execute

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

Class description:
Extends tf.test.TestCase to optionally allow running tests on TPU.

Method signatures and docstrings:
- def execute_tpu(self, graph_fn, inputs): Constructs the graph, executes it on TPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.
- def execute_cpu(self, graph_fn, inputs): Constructs the graph, executes it on CPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.
- def execute(self, graph_fn, inputs): Constructs the graph, creates a test session and returns the results. The graph is executed either on TPU or CPU based on the `tpu_test` flag. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.

Implement the Python class `TestCase` described below.

Class description:
Extends tf.test.TestCase to optionally allow running tests on TPU.

Method signatures and docstrings:
- def execute_tpu(self, graph_fn, inputs): Constructs the graph, executes it on TPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.
- def execute_cpu(self, graph_fn, inputs): Constructs the graph, executes it on CPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.
- def execute(self, graph_fn, inputs): Constructs the graph, creates a test session and returns the results. The graph is executed either on TPU or CPU based on the `tpu_test` flag. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph.

<|skeleton|>
class TestCase:
    """Extends tf.test.TestCase to optionally allow running tests on TPU."""

    def execute_tpu(self, graph_fn, inputs):
        """Constructs the graph, executes it on TPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        <|body_0|>

    def execute_cpu(self, graph_fn, inputs):
        """Constructs the graph, executes it on CPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        <|body_1|>

    def execute(self, graph_fn, inputs):
        """Constructs the graph, creates a test session and returns the results. The graph is executed either on TPU or CPU based on the `tpu_test` flag. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        with self.test_session(graph=tf.Graph()) as sess:
            placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
            tpu_computation = tpu.rewrite(graph_fn, placeholders)
            sess.run(tpu.initialize_system())
            sess.run([tf.global_variables_initializer(), tf.tables_initializer(), tf.local_variables_initializer()])
            materialized_results = sess.run(tpu_computation, feed_dict=dict(zip(placeholders, inputs)))
            sess.run(tpu.shutdown_system())
            if len(materialized_results) == 1 and (isinstance(materialized_results, list) or isinstance(materialized_results, tuple)):
                materialized_results = materialized_results[0]
        return materialized_results
<|end_body_0|>

<|body_start_1|>
        with self.test_session(graph=tf.Graph()) as sess:
            placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
            results = graph_fn(*placeholders)
            sess.run([tf.global_variables_initializer(), tf.tables_initializer(), tf.local_variables_initializer()])
            materialized_results = sess.run(results, feed_dict=dict(zip(placeholders, inputs)))
            if len(materialized_results) == 1 and (isinstance(materialized_results, list) or isinstance(materialized_results, tuple)):
                materialized_results = materialized_results[0]
        return materialized_results
<|end_body_1|>

<|body_start_2|>
        if FLAGS.tpu_test:
            return self.execute_tpu(graph_fn, inputs)
        else:
            return self.execute_cpu(graph_fn, inputs)
<|end_body_2|>

Solution:
class TestCase:
    """Extends tf.test.TestCase to optionally allow running tests on TPU."""

    def execute_tpu(self, graph_fn, inputs):
        """Constructs the graph, executes it on TPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        with self.test_session(graph=tf.Graph()) as sess:
            placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
            tpu_computation = tpu.rewrite(graph_fn, placeholders)
            sess.run(tpu.initialize_system())
            sess.run([tf.global_variables_initializer(), tf.tables_initializer(), tf.local_variables_initializer()])
            materialized_results = sess.run(tpu_computation, feed_dict=dict(zip(placeholders, inputs)))
            sess.run(tpu.shutdown_system())
            if len(materialized_results) == 1 and (isinstance(materialized_results, list) or isinstance(materialized_results, tuple)):
                materialized_results = materialized_results[0]
        return materialized_results

    def execute_cpu(self, graph_fn, inputs):
        """Constructs the graph, executes it on CPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        with self.test_session(graph=tf.Graph()) as sess:
            placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
            results = graph_fn(*placeholders)
            sess.run([tf.global_variables_initializer(), tf.tables_initializer(), tf.local_variables_initializer()])
            materialized_results = sess.run(results, feed_dict=dict(zip(placeholders, inputs)))
            if len(materialized_results) == 1 and (isinstance(materialized_results, list) or isinstance(materialized_results, tuple)):
                materialized_results = materialized_results[0]
        return materialized_results

    def execute(self, graph_fn, inputs):
        """Constructs the graph, creates a test session and returns the results. The graph is executed either on TPU or CPU based on the `tpu_test` flag. Args: graph_fn: a callable that constructs the tensorflow graph to test. The arguments of this function should correspond to `inputs`. inputs: a list of numpy arrays to feed input to the computation graph. Returns: A list of numpy arrays or a scalar returned from executing the tensorflow graph."""
        if FLAGS.tpu_test:
            return self.execute_tpu(graph_fn, inputs)
        else:
            return self.execute_cpu(graph_fn, inputs)


