_williami.cleaned_trees_boston')
        trees_cambridge = repo['ajr10_williami.trees_cambridge'].find()
        trees_boston = repo['ajr10_williami.trees_boston'].find().limit(50)
        for cambridge_tree in trees_cambridge:
            coords = cambridge_tree['the_geom']['coordinates']
            new_tree = {}
            new_tree['longitude'] = coords[0]
            new_tree['latitude'] = coords[1]
            repo['ajr10_williami.cleaned_trees_cambridge'].insert(new_tree)
        for boston_tree in trees_boston:
            coords = boston_tree['geometry']['coordinates']
            new_tree = {}
            new_tree['longitude'] = coords[0]
            new_tree['latitude'] = coords[1]
            repo['ajr10_williami.cleaned_trees_boston'].insert(new_tree)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('awc', 'ajr10_williami')
        this_script = doc.agent('alg:ajr10_williami#clean_trees', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        trees_cambridge_resource = doc.entity('awc:trees_cambridge', {'prov:label': 'cleaned_trees_cambridge, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        trees_boston_resource = doc.entity('awc:trees_boston', {'prov:label': 'cleaned_trees_boston, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        get_trees_cambridge = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        get_trees_boston = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_trees_cambridge, this_script)
        doc.wasAssociatedWith(get_trees_boston, this_script)
        doc.usage(get_trees_cambridge, trees_cambridge_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Clean+Trees+Cambridge'})
        doc.usage(get_trees_boston, trees_boston_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Clean+Trees+Boston'})
        clean_trees_cambridge = doc.entity('dat:ajr10_williami#cleaned_trees_cambridge', {prov.model.PROV_LABEL: 'Cleaned Trees Cambridge', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(clean_trees_cambridge, this_script)
        doc.wasGeneratedBy(clean_trees_cambridge, get_trees_cambridge, endTime)
        doc.wasDerivedFrom(clean_trees_cambridge, trees_cambridge_resource, get_trees_cambridge, get_trees_cambridge, get_trees_cambridge)
        clean_trees_boston = doc.entity('dat:ajr10_williami#cleaned_trees_boston', {prov.model.PROV_LABEL: 'Cleaned Trees Boston', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(clean_trees_boston, this_script)
        doc.wasGeneratedBy(clean_trees_boston, get_trees_boston, endTime)
        doc.wasDerivedFrom(clean_trees_boston, trees_boston_resource, get_trees_boston, get_trees_boston, get_trees_boston)
        repo.logout()
        return doc


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isPowerOfThree(self, n): :type n: int :rtype: bool
- def isPowerOfThree2(self, n): :type n: int :rtype: bool

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isPowerOfThree(self, n): :type n: int :rtype: bool
- def isPowerOfThree2(self, n): :type n: int :rtype: bool

<|skeleton|>
class Solution:

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

    def isPowerOfThree2(self, n):
        """:type n: int :rtype: bool"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        left, right = (0, 19)
        while left <= right:
            mid = left + (right - left) / 2
            if 3 ** mid == n:
                return True
            elif 3 ** mid > n:
                right = mid - 1
            else:
                left = mid + 1
        return False
<|end_body_0|>

<|body_start_1|>
        while n > 1 and n % 3 == 0:
            n /= 3
        return n == 1
<|end_body_1|>

Solution:
class Solution:
    def isPowerOfThree(self, n):
        """:type n: int :rtype: bool"""
        left, right = (0, 19)
        while left <= right:
            mid = left + (right - left) / 2
            if 3 ** mid == n:
                return True
            elif 3 ** mid > n:
                right = mid - 1
            else:
                left = mid + 1
        return False

    def isPowerOfThree2(self, n):
        """:type n: int :rtype: bool"""
        while n > 1 and n % 3 == 0:
            n /= 3
        return n == 1


nextGreatestLetter

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def nextGreatestLetter(self, letters: List[str], target: str) -> str: Sequential Search: O(n), Space: O(1)
- def nextGreatestLetter(self, letters: List[str], target: str) -> str: Binary Search: O(logn), Space: O(1)

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def nextGreatestLetter(self, letters: List[str], target: str) -> str: Sequential Search: O(n), Space: O(1)
- def nextGreatestLetter(self, letters: List[str], target: str) -> str: Binary Search: O(logn), Space: O(1)

<|skeleton|>
class Solution:

    def nextGreatestLetter(self, letters: List[str], target: str) -> str:
        """Sequential Search: O(n), Space: O(1)"""
        <|body_0|>

    def nextGreatestLetter(self, letters: List[str], target: str) -> str:
        """Binary Search: O(logn), Space: O(1)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for letter in letters:
            if letter > target:
                return letter
        return letters[0]
<|end_body_0|>

<|body_start_1|>
        l, r = (0, len(letters))
        while l < r:
            m = l + (r - l) // 2
            if letters[m] > target:
                r = m
            else:
                l = m + 1
        if l == len(letters):
            return letters[0]
        return letters[l]
<|end_body_1|>

Solution:
class Solution:
    def nextGreatestLetter(self, letters: List[str], target: str) -> str:
        """Sequential Search: O(n), Space: O(1)"""
        for letter in letters:
            if letter > target:
                return letter
        return letters[0]

    def nextGreatestLetter(self, letters: List[str], target: str) -> str:
        """Binary Search: O(logn), Space: O(1)"""
        l, r = (0, len(letters))
        while l < r:
            m = l + (r - l) // 2
            if letters[m] > target:
                r = m
            else:
                l = m + 1
        if l == len(letters):
            return letters[0]
        return letters[l]

python/744-Find Smallest Letter Greater Than Target.py


call

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

Class description:
Decode for machine translation

Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): vocab is an integer representing the size of the decode_out vocabulary embedding is an integer representing the dimensionality of the embedding vector units is an integer representing the number of hidden units in the RNN cell batch is an integer representing the batch size
- def call(self, x, s_prev, hidden_states): x is a tensor of shape (batch, 1) containing the previous word in the target sequence as an index of the target vocabulary. s_prev is a tensor of shape (batch, units) containing the previous decoder hidden state. hidden_states is a tensor of shape (batch, input_seq_len, units) containing the outputs of the encoder.

Implement the Python class `RNNDecoder` described below.

Class description:
Decode for machine translation

Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): vocab is an integer representing the size of the decode_out vocabulary embedding is an integer representing the dimensionality of the embedding vector units is an integer representing the number of hidden units in the RNN cell batch is an integer representing the batch size
- def call(self, x, s_prev, hidden_states): x is a tensor of shape (batch, 1) containing the previous word in the target sequence as an index of the target vocabulary. s_prev is a tensor of shape (batch, units) containing the previous decoder hidden state. hidden_states is a tensor of shape (batch, input_seq_len, units) containing the outputs of the encoder.

<|skeleton|>
class RNNDecoder:
    """Decode for machine translation"""

    def __init__(self, vocab, embedding, units, batch):
        """vocab is an integer representing the size of the decode_out vocabulary embedding is an integer representing the dimensionality of the embedding vector units is an integer representing the number of hidden units in the RNN cell batch is an integer representing the batch size"""
        <|body_0|>

    def call(self, x, s_prev, hidden_states):
        """x is a tensor of shape (batch, 1) containing the previous word in the target sequence as an index of the target vocabulary. s_prev is a tensor of shape (batch, units) containing the previous decoder hidden state. hidden_states is a tensor of shape (batch, input_seq_len, units) containing the outputs of the encoder."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.embedding = tf.keras.layers.Embedding(vocab, embedding)
        self.gru = tf.keras.layers.GRU(units, recurrent_initializer='glorot_uniform', return_sequences=True, return_state=True)
        self.F = tf.keras.layers.Dense(vocab)
<|end_body_0|>

<|body_start_1|>
        attention = SelfAttention(s_prev.shape[1])
        context, weights = attention(s_prev, hidden_states)
        x = self.embedding(x)
        x = tf.concat([tf.expand_dims(context, 1), x], -1)
        decode_out, state = self.gru(x)
        decode_out = tf.reshape(decode_out, (-1, decode_out.shape[2]))
        y = self.F(decode_out)
        return (y, state)
<|end_body_1|>

Solution:
class RNNDecoder:
    """Decode for machine translation"""

    def __init__(self, vocab, embedding, units, batch):
        """vocab is an integer representing the size of the decode_out vocabulary embedding is an integer representing the dimensionality of the embedding vector units is an integer representing the number of hidden units in the RNN cell batch is an integer representing the batch size"""
        super().__init__()
        self.embedding = tf.keras.layers.Embedding(vocab, embedding)
        self.gru = tf.keras.layers.GRU(units, recurrent_initializer='glorot_uniform', return_sequences=True, return_state=True)
        self.F = tf.keras.layers.Dense(vocab)

    def call(self, x, s_prev, hidden_states):
        """x is a tensor of shape (batch, 1) containing the previous word in the target sequence as an index of the target vocabulary. s_prev is a tensor of shape (batch, units) containing the previous decoder hidden state. hidden_states is a tensor of shape (batch, input_seq_len, units) containing the outputs of the encoder."""
        attention = SelfAttention(s_prev.shape[1])
        context, weights = attention(s_prev, hidden_states)
        x = self.embedding(x)
        x = tf.concat([tf.expand_dims(context, 1), x], -1)
        decode_out, state = self.gru(x)
        decode_out = tf.reshape(decode_out, (-1, decode_out.shape[2]))
        y = self.F(decode_out)
        return (y, state)


binarysearch

Prompt:
Implement the Python class `Solution_A2` described below.

Class description:
Implement the Solution_A2 class.

Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> bool: 本质上是与VersionA1同一个意思,只是少一个helper判断两段是否是sorted. 把这个Helper的判断直接引入到了递归判断之中. 代码更简洁,但是缺点是,判断不够准确导致可能要连续对两段都使用search,递归层数增多
- def binarysearch(self, nums: List[int], target: int) -> bool: Binary search in sorted array

Implement the Python class `Solution_A2` described below.