forward

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

Class description:
Estimator for cluster centroids positions across domains

Method signatures and docstrings:
- def __init__(self, config, num_clusters): Initializes the centroid estimator based on the provided configuration and number of clusters that will need to be estimated :param config: configuration file :param num_clusters: number of clusters for which to estimate centroids
- def compute_centroids(self, numerator: torch.Tensor, cluster_probabilities: torch.Tensor, eps=0.001): Computes the centroids over the whole numerator and cluster probabilities tensors :param numerator: (num_points, features_size, num_clusters) tensor :param cluster_probabilities: (num_points, num_clusters) tensor :return: (features_size, num_clusters) tensor representing the centroids for each cluster
- def forward(self, features: torch.Tensor, domains: torch.Tensor, cluster_probabilities: torch.Tensor): :param features: (batch_size, features_size) tensor of features to which to estimate centroids :param domains: (1, batch_size) tensor representing the domain of each sample :param cluster_probabilities: (batch_size, num_clusters) tensor of cluster assigment probabilities :return: global_centroid, domain_centroids_list where global centroid is a (feaures_size, num_clusters) tensor representing the global centroids and domain_centroids_list is a list of length domains_count with such centroid tensors computed on each domain

Implement the Python class `CentroidEstimator` described below.

Class description:
Estimator for cluster centroids positions across domains

Method signatures and docstrings:
- def __init__(self, config, num_clusters): Initializes the centroid estimator based on the provided configuration and number of clusters that will need to be estimated :param config: configuration file :param num_clusters: number of clusters for which to estimate centroids
- def compute_centroids(self, numerator: torch.Tensor, cluster_probabilities: torch.Tensor, eps=0.001): Computes the centroids over the whole numerator and cluster probabilities tensors :param numerator: (num_points, features_size, num_clusters) tensor :param cluster_probabilities: (num_points, num_clusters) tensor :return: (features_size, num_clusters) tensor representing the centroids for each cluster
- def forward(self, features: torch.Tensor, domains: torch.Tensor, cluster_probabilities: torch.Tensor): :param features: (batch_size, features_size) tensor of features to which to estimate centroids :param domains: (1, batch_size) tensor representing the domain of each sample :param cluster_probabilities: (batch_size, num_clusters) tensor of cluster assigment probabilities :return: global_centroid, domain_centroids_list where global centroid is a (feaures_size, num_clusters) tensor representing the global centroids and domain_centroids_list is a list of length domains_count with such centroid tensors computed on each domain

<|skeleton|>
class CentroidEstimator:
    """Estimator for cluster centroids positions across domains"""

    def __init__(self, config, num_clusters):
        """Initializes the centroid estimator based on the provided configuration and number of clusters that will need to be estimated :param config: configuration file :param num_clusters: number of clusters for which to estimate centroids"""
        <|body_0|>

    def compute_centroids(self, numerator: torch.Tensor, cluster_probabilities: torch.Tensor, eps=0.001):
        """Computes the centroids over the whole numerator and cluster probabilities tensors :param numerator: (num_points, features_size, num_clusters) tensor :param cluster_probabilities: (num_points, num_clusters) tensor :return: (features_size, num_clusters) tensor representing the centroids for each cluster"""
        <|body_1|>

    def forward(self, features: torch.Tensor, domains: torch.Tensor, cluster_probabilities: torch.Tensor):
        """:param features: (batch_size, features_size) tensor of features to which to estimate centroids :param domains: (1, batch_size) tensor representing the domain of each sample :param cluster_probabilities: (batch_size, num_clusters) tensor of cluster assigment probabilities :return: global_centroid, domain_centroids_list where global centroid is a (feaures_size, num_clusters) tensor representing the global centroids and domain_centroids_list is a list of length domains_count with such centroid tensors computed on each domain"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(CentroidEstimator, self).__init__()
        self.domains_count = config.domains_count
        self.features_size = config.encoder_features_count
        self.num_clusters = num_clusters
        self.alpha = config.centroid_estimation_alpha
        self.global_centroids_estimator = FixedMatrixEstimator(self.features_size, self.num_clusters, self.alpha, initial_value=torch.zeros((self.features_size, self.num_clusters)))
        self.domain_centroids_estimators = nn.ModuleList([FixedMatrixEstimator(self.features_size, self.num_clusters, self.alpha, initial_value=torch.zeros((self.features_size, self.num_clusters))) for i in range(self.domains_count)])
<|end_body_0|>

<|body_start_1|>
        numerator = torch.sum(numerator, dim=0, keepdim=False)
        cluster_probabilities = torch.sum(cluster_probabilities, dim=0, keepdim=False)
        cluster_probabilities = cluster_probabilities + eps
        return numerator / cluster_probabilities
<|end_body_1|>

<|body_start_2|>
        unsqueezed_features = features.unsqueeze(-1)
        unsqueezed_cluster_probabilities = cluster_probabilities.unsqueeze(1)
        unsummed_numerator = unsqueezed_features * unsqueezed_cluster_probabilities
        global_centroids = self.compute_centroids(unsummed_numerator, unsqueezed_cluster_probabilities)
        estimated_global_centroids = self.global_centroids_estimator(global_centroids)
        estimated_domain_centroids = []
        for domain_idx in range(self.domains_count):
            slice_indexes = torch.nonzero(domains.view(-1) == domain_idx)
            domain_numerator = unsummed_numerator[slice_indexes].squeeze(1)
            domain_cluster_probabilities = unsqueezed_cluster_probabilities[slice_indexes].squeeze(1)
            domain_centroid = self.compute_centroids(domain_numerator, domain_cluster_probabilities)
            current_domain_centroids = self.domain_centroids_estimators[domain_idx](domain_centroid)
            estimated_domain_centroids.append(current_domain_centroids)
        return (estimated_global_centroids, estimated_domain_centroids)
<|end_body_2|>

Solution:
class CentroidEstimator:
    """Estimator for cluster centroids positions across domains"""

    def __init__(self, config, num_clusters):
        """Initializes the centroid estimator based on the provided configuration and number of clusters that will need to be estimated :param config: configuration file :param num_clusters: number of clusters for which to estimate centroids"""
        super(CentroidEstimator, self).__init__()
        self.domains_count = config.domains_count
        self.features_size = config.encoder_features_count
        self.num_clusters = num_clusters
        self.alpha = config.centroid_estimation_alpha
        self.global_centroids_estimator = FixedMatrixEstimator(self.features_size, self.num_clusters, self.alpha, initial_value=torch.zeros((self.features_size, self.num_clusters)))
        self.domain_centroids_estimators = nn.ModuleList([FixedMatrixEstimator(self.features_size, self.num_clusters, self.alpha, initial_value=torch.zeros((self.features_size, self.num_clusters))) for i in range(self.domains_count)])

    def compute_centroids(self, numerator: torch.Tensor, cluster_probabilities: torch.Tensor, eps=0.001):
        """Computes the centroids over the whole numerator and cluster probabilities tensors :param numerator: (num_points, features_size, num_clusters) tensor :param cluster_probabilities: (num_points, num_clusters) tensor :return: (features_size, num_clusters) tensor representing the centroids for each cluster"""
        numerator = torch.sum(numerator, dim=0, keepdim=False)
        cluster_probabilities = torch.sum(cluster_probabilities, dim=0, keepdim=False)
        cluster_probabilities = cluster_probabilities + eps
        return numerator / cluster_probabilities

    def forward(self, features: torch.Tensor, domains: torch.Tensor, cluster_probabilities: torch.Tensor):
        """:param features: (batch_size, features_size) tensor of features to which to estimate centroids :param domains: (1, batch_size) tensor representing the domain of each sample :param cluster_probabilities: (batch_size, num_clusters) tensor of cluster assigment probabilities :return: global_centroid, domain_centroids_list where global centroid is a (feaures_size, num_clusters) tensor representing the global centroids and domain_centroids_list is a list of length domains_count with such centroid tensors computed on each domain"""
        unsqueezed_features = features.unsqueeze(-1)
        unsqueezed_cluster_probabilities = cluster_probabilities.unsqueeze(1)
        unsummed_numerator = unsqueezed_features * unsqueezed_cluster_probabilities
        global_centroids = self.compute_centroids(unsummed_numerator, unsqueezed_cluster_probabilities)
        estimated_global_centroids = self.global_centroids_estimator(global_centroids)
        estimated_domain_centroids = []
        for domain_idx in range(self.domains_count):
            slice_indexes = torch.nonzero(domains.view(-1) == domain_idx)
            domain_numerator = unsummed_numerator[slice_indexes].squeeze(1)
            domain_cluster_probabilities = unsqueezed_cluster_probabilities[slice_indexes].squeeze(1)
            domain_centroid = self.compute_centroids(domain_numerator, domain_cluster_probabilities)
            current_domain_centroids = self.domain_centroids_estimators[domain_idx](domain_centroid)
            estimated_domain_centroids.append(current_domain_centroids)
        return (estimated_global_centroids, estimated_domain_centroids)


interpolate

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

Class description:
Implement the CubicSplines class.

Method signatures and docstrings:
- def __init__(self, a, b, orders, values=None): Creates a cubic multi-spline interpolator for many functions on a regular cartesian grid.
- def set_values(self, mvalues): Change values on the nodes of the functions to approximate.
- def interpolate(self, points, diff=False): Interpolate splines at manu points.
- def grid(self): Cartesian enumeration of all nodes.
- def __call__(self, s): Interpolate the splines at one or many points. Parameters ---------- s : (array-like with 1 or 2 dimensions) Coordinates of one point, or list of coordinates, at which the splines are interpolated. Returns: -------- res : (array-like with 1 or 2 dimensions) Vector or list of vectors containing the interpolator evaluated at `s`.

Implement the Python class `CubicSplines` described below.

Class description:
Implement the CubicSplines class.

Method signatures and docstrings:
- def __init__(self, a, b, orders, values=None): Creates a cubic multi-spline interpolator for many functions on a regular cartesian grid.
- def set_values(self, mvalues): Change values on the nodes of the functions to approximate.
- def interpolate(self, points, diff=False): Interpolate splines at manu points.
- def grid(self): Cartesian enumeration of all nodes.
- def __call__(self, s): Interpolate the splines at one or many points. Parameters ---------- s : (array-like with 1 or 2 dimensions) Coordinates of one point, or list of coordinates, at which the splines are interpolated. Returns: -------- res : (array-like with 1 or 2 dimensions) Vector or list of vectors containing the interpolator evaluated at `s`.