Class description:
Implement the Solution_A2 class.

Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> bool: 本质上是与VersionA1同一个意思,只是少一个helper判断两段是否是sorted. 把这个Helper的判断直接引入到了递归判断之中. 代码更简洁,但是缺点是,判断不够准确导致可能要连续对两段都使用search,递归层数增多
- def binarysearch(self, nums: List[int], target: int) -> bool: Binary search in sorted array

<|skeleton|>
class Solution_A2:

    def search(self, nums: List[int], target: int) -> bool:
        """本质上是与VersionA1同一个意思,只是少一个helper判断两段是否是sorted. 把这个Helper的判断直接引入到了递归判断之中. 代码更简洁,但是缺点是,判断不够准确导致可能要连续对两段都使用search,递归层数增多"""
        <|body_0|>

    def binarysearch(self, nums: List[int], target: int) -> bool:
        """Binary search in sorted array"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if len(nums) <= 2:
            return target in nums
        else:
            L = 0
            H = len(nums) - 1
            M = (L + H) // 2
            Lo = nums[L]
            Hi = nums[H]
            Mid = nums[M]
            if Lo <= Mid <= Hi:
                return self.search(nums[L:M], target) or self.search(nums[M:], target)
            elif Lo > Mid and Mid <= Hi:
                return self.search(nums[L:M], target) or self.binarysearch(nums[M:], target)
            elif Lo <= Mid and Mid > Hi:
                return self.binarysearch(nums[L:M + 1], target) or self.search(nums[M + 1:], target)
<|end_body_0|>

<|body_start_1|>
        L = 0
        H = len(nums) - 1
        while L <= H:
            M = (L + H) // 2
            if nums[M] == target:
                return True
            elif nums[M] < target:
                L = M + 1
            else:
                H = M - 1
        return False
<|end_body_1|>

Solution:
class Solution_A2:
    def search(self, nums: List[int], target: int) -> bool:
        """本质上是与VersionA1同一个意思,只是少一个helper判断两段是否是sorted. 把这个Helper的判断直接引入到了递归判断之中. 代码更简洁,但是缺点是,判断不够准确导致可能要连续对两段都使用search,递归层数增多"""
        if len(nums) <= 2:
            return target in nums
        else:
            L = 0
            H = len(nums) - 1
            M = (L + H) // 2
            Lo = nums[L]
            Hi = nums[H]
            Mid = nums[M]
            if Lo <= Mid <= Hi:
                return self.search(nums[L:M], target) or self.search(nums[M:], target)
            elif Lo > Mid and Mid <= Hi:
                return self.search(nums[L:M], target) or self.binarysearch(nums[M:], target)
            elif Lo <= Mid and Mid > Hi:
                return self.binarysearch(nums[L:M + 1], target) or self.search(nums[M + 1:], target)

    def binarysearch(self, nums: List[int], target: int) -> bool:
        """Binary search in sorted array"""
        L = 0
        H = len(nums) - 1
        while L <= H:
            M = (L + H) // 2
            if nums[M] == target:
                return True
            elif nums[M] < target:
                L = M + 1
            else:
                H = M - 1
        return False


call

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

Class description:
class

Method signatures and docstrings:
- def __init__(self, dm, h, hidden, rate=0.1): constructor
- def call(self, x, enc_output, training, look_ahead_mask, padding_mask): method

Implement the Python class `DecoderBlock` described below.

Class description:
class

Method signatures and docstrings:
- def __init__(self, dm, h, hidden, rate=0.1): constructor
- def call(self, x, enc_output, training, look_ahead_mask, padding_mask): method

<|skeleton|>
class DecoderBlock:
    """class"""

    def __init__(self, dm, h, hidden, rate=0.1):
        """constructor"""
        <|body_0|>

    def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
        """method"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(DecoderBlock, self).__init__()
        self.mha1 = MultiHeadAttention(dm, h)
        self.mha2 = MultiHeadAttention(dm, h)
        self.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')
        self.dense_output = tf.keras.layers.Dense(dm)
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
        self.dropout3 = tf.keras.layers.Dropout(rate)
<|end_body_0|>

<|body_start_1|>
        attn1, _ = self.mha1(x, x, x, look_ahead_mask)
        attn1 = self.dropout1(attn1, training=training)
        out1 = self.layernorm1(attn1 + x)
        attn2, _ = self.mha2(out1, enc_output, enc_output, padding_mask)
        attn2 = self.dropout2(attn2, training=training)
        out2 = self.layernorm2(attn2 + out1)
        f_hidden = self.dense_hidden(out2)
        f_output = self.dense_output(f_hidden)
        f_output = self.dropout3(f_output, training=training)
        out3 = self.layernorm3(f_output + out2)
        return out3
<|end_body_1|>

Solution:
class DecoderBlock:
    """class"""

    def __init__(self, dm, h, hidden, rate=0.1):
        """constructor"""
        super(DecoderBlock, self).__init__()
        self.mha1 = MultiHeadAttention(dm, h)
        self.mha2 = MultiHeadAttention(dm, h)
        self.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')
        self.dense_output = tf.keras.layers.Dense(dm)
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
        self.dropout3 = tf.keras.layers.Dropout(rate)

    def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
        """method"""
        attn1, _ = self.mha1(x, x, x, look_ahead_mask)
        attn1 = self.dropout1(attn1, training=training)
        out1 = self.layernorm1(attn1 + x)
        attn2, _ = self.mha2(out1, enc_output, enc_output, padding_mask)
        attn2 = self.dropout2(attn2, training=training)
        out2 = self.layernorm2(attn2 + out1)
        f_hidden = self.dense_hidden(out2)
        f_output = self.dense_output(f_hidden)
        f_output = self.dropout3(f_output, training=training)
        out3 = self.layernorm3(f_output + out2)
        return out3


parse

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

Class description:
Beautylish Products Spider

Method signatures and docstrings:
- def parse(self, response): Extract product links, follow them and go to next page if exists @url https://www.beautylish.com/shop/browse @returns requests 1 @returns items 0 0
- def parse_product(self, response): Extract product details @url https://www.beautylish.com/s/jeffree-star-cosmetics-holographic-makeup-bag-black @returns requests 1 1
- def parse_reviews(self, response): Extract reviews data
- def build_review_url(self, cipherid, offset=0, limit=20, sort='helpful'): Build review url from cipherid
- def extract_review(self, selector): Extract review
- def extract_reviewer(self, selector): Extract reviewer

Implement the Python class `BeautylishProductsSpider` described below.

Class description:
Beautylish Products Spider

Method signatures and docstrings:
- def parse(self, response): Extract product links, follow them and go to next page if exists @url https://www.beautylish.com/shop/browse @returns requests 1 @returns items 0 0
- def parse_product(self, response): Extract product details @url https://www.beautylish.com/s/jeffree-star-cosmetics-holographic-makeup-bag-black @returns requests 1 1
- def parse_reviews(self, response): Extract reviews data
- def build_review_url(self, cipherid, offset=0, limit=20, sort='helpful'): Build review url from cipherid
- def extract_review(self, selector): Extract review
- def extract_reviewer(self, selector): Extract reviewer

<|skeleton|>
class BeautylishProductsSpider:
    """Beautylish Products Spider"""

    def parse(self, response):
        """Extract product links, follow them and go to next page if exists @url https://www.beautylish.com/shop/browse @returns requests 1 @returns items 0 0"""
        <|body_0|>

    def parse_product(self, response):
        """Extract product details @url https://www.beautylish.com/s/jeffree-star-cosmetics-holographic-makeup-bag-black @returns requests 1 1"""
        <|body_1|>

    def parse_reviews(self, response):
        """Extract reviews data"""
        <|body_2|>

    def build_review_url(self, cipherid, offset=0, limit=20, sort='helpful'):
        """Build review url from cipherid"""
        <|body_3|>

    def extract_review(self, selector):
        """Extract review"""
        <|body_4|>

    def extract_reviewer(self, selector):
        """Extract reviewer"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        products = response.xpath('//a[@class="tile"]')
        for product in products:
            href = product.xpath('@href').extract_first()
            yield response.follow(href, callback=self.parse_product)
        next_page = response.xpath('//span[@class="pager_next"]/a')
        if next_page:
            href = next_page.xpath('@href').extract_first()
            yield response.follow(href)
<|end_body_0|>

<|body_start_1|>
        encoded = response.xpath('//script').re_first('window.scriptCtx = "([^"]+)"')
        decoded = base64.b64decode(encoded)
        data = json.loads(decoded)
        product_loader = ProductItemLoader(ProductItem(), response)
        product_loader.add_xpath('gtin', '//span[@itemprop="gtin13"]')
        product_loader.add_xpath('name', '//h1[@itemprop="name"]')
        product_loader.add_xpath('brandLogo', '//h3[@itemprop="brand"]/link[@itemprop="logo"]/@href')
        product_loader.add_xpath('brand', '//h3[@itemprop="brand"]/a[@itemprop="name"]')
        product_loader.add_xpath('category', '//ul[@itemprop="breadcrumb"]/li')
        product_loader.add_xpath('rating', '//meta[@itemprop="ratingValue"]/@content')
        product_loader.add_xpath('reviewCount', '//meta[@itemprop="reviewCount"]/@content')
        product_loader.add_xpath('priceCurrency', '//meta[@itemprop="priceCurrency"]/@content')
        product_loader.add_xpath('price', '//div[@itemprop="price"]')
        product_loader.add_xpath('image', '//img[@itemprop="image"]/@src')
        product_loader.add_xpath('ingredients', '//div[@id="accord-ingredients"]')
        product_loader.add_xpath('availability', '//link[@itemprop="availability"]/@href')
        product_loader.add_xpath('shipping', '//span[@class="img"][span[contains(@class, "shipIcon_time")]]/following-sibling::div[@class="body"]')
        product_loader.add_xpath('returnPolicy', '//span[@class="img"][span[contains(@class, "shipIcon_returns")]]/following-sibling::div[@class="body"]')
        product_loader.add_xpath('description', '//div[@id="desc-accord-content"]')
        product_loader.add_value('url', response.url)
        product = product_loader.load_item()
        if product['reviewCount']:
            cipherid = data['ProductApp']['product']['cipheredId']
            return scrapy.Request(self.build_review_url(cipherid), callback=self.parse_reviews, meta={'product': product, 'cipherid': cipherid})
        return product
<|end_body_1|>