<|skeleton|>
class CubicSplines:

    def __init__(self, a, b, orders, values=None):
        """Creates a cubic multi-spline interpolator for many functions on a regular cartesian grid."""
        <|body_0|>

    def set_values(self, mvalues):
        """Change values on the nodes of the functions to approximate."""
        <|body_1|>

    def interpolate(self, points, diff=False):
        """Interpolate splines at manu points."""
        <|body_2|>

    def grid(self):
        """Cartesian enumeration of all nodes."""
        <|body_3|>

    def __call__(self, s):
        """Interpolate the splines at one or many points. Parameters ---------- s : (array-like with 1 or 2 dimensions) Coordinates of one point, or list of coordinates, at which the splines are interpolated. Returns: -------- res : (array-like with 1 or 2 dimensions) Vector or list of vectors containing the interpolator evaluated at `s`."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.d = len(a)
        assert len(b) == self.d
        assert len(orders) == self.d
        self.a = np.array(a, dtype=float)
        self.b = np.array(b, dtype=float)
        self.orders = np.array(orders, dtype=int)
        self.__mcoeffs__ = None
        if values is not None:
            self.set_values(values)
<|end_body_0|>

<|body_start_1|>
        mvalues = np.array(mvalues, dtype=float)
        n_sp = mvalues.shape[-1]
        mvalues = mvalues.reshape(list(self.orders) + [n_sp])
        if not np.all(np.isfinite(mvalues)):
            raise Exception('Trying to interpolate non-finite values')
        from .filter_cubic import filter_mcoeffs
        self.__mcoeffs__ = filter_mcoeffs(self.a, self.b, self.orders, mvalues)
        self.__mvalues__ = mvalues
<|end_body_1|>

<|body_start_2|>
        import time
        if points.ndim == 1:
            raise Exception('Expected 2d array. Received {}d array'.format(points.ndim))
        if points.shape[1] != self.d:
            raise Exception('Second dimension should be {}. Received : {}.'.format(self.d, points.shape[0]))
        if not np.all(np.isfinite(points)):
            raise Exception('Spline interpolator evaluated at non-finite points.')
        n_sp = self.__mcoeffs__.shape[-1]
        N = points.shape[0]
        d = points.shape[1]
        from .eval_splines import eval_cubic
        if not diff:
            grid = tuple(((self.a[i], self.b[i], self.orders[i]) for i in range(len(self.a))))
            from .eval_splines import eval_cubic
            values = np.empty((N, n_sp), dtype=float)
            eval_cubic(grid, self.__mcoeffs__, points, values)
            return values
        else:
            from .eval_cubic import vec_eval_cubic_splines_G
            values = np.empty((N, n_sp), dtype=float)
            dvalues = np.empty((N, d, n_sp), dtype=float)
            vec_eval_cubic_splines_G(self.a, self.b, self.orders, self.__mcoeffs__, points, values, dvalues)
            return [values, dvalues]
<|end_body_2|>

<|body_start_3|>
        if self.__grid__ is None:
            self.__grid__ = mlinspace(self.a, self.b, self.orders)
        return self.__grid__
<|end_body_3|>

<|body_start_4|>
        if s.ndim == 1:
            res = self.__call__(numpy.atleast_2d(s))
            return res.ravel()
        return self.interpolate(s)
<|end_body_4|>

Solution:
class CubicSplines:
    def __init__(self, a, b, orders, values=None):
        """Creates a cubic multi-spline interpolator for many functions on a regular cartesian grid."""
        self.d = len(a)
        assert len(b) == self.d
        assert len(orders) == self.d
        self.a = np.array(a, dtype=float)
        self.b = np.array(b, dtype=float)
        self.orders = np.array(orders, dtype=int)
        self.__mcoeffs__ = None
        if values is not None:
            self.set_values(values)

    def set_values(self, mvalues):
        """Change values on the nodes of the functions to approximate."""
        mvalues = np.array(mvalues, dtype=float)
        n_sp = mvalues.shape[-1]
        mvalues = mvalues.reshape(list(self.orders) + [n_sp])
        if not np.all(np.isfinite(mvalues)):
            raise Exception('Trying to interpolate non-finite values')
        from .filter_cubic import filter_mcoeffs
        self.__mcoeffs__ = filter_mcoeffs(self.a, self.b, self.orders, mvalues)
        self.__mvalues__ = mvalues

    def interpolate(self, points, diff=False):
        """Interpolate splines at manu points."""
        import time
        if points.ndim == 1:
            raise Exception('Expected 2d array. Received {}d array'.format(points.ndim))
        if points.shape[1] != self.d:
            raise Exception('Second dimension should be {}. Received : {}.'.format(self.d, points.shape[0]))
        if not np.all(np.isfinite(points)):
            raise Exception('Spline interpolator evaluated at non-finite points.')
        n_sp = self.__mcoeffs__.shape[-1]
        N = points.shape[0]
        d = points.shape[1]
        from .eval_splines import eval_cubic
        if not diff:
            grid = tuple(((self.a[i], self.b[i], self.orders[i]) for i in range(len(self.a))))
            from .eval_splines import eval_cubic
            values = np.empty((N, n_sp), dtype=float)
            eval_cubic(grid, self.__mcoeffs__, points, values)
            return values
        else:
            from .eval_cubic import vec_eval_cubic_splines_G
            values = np.empty((N, n_sp), dtype=float)
            dvalues = np.empty((N, d, n_sp), dtype=float)
            vec_eval_cubic_splines_G(self.a, self.b, self.orders, self.__mcoeffs__, points, values, dvalues)
            return [values, dvalues]