<|body_start_2|>
        product = response.meta['product']
        cipherid = response.meta['cipherid']
        reviews = json.loads(response.body)
        if not reviews:
            return product
        product['reviews'] = product.get('reviews') or []
        for each in reviews:
            review = self.extract_review(each)
            review['reviewer'] = self.extract_reviewer(each)
            product['reviews'].append(review)
        limit = response.meta.get('limit', 20)
        offset = response.meta.get('offset', 0) + limit
        sort = response.meta.get('sort', 'helpful')
        return scrapy.Request(self.build_review_url(cipherid, offset, limit, sort), callback=self.parse_reviews, meta={'product': product, 'cipherid': cipherid, 'offset': offset, 'limit': limit})
<|end_body_2|>

<|body_start_3|>
        params = {'offset': offset, 'limit': limit, 'sort': sort}
        query = urllib.urlencode(params)
        return 'https://www.beautylish.com/rest/reviews/p-{cipherid}?{query}'.format(cipherid=cipherid, query=query)
<|end_body_3|>

<|body_start_4|>
        review_loader = ReviewItemLoader(ReviewItem())
        review_loader.add_value('title', selector['shortText'])
        review_loader.add_value('description', selector['text'])
        review_loader.add_value('rating', selector['rating'])
        review_loader.add_value('helpfulCount', selector['likesCount'])
        review_loader.add_value('reviewImage', selector['images'][0]['clUrl'] if selector['images'] else None)
        review_loader.add_value('datePublished', selector['isoDate'])
        return review_loader.load_item()
<|end_body_4|>

<|body_start_5|>
        reviewer_loader = ReviewerItemLoader(ReviewerItem())
        reviewer_loader.add_value('name', selector['userDisplayName'])
        reviewer_loader.add_value('profileUrl', selector['userUrl'])
        return reviewer_loader.load_item()
<|end_body_5|>

Solution:
class BeautylishProductsSpider:
    """Beautylish Products Spider"""

    def parse(self, response):
        """Extract product links, follow them and go to next page if exists @url https://www.beautylish.com/shop/browse @returns requests 1 @returns items 0 0"""
        products = response.xpath('//a[@class="tile"]')
        for product in products:
            href = product.xpath('@href').extract_first()
            yield response.follow(href, callback=self.parse_product)
        next_page = response.xpath('//span[@class="pager_next"]/a')
        if next_page:
            href = next_page.xpath('@href').extract_first()
            yield response.follow(href)

    def parse_product(self, response):
        """Extract product details @url https://www.beautylish.com/s/jeffree-star-cosmetics-holographic-makeup-bag-black @returns requests 1 1"""
        encoded = response.xpath('//script').re_first('window.scriptCtx = "([^"]+)"')
        decoded = base64.b64decode(encoded)
        data = json.loads(decoded)
        product_loader = ProductItemLoader(ProductItem(), response)
        product_loader.add_xpath('gtin', '//span[@itemprop="gtin13"]')
        product_loader.add_xpath('name', '//h1[@itemprop="name"]')
        product_loader.add_xpath('brandLogo', '//h3[@itemprop="brand"]/link[@itemprop="logo"]/@href')
        product_loader.add_xpath('brand', '//h3[@itemprop="brand"]/a[@itemprop="name"]')
        product_loader.add_xpath('category', '//ul[@itemprop="breadcrumb"]/li')
        product_loader.add_xpath('rating', '//meta[@itemprop="ratingValue"]/@content')
        product_loader.add_xpath('reviewCount', '//meta[@itemprop="reviewCount"]/@content')
        product_loader.add_xpath('priceCurrency', '//meta[@itemprop="priceCurrency"]/@content')
        product_loader.add_xpath('price', '//div[@itemprop="price"]')
        product_loader.add_xpath('image', '//img[@itemprop="image"]/@src')
        product_loader.add_xpath('ingredients', '//div[@id="accord-ingredients"]')
        product_loader.add_xpath('availability', '//link[@itemprop="availability"]/@href')
        product_loader.add_xpath('shipping', '//span[@class="img"][span[contains(@class, "shipIcon_time")]]/following-sibling::div[@class="body"]')
        product_loader.add_xpath('returnPolicy', '//span[@class="img"][span[contains(@class, "shipIcon_returns")]]/following-sibling::div[@class="body"]')
        product_loader.add_xpath('description', '//div[@id="desc-accord-content"]')
        product_loader.add_value('url', response.url)
        product = product_loader.load_item()
        if product['reviewCount']:
            cipherid = data['ProductApp']['product']['cipheredId']
            return scrapy.Request(self.build_review_url(cipherid), callback=self.parse_reviews, meta={'product': product, 'cipherid': cipherid})
        return product

    def parse_reviews(self, response):
        """Extract reviews data"""
        product = response.meta['product']
        cipherid = response.meta['cipherid']
        reviews = json.loads(response.body)
        if not reviews:
            return product
        product['reviews'] = product.get('reviews') or []
        for each in reviews:
            review = self.extract_review(each)
            review['reviewer'] = self.extract_reviewer(each)
            product['reviews'].append(review)
        limit = response.meta.get('limit', 20)
        offset = response.meta.get('offset', 0) + limit
        sort = response.meta.get('sort', 'helpful')
        return scrapy.Request(self.build_review_url(cipherid, offset, limit, sort), callback=self.parse_reviews, meta={'product': product, 'cipherid': cipherid, 'offset': offset, 'limit': limit})

    def build_review_url(self, cipherid, offset=0, limit=20, sort='helpful'):
        """Build review url from cipherid"""
        params = {'offset': offset, 'limit': limit, 'sort': sort}
        query = urllib.urlencode(params)
        return 'https://www.beautylish.com/rest/reviews/p-{cipherid}?{query}'.format(cipherid=cipherid, query=query)

    def extract_review(self, selector):
        """Extract review"""
        review_loader = ReviewItemLoader(ReviewItem())
        review_loader.add_value('title', selector['shortText'])
        review_loader.add_value('description', selector['text'])
        review_loader.add_value('rating', selector['rating'])
        review_loader.add_value('helpfulCount', selector['likesCount'])
        review_loader.add_value('reviewImage', selector['images'][0]['clUrl'] if selector['images'] else None)
        review_loader.add_value('datePublished', selector['isoDate'])
        return review_loader.load_item()

    def extract_reviewer(self, selector):
        """Extract reviewer"""
        reviewer_loader = ReviewerItemLoader(ReviewerItem())
        reviewer_loader.add_value('name', selector['userDisplayName'])
        reviewer_loader.add_value('profileUrl', selector['userUrl'])
        return reviewer_loader.load_item()


FromErrorPaths

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

Class description:
Indicates an error in decoding a protorpclite message.

Method signatures and docstrings:
- def _FormatProtoPath(cls, edges, field_names): Returns a string representation of a path to a proto field. The return value represents one or more fields in a python dictionary representation of a message (json/yaml) that could not be decoded into the message as a string. The format is a dot separated list of python like sub field references (name, name[index], name[name]). The final element of the returned dot separated path may be a comma separated list of names enclosed in curly braces to represent multiple subfields (see examples) Examples: o Reference to a single field that could not be decoded: 'a.b[1].c[x].d' o Reference to two subfields 'a.b[1].c[x].{d,e}' Args: edges: List of objects representing python field references (__str__
- def FromErrorPaths(cls, message, errors): Returns a DecodeError from a list of locations of errors. Args: message: The protorpc Message in which a parsing error occurred. errors: List[(edges, field_names)], A list of locations of errors encountered while decoding the message.

Implement the Python class `DecodeError` described below.

Class description:
Indicates an error in decoding a protorpclite message.

Method signatures and docstrings:
- def _FormatProtoPath(cls, edges, field_names): Returns a string representation of a path to a proto field. The return value represents one or more fields in a python dictionary representation of a message (json/yaml) that could not be decoded into the message as a string. The format is a dot separated list of python like sub field references (name, name[index], name[name]). The final element of the returned dot separated path may be a comma separated list of names enclosed in curly braces to represent multiple subfields (see examples) Examples: o Reference to a single field that could not be decoded: 'a.b[1].c[x].d' o Reference to two subfields 'a.b[1].c[x].{d,e}' Args: edges: List of objects representing python field references (__str__
- def FromErrorPaths(cls, message, errors): Returns a DecodeError from a list of locations of errors. Args: message: The protorpc Message in which a parsing error occurred. errors: List[(edges, field_names)], A list of locations of errors encountered while decoding the message.