    def grid(self):
        """Cartesian enumeration of all nodes."""
        if self.__grid__ is None:
            self.__grid__ = mlinspace(self.a, self.b, self.orders)
        return self.__grid__

    def __call__(self, s):
        """Interpolate the splines at one or many points. Parameters ---------- s : (array-like with 1 or 2 dimensions) Coordinates of one point, or list of coordinates, at which the splines are interpolated. Returns: -------- res : (array-like with 1 or 2 dimensions) Vector or list of vectors containing the interpolator evaluated at `s`."""
        if s.ndim == 1:
            res = self.__call__(numpy.atleast_2d(s))
            return res.ravel()
        return self.interpolate(s)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome1(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :rtype: str

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def longestPalindrome1(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :rtype: str

<|skeleton|>
class Solution:

    def longestPalindrome1(self, s):
        """:type s: str :rtype: str"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        s_length = len(s)
        if s_length < 2:
            return s
        result_start, result_length = (0, 1)
        center1, center2 = (0, 1)
        i = 1
        while i < s_length:
            start = center1 - (i - center2)
            if s[i] == s[start]:
                length = i - start + 1
                if length > result_length:
                    result_start, result_length = (start, length)
                if start > 0:
                    i += 1
                    continue
            if center1 < center2:
                center1 += 1
            elif s[center1] == s[center1 + 1]:
                center2 += 1
            else:
                center1 += 1
                center2 += 1
            if s_length * 2 - center1 - center2 - 1 <= result_length:
                break
            i = center2 + 1
        return s[result_start:result_start + result_length]
<|end_body_0|>

<|body_start_1|>
        s_length = len(s)
        if s_length < 2:
            return s
        result_start, result_length = (0, 1)
        for i in range(s_length - 1):
            i1, i2 = (i, i)
            while i1 >= 0 and i2 < s_length and (s[i1] == s[i2]):
                i1 -= 1
                i2 += 1
            p_length = i2 - i1 - 1
            if result_length < p_length:
                result_start = i1 + 1
                result_length = p_length
            i1, i2 = (i, i + 1)
            while i1 >= 0 and i2 < s_length and (s[i1] == s[i2]):
                i1 -= 1
                i2 += 1
            p_length = i2 - i1 - 1
            if result_length < p_length:
                result_start = i1 + 1
                result_length = p_length
        return s[result_start:result_start + result_length]
<|end_body_1|>

Solution:
class Solution:
    def longestPalindrome1(self, s):
        """:type s: str :rtype: str"""
        s_length = len(s)
        if s_length < 2:
            return s
        result_start, result_length = (0, 1)
        center1, center2 = (0, 1)
        i = 1
        while i < s_length:
            start = center1 - (i - center2)
            if s[i] == s[start]:
                length = i - start + 1
                if length > result_length:
                    result_start, result_length = (start, length)
                if start > 0:
                    i += 1
                    continue
            if center1 < center2:
                center1 += 1
            elif s[center1] == s[center1 + 1]:
                center2 += 1
            else:
                center1 += 1
                center2 += 1
            if s_length * 2 - center1 - center2 - 1 <= result_length:
                break
            i = center2 + 1
        return s[result_start:result_start + result_length]

    def longestPalindrome(self, s):
        """:type s: str :rtype: str"""
        s_length = len(s)
        if s_length < 2:
            return s
        result_start, result_length = (0, 1)
        for i in range(s_length - 1):
            i1, i2 = (i, i)
            while i1 >= 0 and i2 < s_length and (s[i1] == s[i2]):
                i1 -= 1
                i2 += 1
            p_length = i2 - i1 - 1
            if result_length < p_length:
                result_start = i1 + 1
                result_length = p_length
            i1, i2 = (i, i + 1)
            while i1 >= 0 and i2 < s_length and (s[i1] == s[i2]):
                i1 -= 1
                i2 += 1
            p_length = i2 - i1 - 1
            if result_length < p_length:
                result_start = i1 + 1
                result_length = p_length
        return s[result_start:result_start + result_length]


writeCache

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

Class description:
Implement the FrontEnd class.

Method signatures and docstrings:
- def __init__(self, backend, webservername): Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:
- def initializeCache(self, cacheCapacity, processList): Initialize the cache. Cache is write-through. :param cacheCapacity: The cache capacity. :return: None
- def registerProcesses(self, processname): The incoming new process registers itself through this. A new database file is created. :param processname: The process to be reigstered. :return: None
- def getData(self, processname, requestedCount): Checks cache if the requsted recent entries are found. If not, then database is queried. :param processname: process for which entries are requested :param requestedCount: The latest count of entries. :return: The entries
- def pushData(self, processname, state, logtime): The new data is written into database first. Then its written into cache. :param processname: :param state: :param logtime: :return:
- def writeCache(self, processname, data): Writes data to cache of the process :param processname: The processname :param data: The data to be written :return:

Implement the Python class `FrontEnd` described below.

Class description:
Implement the FrontEnd class.

Method signatures and docstrings:
- def __init__(self, backend, webservername): Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:
- def initializeCache(self, cacheCapacity, processList): Initialize the cache. Cache is write-through. :param cacheCapacity: The cache capacity. :return: None
- def registerProcesses(self, processname): The incoming new process registers itself through this. A new database file is created. :param processname: The process to be reigstered. :return: None
- def getData(self, processname, requestedCount): Checks cache if the requsted recent entries are found. If not, then database is queried. :param processname: process for which entries are requested :param requestedCount: The latest count of entries. :return: The entries
- def pushData(self, processname, state, logtime): The new data is written into database first. Then its written into cache. :param processname: :param state: :param logtime: :return:
- def writeCache(self, processname, data): Writes data to cache of the process :param processname: The processname :param data: The data to be written :return:

<|skeleton|>
class FrontEnd:

    def __init__(self, backend, webservername):
        """Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:"""
        <|body_0|>

    def initializeCache(self, cacheCapacity, processList):
        """Initialize the cache. Cache is write-through. :param cacheCapacity: The cache capacity. :return: None"""
        <|body_1|>

    def registerProcesses(self, processname):
        """The incoming new process registers itself through this. A new database file is created. :param processname: The process to be reigstered. :return: None"""
        <|body_2|>

    def getData(self, processname, requestedCount):
        """Checks cache if the requsted recent entries are found. If not, then database is queried. :param processname: process for which entries are requested :param requestedCount: The latest count of entries. :return: The entries"""
        <|body_3|>

    def pushData(self, processname, state, logtime):
        """The new data is written into database first. Then its written into cache. :param processname: :param state: :param logtime: :return:"""
        <|body_4|>

    def writeCache(self, processname, data):
        """Writes data to cache of the process :param processname: The processname :param data: The data to be written :return:"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        self.webservername = webservername
        self.processToFileMap = {}
        self._backend = backend
        self.registerdProcesses = []
        self.cacheCapacity = 5
        self.cache = defaultdict(list)
<|end_body_0|>

<|body_start_1|>
        self.cacheCapacity = cacheCapacity
        print('Initializing the cache and processNames are : {}'.format(processList))
        for key in processList:
            self.cache[key] = deque([], self.cacheCapacity)
        print('Items in {} cache {}'.format(self.webservername, self.cache))
<|end_body_1|>

<|body_start_2|>
        self.registerdProcesses.append(processname)
        filepath = Constants.FileConstants.FILE_DIR + self.webservername + processname + '.txt'
        self._backend.createFile(filepath)
        self.processToFileMap[processname] = filepath
<|end_body_2|>

<|body_start_3|>
        def getKey(item):
            return item[0]
        print('Current Information in cache is {}'.format(self.cache))
        print()
        if requestedCount <= self.cacheCapacity and len(self.cache[processname]) >= requestedCount:
            t0 = time()
            print('Information found in Cache')
            data = list(self.cache[processname])
            print('Top {} records for {} from cache are {}'.format(requestedCount, processname, data[:requestedCount]))
            print('Time to fetch the information is {}'.format(time() - t0))
            return data[:requestedCount]
        else:
            print('Information not in Cache fetching from database')
            t0 = time()
            result = self._backend.readFromFile(self.processToFileMap[processname], requestedCount)
            print('Requested information is {}'.format(result))
            print()
            print('Now toring the requested information in cache')
            print()
            self.cache[processname].clear()
            sortedData = sorted(result, key=getKey)
            for index in range(self.cacheCapacity):
                if index < len(sortedData):
                    self.cache[processname].append([sortedData[index][0], sortedData[index][1]])
            print('Time to fetch the information is {}'.format(time() - t0))
<|end_body_3|>

<|body_start_4|>
        print('Contacting Backend to push state for {}'.format(processname))
        string = str(logtime) + ',' + str(processname) + ',' + str(state) + '\n'
        if processname not in self.processToFileMap:
            filepath = Constants.FileConstants.FILE_DIR + self.webservername + processname + '.txt'
            self._backend.createFile(filepath)
            self.processToFileMap[processname] = filepath
        filepath = self.processToFileMap[processname]
        if self._backend.writeToFile(string, filepath):
            print('Writing information to cache for {}'.format(processname))
            self.writeCache(processname, [str(logtime), str(state)])
<|end_body_4|>

<|body_start_5|>
        if self.cache[processname] and len(self.cache[processname]) == self.cacheCapacity:
            self.cache[processname].pop()
        self.cache[processname].appendleft(data)
<|end_body_5|>

Solution:
class FrontEnd:
    def __init__(self, backend, webservername):
        """Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:"""
        self.webservername = webservername
        self.processToFileMap = {}
        self._backend = backend
        self.registerdProcesses = []
        self.cacheCapacity = 5
        self.cache = defaultdict(list)

    def initializeCache(self, cacheCapacity, processList):
        """Initialize the cache. Cache is write-through. :param cacheCapacity: The cache capacity. :return: None"""
        self.cacheCapacity = cacheCapacity
        print('Initializing the cache and processNames are : {}'.format(processList))
        for key in processList:
            self.cache[key] = deque([], self.cacheCapacity)
        print('Items in {} cache {}'.format(self.webservername, self.cache))