<|skeleton|>
class DecodeError:
    """Indicates an error in decoding a protorpclite message."""

    def _FormatProtoPath(cls, edges, field_names):
        """Returns a string representation of a path to a proto field. The return value represents one or more fields in a python dictionary representation of a message (json/yaml) that could not be decoded into the message as a string. The format is a dot separated list of python like sub field references (name, name[index], name[name]). The final element of the returned dot separated path may be a comma separated list of names enclosed in curly braces to represent multiple subfields (see examples) Examples: o Reference to a single field that could not be decoded: 'a.b[1].c[x].d' o Reference to two subfields 'a.b[1].c[x].{d,e}' Args: edges: List of objects representing python field references (__str__"""
        <|body_0|>

    def FromErrorPaths(cls, message, errors):
        """Returns a DecodeError from a list of locations of errors. Args: message: The protorpc Message in which a parsing error occurred. errors: List[(edges, field_names)], A list of locations of errors encountered while decoding the message."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        path = [six.text_type(edge) for edge in edges]
        if len(field_names) > 1:
            path.append('{{{}}}'.format(','.join(sorted(field_names))))
        elif field_names:
            path.append(field_names[0])
        return '.'.join(path)
<|end_body_0|>

<|body_start_1|>
        type_ = type(message).__name__
        base_msg = 'Failed to parse value(s) in protobuf [{type_}]:'.format(type_=type_)
        error_paths = ['  {type_}.{path}'.format(type_=type_, path=cls._FormatProtoPath(edges, field_names)) for edges, field_names in errors]
        return cls('\n'.join([base_msg] + error_paths))
<|end_body_1|>

Solution:
class DecodeError:
    """Indicates an error in decoding a protorpclite message."""

    def _FormatProtoPath(cls, edges, field_names):
        """Returns a string representation of a path to a proto field. The return value represents one or more fields in a python dictionary representation of a message (json/yaml) that could not be decoded into the message as a string. The format is a dot separated list of python like sub field references (name, name[index], name[name]). The final element of the returned dot separated path may be a comma separated list of names enclosed in curly braces to represent multiple subfields (see examples) Examples: o Reference to a single field that could not be decoded: 'a.b[1].c[x].d' o Reference to two subfields 'a.b[1].c[x].{d,e}' Args: edges: List of objects representing python field references (__str__"""
        path = [six.text_type(edge) for edge in edges]
        if len(field_names) > 1:
            path.append('{{{}}}'.format(','.join(sorted(field_names))))
        elif field_names:
            path.append(field_names[0])
        return '.'.join(path)

    def FromErrorPaths(cls, message, errors):
        """Returns a DecodeError from a list of locations of errors. Args: message: The protorpc Message in which a parsing error occurred. errors: List[(edges, field_names)], A list of locations of errors encountered while decoding the message."""
        type_ = type(message).__name__
        base_msg = 'Failed to parse value(s) in protobuf [{type_}]:'.format(type_=type_)
        error_paths = ['  {type_}.{path}'.format(type_=type_, path=cls._FormatProtoPath(edges, field_names)) for edges, field_names in errors]
        return cls('\n'.join([base_msg] + error_paths))


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

Class description:
Implement the _BuildKepler class.

Method signatures and docstrings:
- def __init__(self, NBURN=None, JMZ=None, FULDAT=None, NAME=None, machine='CPU', hashing=False, update=True): Note - all initilisation needed is done in class definition for now. Maybe that should go here instead ...
- def build_library_check(self, debug=True): check whether KEPLER library is up to date

Implement the Python class `_BuildKepler` described below.

Class description:
Implement the _BuildKepler class.

Method signatures and docstrings:
- def __init__(self, NBURN=None, JMZ=None, FULDAT=None, NAME=None, machine='CPU', hashing=False, update=True): Note - all initilisation needed is done in class definition for now. Maybe that should go here instead ...
- def build_library_check(self, debug=True): check whether KEPLER library is up to date

<|skeleton|>
class _BuildKepler:

    def __init__(self, NBURN=None, JMZ=None, FULDAT=None, NAME=None, machine='CPU', hashing=False, update=True):
        """Note - all initilisation needed is done in class definition for now. Maybe that should go here instead ..."""
        <|body_0|>

    def build_library_check(self, debug=True):
        """check whether KEPLER library is up to date"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        buildflags = OrderedDict()
        if NBURN is not None:
            buildflags['NBURN'] = str(NBURN)
        if JMZ is not None:
            buildflags['JMZ'] = str(JMZ)
        if FULDAT is not None:
            buildflags['FULDAT'] = FULDAT
        if NAME is not None:
            buildflags['NAME'] = NAME
        self.buildflags = buildflags
        self.makeflags = [f'{k}={v}' for k, v in buildflags.items()]
        self.update = update
        versionflags = buildflags.copy()
        if 'FULDAT' in versionflags:
            versionflags['FULDAT'] = versionflags['FULDAT'].replace('.', '_')
        self.version = '_' + _versionfunc(versionflags, machine=machine, hashing=hashing)
        if len(versionflags) == 0:
            print('[build] building with defaults from Makefile.')
        self.kepler_source_path = os.path.join(os.environ['KEPLER_PATH'], 'source')
        self.kepler_library_path = os.path.join(self.path, f'_kepler{self.version}')
        self.kepler_library_file = os.path.join(self.kepler_library_path, 'kepler.a')
        self.project_libraries = (self.kepler_library_file,)
        self.include_paths = (self.kepler_source_path, self.kepler_library_path)
        self.signature_file = f'_kepler{self.version}.pyf'
        self.module = f'_kepler{self.version}'
        self.executable_file = 'kepler{self.version}.exe'
        super().__init__()
<|end_body_0|>

<|body_start_1|>
        try:
            library_time = os.path.getctime(self.kepler_library_file)
            if self.update == False:
                return False
        except FileNotFoundError:
            library_time = 0
        exclude = ('uuidcom', 'gitcom', 'nburncom', 'gridcom')
        patterns = ('*com', '*.f', '*.f90', '*.c', 'Makefile*')
        makefile = os.path.join(self.path, 'Makefile')
        last_time = os.path.getctime(makefile)
        for p in patterns:
            for f in glob.glob(os.path.join(self.kepler_source_path, p)):
                if os.path.basename(f) in exclude:
                    continue
                last_time = max(last_time, os.path.getctime(f))
        if last_time > library_time:
            cwd = os.getcwd()
            try:
                os.mkdir(self.kepler_library_path)
            except FileExistsError:
                pass
            os.chdir(self.kepler_library_path)
            cmd = ['make', '-j', '-f', makefile] + self.makeflags
            subprocess.run(cmd, shell=False, check=True)
            os.chdir(cwd)
            return True
        return False
<|end_body_1|>

Solution:
class _BuildKepler:
    def __init__(self, NBURN=None, JMZ=None, FULDAT=None, NAME=None, machine='CPU', hashing=False, update=True):
        """Note - all initilisation needed is done in class definition for now. Maybe that should go here instead ..."""
        buildflags = OrderedDict()
        if NBURN is not None:
            buildflags['NBURN'] = str(NBURN)
        if JMZ is not None:
            buildflags['JMZ'] = str(JMZ)
        if FULDAT is not None:
            buildflags['FULDAT'] = FULDAT
        if NAME is not None:
            buildflags['NAME'] = NAME
        self.buildflags = buildflags
        self.makeflags = [f'{k}={v}' for k, v in buildflags.items()]
        self.update = update
        versionflags = buildflags.copy()
        if 'FULDAT' in versionflags:
            versionflags['FULDAT'] = versionflags['FULDAT'].replace('.', '_')
        self.version = '_' + _versionfunc(versionflags, machine=machine, hashing=hashing)
        if len(versionflags) == 0:
            print('[build] building with defaults from Makefile.')
        self.kepler_source_path = os.path.join(os.environ['KEPLER_PATH'], 'source')
        self.kepler_library_path = os.path.join(self.path, f'_kepler{self.version}')
        self.kepler_library_file = os.path.join(self.kepler_library_path, 'kepler.a')
        self.project_libraries = (self.kepler_library_file,)
        self.include_paths = (self.kepler_source_path, self.kepler_library_path)
        self.signature_file = f'_kepler{self.version}.pyf'
        self.module = f'_kepler{self.version}'
        self.executable_file = 'kepler{self.version}.exe'
        super().__init__()

    def build_library_check(self, debug=True):
        """check whether KEPLER library is up to date"""
        try:
            library_time = os.path.getctime(self.kepler_library_file)
            if self.update == False:
                return False
        except FileNotFoundError:
            library_time = 0
        exclude = ('uuidcom', 'gitcom', 'nburncom', 'gridcom')
        patterns = ('*com', '*.f', '*.f90', '*.c', 'Makefile*')
        makefile = os.path.join(self.path, 'Makefile')
        last_time = os.path.getctime(makefile)
        for p in patterns:
            for f in glob.glob(os.path.join(self.kepler_source_path, p)):
                if os.path.basename(f) in exclude:
                    continue
                last_time = max(last_time, os.path.getctime(f))
        if last_time > library_time:
            cwd = os.getcwd()
            try:
                os.mkdir(self.kepler_library_path)
            except FileExistsError:
                pass
            os.chdir(self.kepler_library_path)
            cmd = ['make', '-j', '-f', makefile] + self.makeflags
            subprocess.run(cmd, shell=False, check=True)
            os.chdir(cwd)
            return True
        return False


dump

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

Class description:
Dump node stats messages are sent to a peer node to request it to dump statistics. Attributes: DumpNodeStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward (bool): Whether the message should be automatically forwarded. IsReliable (bool): Whether reliable delivery is required. DomainList (list): A list of domains to dump stats for. MetricList (list): A list of stats to dump.

Method signatures and docstrings:
- def __init__(self, minfo=None): Constructor for the DumpNodeStatsMessage class. Args: minfo (dict): Dictionary of values for message fields.
- def dump(self): Dumps a dict containing object attributes. Returns: dict: A mapping of object attribute names to values.

Implement the Python class `DumpNodeStatsMessage` described below.

Class description:
Dump node stats messages are sent to a peer node to request it to dump statistics. Attributes: DumpNodeStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward (bool): Whether the message should be automatically forwarded. IsReliable (bool): Whether reliable delivery is required. DomainList (list): A list of domains to dump stats for. MetricList (list): A list of stats to dump.