    def registerProcesses(self, processname):
        """The incoming new process registers itself through this. A new database file is created. :param processname: The process to be reigstered. :return: None"""
        self.registerdProcesses.append(processname)
        filepath = Constants.FileConstants.FILE_DIR + self.webservername + processname + '.txt'
        self._backend.createFile(filepath)
        self.processToFileMap[processname] = filepath

    def getData(self, processname, requestedCount):
        """Checks cache if the requsted recent entries are found. If not, then database is queried. :param processname: process for which entries are requested :param requestedCount: The latest count of entries. :return: The entries"""
        def getKey(item):
            return item[0]
        print('Current Information in cache is {}'.format(self.cache))
        print()
        if requestedCount <= self.cacheCapacity and len(self.cache[processname]) >= requestedCount:
            t0 = time()
            print('Information found in Cache')
            data = list(self.cache[processname])
            print('Top {} records for {} from cache are {}'.format(requestedCount, processname, data[:requestedCount]))
            print('Time to fetch the information is {}'.format(time() - t0))
            return data[:requestedCount]
        else:
            print('Information not in Cache fetching from database')
            t0 = time()
            result = self._backend.readFromFile(self.processToFileMap[processname], requestedCount)
            print('Requested information is {}'.format(result))
            print()
            print('Now toring the requested information in cache')
            print()
            self.cache[processname].clear()
            sortedData = sorted(result, key=getKey)
            for index in range(self.cacheCapacity):
                if index < len(sortedData):
                    self.cache[processname].append([sortedData[index][0], sortedData[index][1]])
            print('Time to fetch the information is {}'.format(time() - t0))

    def pushData(self, processname, state, logtime):
        """The new data is written into database first. Then its written into cache. :param processname: :param state: :param logtime: :return:"""
        print('Contacting Backend to push state for {}'.format(processname))
        string = str(logtime) + ',' + str(processname) + ',' + str(state) + '\n'
        if processname not in self.processToFileMap:
            filepath = Constants.FileConstants.FILE_DIR + self.webservername + processname + '.txt'
            self._backend.createFile(filepath)
            self.processToFileMap[processname] = filepath
        filepath = self.processToFileMap[processname]
        if self._backend.writeToFile(string, filepath):
            print('Writing information to cache for {}'.format(processname))
            self.writeCache(processname, [str(logtime), str(state)])

    def writeCache(self, processname, data):
        """Writes data to cache of the process :param processname: The processname :param data: The data to be written :return:"""
        if self.cache[processname] and len(self.cache[processname]) == self.cacheCapacity:
            self.cache[processname].pop()
        self.cache[processname].appendleft(data)


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

Class description:
Matrix plane.

Method signatures and docstrings:
- def __init__(self, width: float=1.0, height: float=1.0, x: int=2, y: int=2, **kwargs: Any) -> None: Define a Matrix Plane. Matrix plane is an MxN grid matrix where you can change the height / color of each individual vertex in the matrix by ease Keyword arguments: width -- Width of the plane height -- Height of the plane x -- Number of points in X direction y -- Number of points in Y direction
- def update_grid(self) -> None: Update the grid for changes.
- def populate_grid(self) -> None: Turn grid data into plane.

Implement the Python class `MatrixPlane` described below.

Class description:
Matrix plane.

Method signatures and docstrings:
- def __init__(self, width: float=1.0, height: float=1.0, x: int=2, y: int=2, **kwargs: Any) -> None: Define a Matrix Plane. Matrix plane is an MxN grid matrix where you can change the height / color of each individual vertex in the matrix by ease Keyword arguments: width -- Width of the plane height -- Height of the plane x -- Number of points in X direction y -- Number of points in Y direction
- def update_grid(self) -> None: Update the grid for changes.
- def populate_grid(self) -> None: Turn grid data into plane.

<|skeleton|>
class MatrixPlane:
    """Matrix plane."""

    def __init__(self, width: float=1.0, height: float=1.0, x: int=2, y: int=2, **kwargs: Any) -> None:
        """Define a Matrix Plane. Matrix plane is an MxN grid matrix where you can change the height / color of each individual vertex in the matrix by ease Keyword arguments: width -- Width of the plane height -- Height of the plane x -- Number of points in X direction y -- Number of points in Y direction"""
        <|body_0|>

    def update_grid(self) -> None:
        """Update the grid for changes."""
        <|body_1|>

    def populate_grid(self) -> None:
        """Turn grid data into plane."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        self.width = width
        self.height = height
        self.x = x
        self.y = y
        self.x = max(self.x, 2)
        self.y = max(self.y, 2)
        self.grid: List[List[float]] = []
        self.color_grid: List[List[List[float]]] = []
        self.populate_grid()
<|end_body_0|>

<|body_start_1|>
        for i, j in product(range(self.x), range(self.y)):
            self._vertices[self.x * i + j][2] = self.grid[i][j]
            self._vertex_colors[self.x * i + j] = self.color_grid[i][j]
        if self.material.display == SOLID:
            self.fix_normals()
        self.refresh()
<|end_body_1|>