Method signatures and docstrings:
- def __init__(self, minfo=None): Constructor for the DumpNodeStatsMessage class. Args: minfo (dict): Dictionary of values for message fields.
- def dump(self): Dumps a dict containing object attributes. Returns: dict: A mapping of object attribute names to values.

<|skeleton|>
class DumpNodeStatsMessage:
    """Dump node stats messages are sent to a peer node to request it to dump statistics. Attributes: DumpNodeStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward (bool): Whether the message should be automatically forwarded. IsReliable (bool): Whether reliable delivery is required. DomainList (list): A list of domains to dump stats for. MetricList (list): A list of stats to dump."""

    def __init__(self, minfo=None):
        """Constructor for the DumpNodeStatsMessage class. Args: minfo (dict): Dictionary of values for message fields."""
        <|body_0|>

    def dump(self):
        """Dumps a dict containing object attributes. Returns: dict: A mapping of object attribute names to values."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if minfo is None:
            minfo = {}
        super(DumpNodeStatsMessage, self).__init__(minfo)
        self.IsSystemMessage = False
        self.IsForward = True
        self.IsReliable = True
        self.DomainList = minfo.get('DomainList', [])
        self.MetricList = minfo.get('MetricList', [])
<|end_body_0|>

<|body_start_1|>
        result = super(DumpNodeStatsMessage, self).dump()
        result['DomainList'] = []
        for domain in self.DomainList:
            result['DomainList'].append(domain)
        result['MetricList'] = []
        for metric in self.MetricList:
            result['MetricList'].append(metric)
        return result
<|end_body_1|>

Solution:
class DumpNodeStatsMessage:
    """Dump node stats messages are sent to a peer node to request it to dump statistics. Attributes: DumpNodeStatsMessage.MessageType (str): The class name of the message. IsSystemMessage (bool): Whether or not this is a system message. System messages have special delivery priority rules. IsForward (bool): Whether the message should be automatically forwarded. IsReliable (bool): Whether reliable delivery is required. DomainList (list): A list of domains to dump stats for. MetricList (list): A list of stats to dump."""

    def __init__(self, minfo=None):
        """Constructor for the DumpNodeStatsMessage class. Args: minfo (dict): Dictionary of values for message fields."""
        if minfo is None:
            minfo = {}
        super(DumpNodeStatsMessage, self).__init__(minfo)
        self.IsSystemMessage = False
        self.IsForward = True
        self.IsReliable = True
        self.DomainList = minfo.get('DomainList', [])
        self.MetricList = minfo.get('MetricList', [])

    def dump(self):
        """Dumps a dict containing object attributes. Returns: dict: A mapping of object attribute names to values."""
        result = super(DumpNodeStatsMessage, self).dump()
        result['DomainList'] = []
        for domain in self.DomainList:
            result['DomainList'].append(domain)
        result['MetricList'] = []
        for metric in self.MetricList:
            result['MetricList'].append(metric)
        return result


shortest

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

Class description:
Implement the WordDistance class.

Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int

Implement the Python class `WordDistance` described below.

Class description:
Implement the WordDistance class.

Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int

<|skeleton|>
class WordDistance:

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

    def shortest(self, word1, word2):
        """:type word1: str :type word2: str :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.dict = {}
        for i in range(len(words)):
            w = words[i]
            if w in self.dict:
                self.dict[w].append(i)
            else:
                self.dict[w] = [i]
<|end_body_0|>

<|body_start_1|>
        ix1 = self.dict[word1]
        ix2 = self.dict[word2]
        i1, i2 = (0, 0)
        ret = float('inf')
        while i1 < len(ix1) and i2 < len(ix2):
            ret = min(ret, abs(ix2[i2] - ix1[i1]))
            if ix1[i1] > ix2[i2]:
                i2 += 1
            else:
                i1 += 1
        return ret
<|end_body_1|>

Solution:
class WordDistance:
    def __init__(self, words):
        """:type words: List[str]"""
        self.dict = {}
        for i in range(len(words)):
            w = words[i]
            if w in self.dict:
                self.dict[w].append(i)
            else:
                self.dict[w] = [i]

    def shortest(self, word1, word2):
        """:type word1: str :type word2: str :rtype: int"""
        ix1 = self.dict[word1]
        ix2 = self.dict[word2]
        i1, i2 = (0, 0)
        ret = float('inf')
        while i1 < len(ix1) and i2 < len(ix2):
            ret = min(ret, abs(ix2[i2] - ix1[i1]))
            if ix1[i1] > ix2[i2]:
                i2 += 1
            else:
                i1 += 1
        return ret


parser

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

Class description:
HTML解析器

Method signatures and docstrings:
- def parser(self, page_url, html_cont): parse a given html page :param page_url: the url path :param html_cont: the content of this url :return: a turple about new urls and the dictionary of this url
- def _get_new_urls(self, page_url, soup): get some new urls which need to download through this page url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the new urls set need to download
- def _get_new_data(self, page_url, soup): get content dictionary by given page_url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the dictionary about this url content

Implement the Python class `HtmlParser` described below.

Class description:
HTML解析器

Method signatures and docstrings:
- def parser(self, page_url, html_cont): parse a given html page :param page_url: the url path :param html_cont: the content of this url :return: a turple about new urls and the dictionary of this url
- def _get_new_urls(self, page_url, soup): get some new urls which need to download through this page url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the new urls set need to download
- def _get_new_data(self, page_url, soup): get content dictionary by given page_url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the dictionary about this url content

<|skeleton|>
class HtmlParser:
    """HTML解析器"""

    def parser(self, page_url, html_cont):
        """parse a given html page :param page_url: the url path :param html_cont: the content of this url :return: a turple about new urls and the dictionary of this url"""
        <|body_0|>

    def _get_new_urls(self, page_url, soup):
        """get some new urls which need to download through this page url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the new urls set need to download"""
        <|body_1|>

    def _get_new_data(self, page_url, soup):
        """get content dictionary by given page_url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the dictionary about this url content"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if page_url is None or html_cont is None:
            return
        soup = BeautifulSoup(html_cont, 'html.parser')
        new_urls = self._get_new_urls(page_url, soup)
        new_data = self._get_new_data(page_url, soup)
        return (new_urls, new_data)
<|end_body_0|>

<|body_start_1|>
        new_urls = set()
        links = soup.find_all('a', href=re.compile('/item/\\w+'))
        for link in links:
            new_url = link['href']
            new_full_url = urljoin(page_url, new_url)
            new_urls.add(new_full_url)
        return new_urls
<|end_body_1|>

<|body_start_2|>
        data = {}
        data['url'] = page_url
        title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')
        data['title'] = title.get_text()
        summary = soup.find('div', class_='lemma-summary')
        data['summary'] = summary.get_text()
        return data
<|end_body_2|>

Solution:
class HtmlParser:
    """HTML解析器"""

    def parser(self, page_url, html_cont):
        """parse a given html page :param page_url: the url path :param html_cont: the content of this url :return: a turple about new urls and the dictionary of this url"""
        if page_url is None or html_cont is None:
            return
        soup = BeautifulSoup(html_cont, 'html.parser')
        new_urls = self._get_new_urls(page_url, soup)
        new_data = self._get_new_data(page_url, soup)
        return (new_urls, new_data)

    def _get_new_urls(self, page_url, soup):
        """get some new urls which need to download through this page url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the new urls set need to download"""
        new_urls = set()
        links = soup.find_all('a', href=re.compile('/item/\\w+'))
        for link in links:
            new_url = link['href']
            new_full_url = urljoin(page_url, new_url)
            new_urls.add(new_full_url)
        return new_urls

    def _get_new_data(self, page_url, soup):
        """get content dictionary by given page_url :param page_url: the given url :param soup: the object of BeautifulSoup :return: the dictionary about this url content"""
        data = {}
        data['url'] = page_url
        title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')
        data['title'] = title.get_text()
        summary = soup.find('div', class_='lemma-summary')
        data['summary'] = summary.get_text()
        return data


backtrack

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def is_possible_divide(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def backtrack(self, k, cur_bucket_total, nums, start, used, target): @param k: 待选择的桶编号 @param cur_bucket_total: 当前桶已经装的数字之和 @param nums: 待选择的数字列表 @param used: 已经选择过的索引 @param start: 开始遍历的位置 @param target: 目标值

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def is_possible_divide(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def backtrack(self, k, cur_bucket_total, nums, start, used, target): @param k: 待选择的桶编号 @param cur_bucket_total: 当前桶已经装的数字之和 @param nums: 待选择的数字列表 @param used: 已经选择过的索引 @param start: 开始遍历的位置 @param target: 目标值

<|skeleton|>
class Solution:

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

    def backtrack(self, k, cur_bucket_total, nums, start, used, target):
        """@param k: 待选择的桶编号 @param cur_bucket_total: 当前桶已经装的数字之和 @param nums: 待选择的数字列表 @param used: 已经选择过的索引 @param start: 开始遍历的位置 @param target: 目标值"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if len(nums) < k:
            return False
        total = sum(nums)
        if total % k != 0:
            return False
        target = total / k
        used = [0] * len(nums)
        s = self.backtrack(k, 0, nums, 0, used, target)
        return s
<|end_body_0|>

<|body_start_1|>
        if k == 0:
            return True
        if cur_bucket_total == target:
            return self.backtrack(k - 1, 0, nums, 0, used, target)
        for i in range(start, len(nums)):
            if used[i]:
                continue
            if nums[i] + cur_bucket_total > target:
                continue
            used[i] = True
            cur_bucket_total += nums[i]
            if self.backtrack(k, cur_bucket_total, nums, i + 1, used, target):
                return True
            used[i] = False
            cur_bucket_total -= nums[i]
        return False
<|end_body_1|>

Solution:
class Solution:
    def is_possible_divide(self, nums, k):
        """:type nums: List[int] :type k: int :rtype: bool"""
        if len(nums) < k:
            return False
        total = sum(nums)
        if total % k != 0:
            return False
        target = total / k
        used = [0] * len(nums)
        s = self.backtrack(k, 0, nums, 0, used, target)
        return s

    def backtrack(self, k, cur_bucket_total, nums, start, used, target):
        """@param k: 待选择的桶编号 @param cur_bucket_total: 当前桶已经装的数字之和 @param nums: 待选择的数字列表 @param used: 已经选择过的索引 @param start: 开始遍历的位置 @param target: 目标值"""
        if k == 0:
            return True
        if cur_bucket_total == target:
            return self.backtrack(k - 1, 0, nums, 0, used, target)
        for i in range(start, len(nums)):
            if used[i]:
                continue
            if nums[i] + cur_bucket_total > target:
                continue
            used[i] = True
            cur_bucket_total += nums[i]
            if self.backtrack(k, cur_bucket_total, nums, i + 1, used, target):
                return True
            used[i] = False
            cur_bucket_total -= nums[i]
        return False


removeValidators

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

Class description:
Form to add a new media item

Method signatures and docstrings:
- def isNum(form, field): Check if the field"s value is a number(integer or floating value)
- def removeValidators(self, field, validator_names=None): Remove validator from the field's validator list

Implement the Python class `AddMediaForm` described below.

Class description:
Form to add a new media item

Method signatures and docstrings:
- def isNum(form, field): Check if the field"s value is a number(integer or floating value)
- def removeValidators(self, field, validator_names=None): Remove validator from the field's validator list

<|skeleton|>
class AddMediaForm:
    """Form to add a new media item"""

    def isNum(form, field):
        """Check if the field"s value is a number(integer or floating value)"""
        <|body_0|>

    def removeValidators(self, field, validator_names=None):
        """Remove validator from the field's validator list"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            val = field.data.strip()
            if val:
                float(val)
            return True
        except ValueError:
            raise ValidationError('Invalid number provided(only numbers and 1 period allowed)')
<|end_body_0|>

<|body_start_1|>
        if not validator_names:
            return field
        for i in xrange(0, len(validator_names)):
            if validator_names[i] == 'DataRequired':
                validator_names[i] = validators.DataRequired
        field.validators = [x for x in field.validators if not type(x) in validator_names]
        return field
<|end_body_1|>

Solution:
class AddMediaForm:
    """Form to add a new media item"""

    def isNum(form, field):
        """Check if the field"s value is a number(integer or floating value)"""
        try:
            val = field.data.strip()
            if val:
                float(val)
            return True
        except ValueError:
            raise ValidationError('Invalid number provided(only numbers and 1 period allowed)')

    def removeValidators(self, field, validator_names=None):
        """Remove validator from the field's validator list"""
        if not validator_names:
            return field
        for i in xrange(0, len(validator_names)):
            if validator_names[i] == 'DataRequired':
                validator_names[i] = validators.DataRequired
        field.validators = [x for x in field.validators if not type(x) in validator_names]
        return field


adjoint

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

Class description:
Row operator

Method signatures and docstrings:
- def __init__(self, ops, dims): rowop constructor Parameters: ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, 'ncols': 10},...] Note that the row operator will be formed in the order in which the operators are supplied. For example ops = [A,B,C,...] will result in [A,B,C,...][ma] [mb] [mc] [::]
- def totaldims(self): Returns the total dims of the row operator
- def forward(self, add, mod, dat): Applies the forward of a generic row operator Parameters: add - whether to add to the total output mod - a list of input models [mc,mb,ma] dat - output data vector
- def adjoint(self, add, mod, dat): Applies the adjoint of a generic row operator (column operator) Parameters: add - whether to add to the total output mod - a list of output models [mc,mb,ma] dat - input data vector

Implement the Python class `rowop` described below.

Class description:
Row operator

Method signatures and docstrings:
- def __init__(self, ops, dims): rowop constructor Parameters: ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, 'ncols': 10},...] Note that the row operator will be formed in the order in which the operators are supplied. For example ops = [A,B,C,...] will result in [A,B,C,...][ma] [mb] [mc] [::]
- def totaldims(self): Returns the total dims of the row operator
- def forward(self, add, mod, dat): Applies the forward of a generic row operator Parameters: add - whether to add to the total output mod - a list of input models [mc,mb,ma] dat - output data vector
- def adjoint(self, add, mod, dat): Applies the adjoint of a generic row operator (column operator) Parameters: add - whether to add to the total output mod - a list of output models [mc,mb,ma] dat - input data vector

<|skeleton|>
class rowop:
    """Row operator"""

    def __init__(self, ops, dims):
        """rowop constructor Parameters: ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, 'ncols': 10},...] Note that the row operator will be formed in the order in which the operators are supplied. For example ops = [A,B,C,...] will result in [A,B,C,...][ma] [mb] [mc] [::]"""
        <|body_0|>

    def totaldims(self):
        """Returns the total dims of the row operator"""
        <|body_1|>

    def forward(self, add, mod, dat):
        """Applies the forward of a generic row operator Parameters: add - whether to add to the total output mod - a list of input models [mc,mb,ma] dat - output data vector"""
        <|body_2|>

    def adjoint(self, add, mod, dat):
        """Applies the adjoint of a generic row operator (column operator) Parameters: add - whether to add to the total output mod - a list of output models [mc,mb,ma] dat - input data vector"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.__ops = ops
        self.__nops = len(ops)
        for iop in range(self.__nops):
            if not isinstance(self.__ops[iop], operator):
                raise Exception('Elements of ops list must be of type operator')
        if self.__nops != len(dims):
            raise Exception('Number of dimensions (%d) must equal number of operators (%d)' % (len(dims), self.__nops))
        self.__ddim = dims[-1]['nrows']
        for idim in range(len(dims) - 1):
            if np.prod(dims[idim]['nrows']) != np.prod(dims[idim + 1]['nrows']):
                print('Operator %d has %d rows and operator %d has %d rows' % (idim, np.prod(dims[idim]['nrows']), idim + 1, np.prod(dims[idim + 1]['nrows'])))
                raise Exception("Dimensions of ops don't match")
        self.__dims = dims
<|end_body_0|>

<|body_start_1|>
        totaldims = {}
        totaldims['nrows'] = self.__ddim
        totaldims['ncols'] = 0
        for iop in range(self.__nops):
            totaldims['ncols'] += dims[iop]['ncols']
        return totaldims
<|end_body_1|>

<|body_start_2|>
        if not isinstance(mod, list):
            raise Exception('Input model must be a list of numpy arrays')
        if len(mod) != self.__nops:
            raise Exception('Number of models in list must be same as number of operators')
        if dat.shape != self.__ddim:
            raise Exception('Output data does not match row operator')
        iadd = add
        for iop in range(self.__nops):
            self.__ops[iop].forward(iadd, mod[iop], dat)
            iadd = True
<|end_body_2|>

<|body_start_3|>
        if not isinstance(mod, list):
            raise Exception('Output model must be a list of numpy arrays')
        if len(mod) != self.__nops:
            raise Exception('Number of models in list must be same as number of operators')
        if dat.shape != self.__ddim:
            raise Exception('Output data does not match row operator')
        for iop in range(self.__nops):
            self.__ops[iop].forward(add, m[iop], data)
<|end_body_3|>

Solution:
class rowop:
    """Row operator"""

    def __init__(self, ops, dims):
        """rowop constructor Parameters: ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, 'ncols': 10},...] Note that the row operator will be formed in the order in which the operators are supplied. For example ops = [A,B,C,...] will result in [A,B,C,...][ma] [mb] [mc] [::]"""
        self.__ops = ops
        self.__nops = len(ops)
        for iop in range(self.__nops):
            if not isinstance(self.__ops[iop], operator):
                raise Exception('Elements of ops list must be of type operator')
        if self.__nops != len(dims):
            raise Exception('Number of dimensions (%d) must equal number of operators (%d)' % (len(dims), self.__nops))
        self.__ddim = dims[-1]['nrows']
        for idim in range(len(dims) - 1):
            if np.prod(dims[idim]['nrows']) != np.prod(dims[idim + 1]['nrows']):
                print('Operator %d has %d rows and operator %d has %d rows' % (idim, np.prod(dims[idim]['nrows']), idim + 1, np.prod(dims[idim + 1]['nrows'])))
                raise Exception("Dimensions of ops don't match")
        self.__dims = dims

    def totaldims(self):
        """Returns the total dims of the row operator"""
        totaldims = {}
        totaldims['nrows'] = self.__ddim
        totaldims['ncols'] = 0
        for iop in range(self.__nops):
            totaldims['ncols'] += dims[iop]['ncols']
        return totaldims

    def forward(self, add, mod, dat):
        """Applies the forward of a generic row operator Parameters: add - whether to add to the total output mod - a list of input models [mc,mb,ma] dat - output data vector"""
        if not isinstance(mod, list):
            raise Exception('Input model must be a list of numpy arrays')
        if len(mod) != self.__nops:
            raise Exception('Number of models in list must be same as number of operators')
        if dat.shape != self.__ddim:
            raise Exception('Output data does not match row operator')
        iadd = add
        for iop in range(self.__nops):
            self.__ops[iop].forward(iadd, mod[iop], dat)
            iadd = True

    def adjoint(self, add, mod, dat):
        """Applies the adjoint of a generic row operator (column operator) Parameters: add - whether to add to the total output mod - a list of output models [mc,mb,ma] dat - input data vector"""
        if not isinstance(mod, list):
            raise Exception('Output model must be a list of numpy arrays')
        if len(mod) != self.__nops:
            raise Exception('Number of models in list must be same as number of operators')
        if dat.shape != self.__ddim:
            raise Exception('Output data does not match row operator')
        for iop in range(self.__nops):
            self.__ops[iop].forward(add, m[iop], data)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findCircleNum(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_unionfind(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_wrong(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_toomuchrecursion(self, M): :type M: List[List[int]] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findCircleNum(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_unionfind(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_wrong(self, M): :type M: List[List[int]] :rtype: int
- def findCircleNum_toomuchrecursion(self, M): :type M: List[List[int]] :rtype: int

<|skeleton|>
class Solution:

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

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

    def findCircleNum_wrong(self, M):
        """:type M: List[List[int]] :rtype: int"""
        <|body_2|>

    def findCircleNum_toomuchrecursion(self, M):
        """:type M: List[List[int]] :rtype: int"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        def dfs(i):
            if visited[i]:
                return 0
            visited[i] = True
            count = 1
            for j in range(len(M[i])):
                if M[i][j] == 1 and i != j:
                    count += dfs(j)
            return count
        count = 0
        visited = [False] * len(M)
        for i in range(len(M)):
            if dfs(i) > 0:
                count += 1
        return count
<|end_body_0|>

<|body_start_1|>
        class UnionFind:

            def __init__(self, n):
                self.set = list(range(n))
                self.count = n

            def find_set(self, x):
                if self.set[x] != x:
                    self.set[x] = self.find_set(self.set[x])
                return self.set[x]

            def union_set(self, x, y):
                x_root, y_root = map(self.find_set, (x, y))
                if x_root != y_root:
                    self.set[min(x_root, y_root)] = max(x_root, y_root)
                    self.count -= 1
        circles = UnionFind(len(M))
        for i in range(len(M)):
            for j in range(len(M[i])):
                if M[i][j] and i != j:
                    circles.union_set(i, j)
        return circles.count
<|end_body_1|>

<|body_start_2|>
        def traverse(i, j, cid):
            if i < 0 or i >= len(M) or j < 0 or (j >= len(M[i])):
                return
            if M[i][j] <= 0:
                return
            M[i][j] = cid
            traverse(i + 1, j, cid)
            traverse(i, j + 1, cid)
        cid = 0
        for i in range(len(M)):
            for j in range(len(M[i])):
                if M[i][j] == 1:
                    cid -= 1
                    traverse(i, j, cid)
        return -cid
<|end_body_2|>

<|body_start_3|>
        def search(i, j):
            if i >= len(M) or j >= len(M[i]) or M[i][j] == 0:
                return 0
            M[i][j] = 0
            count = 1
            count += search(i + 1, j) + search(i - 1, j) + search(i, j + 1) + search(i, j - 1)
            return count
        count = 0
        for i in range(len(M)):
            for j in range(len(M[i])):
                if search(i, j) > 0:
                    count += 1
        return count
<|end_body_3|>

Solution:
class Solution:
    def findCircleNum(self, M):
        """:type M: List[List[int]] :rtype: int"""
        def dfs(i):
            if visited[i]:
                return 0
            visited[i] = True
            count = 1
            for j in range(len(M[i])):
                if M[i][j] == 1 and i != j:
                    count += dfs(j)
            return count
        count = 0
        visited = [False] * len(M)
        for i in range(len(M)):
            if dfs(i) > 0:
                count += 1
        return count

    def findCircleNum_unionfind(self, M):
        """:type M: List[List[int]] :rtype: int"""
        class UnionFind:

            def __init__(self, n):
                self.set = list(range(n))
                self.count = n

            def find_set(self, x):
                if self.set[x] != x:
                    self.set[x] = self.find_set(self.set[x])
                return self.set[x]

            def union_set(self, x, y):
                x_root, y_root = map(self.find_set, (x, y))
                if x_root != y_root:
                    self.set[min(x_root, y_root)] = max(x_root, y_root)
                    self.count -= 1
        circles = UnionFind(len(M))
        for i in range(len(M)):
            for j in range(len(M[i])):
                if M[i][j] and i != j:
                    circles.union_set(i, j)
        return circles.count

    def findCircleNum_wrong(self, M):
        """:type M: List[List[int]] :rtype: int"""
        def traverse(i, j, cid):
            if i < 0 or i >= len(M) or j < 0 or (j >= len(M[i])):
                return
            if M[i][j] <= 0:
                return
            M[i][j] = cid
            traverse(i + 1, j, cid)
            traverse(i, j + 1, cid)
        cid = 0
        for i in range(len(M)):
            for j in range(len(M[i])):
                if M[i][j] == 1:
                    cid -= 1
                    traverse(i, j, cid)
        return -cid

    def findCircleNum_toomuchrecursion(self, M):
        """:type M: List[List[int]] :rtype: int"""
        def search(i, j):
            if i >= len(M) or j >= len(M[i]) or M[i][j] == 0:
                return 0
            M[i][j] = 0
            count = 1
            count += search(i + 1, j) + search(i - 1, j) + search(i, j + 1) + search(i, j - 1)
            return count
        count = 0
        for i in range(len(M)):
            for j in range(len(M[i])):
                if search(i, j) > 0:
                    count += 1
        return count


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

Class description:
Data set

Method signatures and docstrings:
- def __init__(self): Data set
- def tokenize_dataset(self, data): Data set

Implement the Python class `Dataset` described below.

Class description:
Data set

Method signatures and docstrings:
- def __init__(self): Data set
- def tokenize_dataset(self, data): Data set

<|skeleton|>
class Dataset:
    """Data set"""

    def __init__(self):
        """Data set"""
        <|body_0|>

    def tokenize_dataset(self, data):
        """Data set"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        examples, _ = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True, as_supervised=True)
        self.data_train = examples['train']
        self.data_valid = examples['validation']
        self.tokenizer_pt, self.tokenizer_en = self.tokenize_dataset(self.data_train)
<|end_body_0|>

<|body_start_1|>
        tokenizer_pt = tfds.features.text.SubwordTextEncoder.build_from_corpus((pt.numpy() for pt, en in data), target_vocab_size=2 ** 15)
        tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus((en.numpy() for pt, en in data), target_vocab_size=2 ** 15)
        return (tokenizer_pt, tokenizer_en)
<|end_body_1|>

Solution:
class Dataset:
    """Data set"""

    def __init__(self):
        """Data set"""
        examples, _ = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True, as_supervised=True)
        self.data_train = examples['train']
        self.data_valid = examples['validation']
        self.tokenizer_pt, self.tokenizer_en = self.tokenize_dataset(self.data_train)

    def tokenize_dataset(self, data):
        """Data set"""
        tokenizer_pt = tfds.features.text.SubwordTextEncoder.build_from_corpus((pt.numpy() for pt, en in data), target_vocab_size=2 ** 15)
        tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus((en.numpy() for pt, en in data), target_vocab_size=2 ** 15)
        return (tokenizer_pt, tokenizer_en)


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

Class description:
Implement the RetryFailed class.

Method signatures and docstrings:
- def get(self, session=None): List all failed entries
- def delete(self, session=None): Clear all failed entries

Implement the Python class `RetryFailed` described below.

Class description:
Implement the RetryFailed class.

Method signatures and docstrings:
- def get(self, session=None): List all failed entries
- def delete(self, session=None): Clear all failed entries

<|skeleton|>
class RetryFailed:

    def get(self, session=None):
        """List all failed entries"""
        <|body_0|>

    def delete(self, session=None):
        """Clear all failed entries"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        args = failed_parser.parse_args()
        page = args['page']
        per_page = args['per_page']
        sort_by = args['sort_by']
        sort_order = args['order']
        if sort_by == 'failure_time':
            sort_by = 'tof'
        if per_page > 100:
            per_page = 100
        descending = sort_order == 'desc'
        if per_page > 100:
            per_page = 100
        start = per_page * (page - 1)
        stop = start + per_page
        kwargs = {'start': start, 'stop': stop, 'descending': descending, 'sort_by': sort_by, 'session': session}
        total_items = db.get_failures(session, count=True)
        if not total_items:
            return jsonify([])
        failed_entries = [failed.to_dict() for failed in db.get_failures(**kwargs)]
        total_pages = int(ceil(total_items / float(per_page)))
        if page > total_pages:
            raise NotFoundError('page %s does not exist' % page)
        actual_size = min(per_page, len(failed_entries))
        pagination = pagination_headers(total_pages, total_items, actual_size, request)
        rsp = jsonify(failed_entries)
        rsp.headers.extend(pagination)
        return rsp
<|end_body_0|>

<|body_start_1|>
        logger.debug('deleting all failed entries')
        deleted = session.query(db.FailedEntry).delete()
        return success_response('successfully deleted %d failed entries' % deleted)
<|end_body_1|>

Solution:
class RetryFailed:
    def get(self, session=None):
        """List all failed entries"""
        args = failed_parser.parse_args()
        page = args['page']
        per_page = args['per_page']
        sort_by = args['sort_by']
        sort_order = args['order']
        if sort_by == 'failure_time':
            sort_by = 'tof'
        if per_page > 100:
            per_page = 100
        descending = sort_order == 'desc'
        if per_page > 100:
            per_page = 100
        start = per_page * (page - 1)
        stop = start + per_page
        kwargs = {'start': start, 'stop': stop, 'descending': descending, 'sort_by': sort_by, 'session': session}
        total_items = db.get_failures(session, count=True)
        if not total_items:
            return jsonify([])
        failed_entries = [failed.to_dict() for failed in db.get_failures(**kwargs)]
        total_pages = int(ceil(total_items / float(per_page)))
        if page > total_pages:
            raise NotFoundError('page %s does not exist' % page)
        actual_size = min(per_page, len(failed_entries))
        pagination = pagination_headers(total_pages, total_items, actual_size, request)
        rsp = jsonify(failed_entries)
        rsp.headers.extend(pagination)
        return rsp

    def delete(self, session=None):
        """Clear all failed entries"""
        logger.debug('deleting all failed entries')
        deleted = session.query(db.FailedEntry).delete()
        return success_response('successfully deleted %d failed entries' % deleted)


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

Class description:
Handle a config flow for Ring.

Method signatures and docstrings:
- async def async_step_user(self, user_input=None): Handle the initial step.
- async def async_step_2fa(self, user_input=None): Handle 2fa step.

Implement the Python class `RingConfigFlow` described below.

Class description:
Handle a config flow for Ring.

Method signatures and docstrings:
- async def async_step_user(self, user_input=None): Handle the initial step.
- async def async_step_2fa(self, user_input=None): Handle 2fa step.

<|skeleton|>
class RingConfigFlow:
    """Handle a config flow for Ring."""

    async def async_step_user(self, user_input=None):
        """Handle the initial step."""
        <|body_0|>

    async def async_step_2fa(self, user_input=None):
        """Handle 2fa step."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        errors = {}
        if user_input is not None:
            try:
                token = await validate_input(self.hass, user_input)
                await self.async_set_unique_id(user_input['username'])
                return self.async_create_entry(title=user_input['username'], data={'username': user_input['username'], 'token': token})
            except Require2FA:
                self.user_pass = user_input
                return await self.async_step_2fa()
            except InvalidAuth:
                errors['base'] = 'invalid_auth'
            except Exception:
                _LOGGER.exception('Unexpected exception')
                errors['base'] = 'unknown'
        return self.async_show_form(step_id='user', data_schema=vol.Schema({vol.Required('username'): str, vol.Required('password'): str}), errors=errors)
<|end_body_0|>

<|body_start_1|>
        if user_input:
            return await self.async_step_user({**self.user_pass, **user_input})
        return self.async_show_form(step_id='2fa', data_schema=vol.Schema({vol.Required('2fa'): str}))
<|end_body_1|>

Solution:
class RingConfigFlow:
    """Handle a config flow for Ring."""

    async def async_step_user(self, user_input=None):
        """Handle the initial step."""
        errors = {}
        if user_input is not None:
            try:
                token = await validate_input(self.hass, user_input)
                await self.async_set_unique_id(user_input['username'])
                return self.async_create_entry(title=user_input['username'], data={'username': user_input['username'], 'token': token})
            except Require2FA:
                self.user_pass = user_input
                return await self.async_step_2fa()
            except InvalidAuth:
                errors['base'] = 'invalid_auth'
            except Exception:
                _LOGGER.exception('Unexpected exception')
                errors['base'] = 'unknown'
        return self.async_show_form(step_id='user', data_schema=vol.Schema({vol.Required('username'): str, vol.Required('password'): str}), errors=errors)

    async def async_step_2fa(self, user_input=None):
        """Handle 2fa step."""
        if user_input:
            return await self.async_step_user({**self.user_pass, **user_input})
        return self.async_show_form(step_id='2fa', data_schema=vol.Schema({vol.Required('2fa'): str}))


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

Class description:
Implement the Scamp class.

Method signatures and docstrings:
- def __init__(self, logger, mag, scmp_d, scmp_cf, sex_d): :param logger: :param mag: :param scmp_d: :param scmp_cf: :param sex_d:
- def scamp_process(self): :return:

Implement the Python class `Scamp` described below.

Class description:
Implement the Scamp class.

Method signatures and docstrings:
- def __init__(self, logger, mag, scmp_d, scmp_cf, sex_d): :param logger: :param mag: :param scmp_d: :param scmp_cf: :param sex_d:
- def scamp_process(self): :return:

<|skeleton|>
class Scamp:

    def __init__(self, logger, mag, scmp_d, scmp_cf, sex_d):
        """:param logger: :param mag: :param scmp_d: :param scmp_cf: :param sex_d:"""
        <|body_0|>

    def scamp_process(self):
        """:return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.prfs_d = extract_settings()
        self.logger = logger
        self.mag = mag
        self.scmp_d = scmp_d
        self.scmp_cf = scmp_cf
        self.sex_d = sex_d
        self.scamp_process()
<|end_body_0|>

<|body_start_1|>
        sex_cf = '{}_{}_{}_{}_{}'.format(self.sex_d['deblend_nthresh'], self.sex_d['analysis_thresh'], self.sex_d['detect_thresh'], self.sex_d['deblend_mincount'], self.sex_d['detect_minarea'])
        self.logger.info('Scamp process for magnitude {}'.format(self.mag))
        self.logger.info('Sextractor configuration: {}'.format(sex_cf))
        self.logger.info('Scamp configuration: {}'.format(self.scmp_cf))
        scmp_1 = 'scamp -c {}'.format(self.prfs_d['conf_scamp'])
        sex_loc = '{}/{}/CCDs/{}'.format(self.prfs_d['fits_dir'], self.mag, sex_cf)
        sex_output = 'mag_{}_CCD_x?_y?_d?.cat'.format(self.mag)
        scmp_2 = ' {}/{}'.format(sex_loc, sex_output)
        scmp_3 = ' -ASTREFCAT_NAME'
        cat_loc = '{}/{}/CCDs/{}'.format(self.prfs_d['fits_dir'], self.mag, sex_cf)
        cat_input = 'catalog_{}.cat'.format(self.mag)
        scmp_4 = ' {}/{}'.format(cat_loc, cat_input)
        scmp_5 = ' -PIXSCALE_MAXERR {}'.format(self.scmp_d['pixscale_maxerr'])
        scmp_6 = ' -POSANGLE_MAXERR {}'.format(self.scmp_d['posangle_maxerr'])
        scmp_7 = ' -POSITION_MAXERR {}'.format(self.scmp_d['position_maxerr'])
        scmp_8 = ' -CROSSID_RADIUS {}'.format(self.scmp_d['crossid_radius'])
        cats_dir = '{}/{}/{}/{}'.format(self.prfs_d['catalogs_dir'], self.mag, sex_cf, self.scmp_cf)
        merged_cat = '{}/merged_{}_{}.cat'.format(cats_dir, self.scmp_cf, self.mag)
        scmp_9 = ' -MERGEDOUTCAT_NAME {}'.format(merged_cat)
        full_cat = '{}/full_{}_{}.cat'.format(cats_dir, self.scmp_cf, self.mag)
        scmp_10 = ' -FULLOUTCAT_NAME {}'.format(full_cat)
        scmp_p = scmp_1 + scmp_2 + scmp_3 + scmp_4 + scmp_5
        scmp_p = scmp_p + scmp_6 + scmp_7 + scmp_8 + scmp_9
        scmp_p = scmp_p + scmp_10
        create_folder(self.logger, cats_dir)
        process_scamp = Popen(scmp_p, shell=True)
        process_scamp.wait()
        self.logger.info('Scamp process finished.')
        return True
<|end_body_1|>

Solution:
class Scamp:
    def __init__(self, logger, mag, scmp_d, scmp_cf, sex_d):
        """:param logger: :param mag: :param scmp_d: :param scmp_cf: :param sex_d:"""
        self.prfs_d = extract_settings()
        self.logger = logger
        self.mag = mag
        self.scmp_d = scmp_d
        self.scmp_cf = scmp_cf
        self.sex_d = sex_d
        self.scamp_process()

    def scamp_process(self):
        """:return:"""
        sex_cf = '{}_{}_{}_{}_{}'.format(self.sex_d['deblend_nthresh'], self.sex_d['analysis_thresh'], self.sex_d['detect_thresh'], self.sex_d['deblend_mincount'], self.sex_d['detect_minarea'])
        self.logger.info('Scamp process for magnitude {}'.format(self.mag))
        self.logger.info('Sextractor configuration: {}'.format(sex_cf))
        self.logger.info('Scamp configuration: {}'.format(self.scmp_cf))
        scmp_1 = 'scamp -c {}'.format(self.prfs_d['conf_scamp'])
        sex_loc = '{}/{}/CCDs/{}'.format(self.prfs_d['fits_dir'], self.mag, sex_cf)
        sex_output = 'mag_{}_CCD_x?_y?_d?.cat'.format(self.mag)
        scmp_2 = ' {}/{}'.format(sex_loc, sex_output)
        scmp_3 = ' -ASTREFCAT_NAME'
        cat_loc = '{}/{}/CCDs/{}'.format(self.prfs_d['fits_dir'], self.mag, sex_cf)
        cat_input = 'catalog_{}.cat'.format(self.mag)
        scmp_4 = ' {}/{}'.format(cat_loc, cat_input)
        scmp_5 = ' -PIXSCALE_MAXERR {}'.format(self.scmp_d['pixscale_maxerr'])
        scmp_6 = ' -POSANGLE_MAXERR {}'.format(self.scmp_d['posangle_maxerr'])
        scmp_7 = ' -POSITION_MAXERR {}'.format(self.scmp_d['position_maxerr'])
        scmp_8 = ' -CROSSID_RADIUS {}'.format(self.scmp_d['crossid_radius'])
        cats_dir = '{}/{}/{}/{}'.format(self.prfs_d['catalogs_dir'], self.mag, sex_cf, self.scmp_cf)
        merged_cat = '{}/merged_{}_{}.cat'.format(cats_dir, self.scmp_cf, self.mag)
        scmp_9 = ' -MERGEDOUTCAT_NAME {}'.format(merged_cat)
        full_cat = '{}/full_{}_{}.cat'.format(cats_dir, self.scmp_cf, self.mag)
        scmp_10 = ' -FULLOUTCAT_NAME {}'.format(full_cat)
        scmp_p = scmp_1 + scmp_2 + scmp_3 + scmp_4 + scmp_5
        scmp_p = scmp_p + scmp_6 + scmp_7 + scmp_8 + scmp_9
        scmp_p = scmp_p + scmp_10
        create_folder(self.logger, cats_dir)
        process_scamp = Popen(scmp_p, shell=True)
        process_scamp.wait()
        self.logger.info('Scamp process finished.')
        return True