<|body_start_2|>
        self.clear_triangles()
        self.grid = [[0.0] * self.y for _i in range(self.x)]
        self.color_grid = [[WHITE] * self.y for _i in range(self.x)]
        step_x = self.width / (self.x - 1)
        step_y = self.height / (self.y - 1)
        step_u = 1.0 / (self.x - 1)
        step_v = 1.0 / (self.y - 1)
        c_x = self.width / 2.0
        c_y = self.height / 2.0
        for i, j in product(range(self.x), range(self.y)):
            self._vertices.append([i * step_x - c_x, j * step_y - c_y, self.grid[i][j]])
            self._texcoords.append([i * step_u, j * step_v])
            self._vertex_colors.append(WHITE)
            if i < self.x - 1 and j < self.y - 1:
                left = self.x * i + j
                right = self.x * i + (j + 1)
                top_left = self.x * (i + 1) + j
                top_right = self.x * (i + 1) + (j + 1)
                self._indices.append([top_right, right, left])
                self._indices.append([left, top_left, top_right])
        self.material._indices = self._indices
        self.fix_normals()
        self.refresh()
<|end_body_2|>

Solution:
class MatrixPlane:
    """Matrix plane."""

    def __init__(self, width: float=1.0, height: float=1.0, x: int=2, y: int=2, **kwargs: Any) -> None:
        """Define a Matrix Plane. Matrix plane is an MxN grid matrix where you can change the height / color of each individual vertex in the matrix by ease Keyword arguments: width -- Width of the plane height -- Height of the plane x -- Number of points in X direction y -- Number of points in Y direction"""
        super().__init__(**kwargs)
        self.width = width
        self.height = height
        self.x = x
        self.y = y
        self.x = max(self.x, 2)
        self.y = max(self.y, 2)
        self.grid: List[List[float]] = []
        self.color_grid: List[List[List[float]]] = []
        self.populate_grid()

    def update_grid(self) -> None:
        """Update the grid for changes."""
        for i, j in product(range(self.x), range(self.y)):
            self._vertices[self.x * i + j][2] = self.grid[i][j]
            self._vertex_colors[self.x * i + j] = self.color_grid[i][j]
        if self.material.display == SOLID:
            self.fix_normals()
        self.refresh()

    def populate_grid(self) -> None:
        """Turn grid data into plane."""
        self.clear_triangles()
        self.grid = [[0.0] * self.y for _i in range(self.x)]
        self.color_grid = [[WHITE] * self.y for _i in range(self.x)]
        step_x = self.width / (self.x - 1)
        step_y = self.height / (self.y - 1)
        step_u = 1.0 / (self.x - 1)
        step_v = 1.0 / (self.y - 1)
        c_x = self.width / 2.0
        c_y = self.height / 2.0
        for i, j in product(range(self.x), range(self.y)):
            self._vertices.append([i * step_x - c_x, j * step_y - c_y, self.grid[i][j]])
            self._texcoords.append([i * step_u, j * step_v])
            self._vertex_colors.append(WHITE)
            if i < self.x - 1 and j < self.y - 1:
                left = self.x * i + j
                right = self.x * i + (j + 1)
                top_left = self.x * (i + 1) + j
                top_right = self.x * (i + 1) + (j + 1)
                self._indices.append([top_right, right, left])
                self._indices.append([left, top_left, top_right])
        self.material._indices = self._indices
        self.fix_normals()
        self.refresh()


do

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

Class description:
Resizes an Entity

Method signatures and docstrings:
- def __init__(self, pad_amounts: tuple=(0, 0, 0, 0), mode: str='constant', pad_value: int=0) -> None: Initialize the resizer object :param pad_amounts: a tuple of the pixel count o add to each side (y_pre, y_post, x_pre, x_post) :param mode: what type of padding to use, supports numpy.pad options :param pad_value: the value to use when padding
- def do(self, img_obj: ImageEntity, random_state_obj: RandomState) -> ImageEntity: Perform the resizing :param img_obj: The input object to be resized according the specified configuration :param random_state_obj: ignored :return: The resized object

Implement the Python class `Pad` described below.

Class description:
Resizes an Entity

Method signatures and docstrings:
- def __init__(self, pad_amounts: tuple=(0, 0, 0, 0), mode: str='constant', pad_value: int=0) -> None: Initialize the resizer object :param pad_amounts: a tuple of the pixel count o add to each side (y_pre, y_post, x_pre, x_post) :param mode: what type of padding to use, supports numpy.pad options :param pad_value: the value to use when padding
- def do(self, img_obj: ImageEntity, random_state_obj: RandomState) -> ImageEntity: Perform the resizing :param img_obj: The input object to be resized according the specified configuration :param random_state_obj: ignored :return: The resized object

<|skeleton|>
class Pad:
    """Resizes an Entity"""

    def __init__(self, pad_amounts: tuple=(0, 0, 0, 0), mode: str='constant', pad_value: int=0) -> None:
        """Initialize the resizer object :param pad_amounts: a tuple of the pixel count o add to each side (y_pre, y_post, x_pre, x_post) :param mode: what type of padding to use, supports numpy.pad options :param pad_value: the value to use when padding"""
        <|body_0|>

    def do(self, img_obj: ImageEntity, random_state_obj: RandomState) -> ImageEntity:
        """Perform the resizing :param img_obj: The input object to be resized according the specified configuration :param random_state_obj: ignored :return: The resized object"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.pad_amounts = pad_amounts
        self.mode = mode
        self.pad_value = pad_value
<|end_body_0|>

<|body_start_1|>
        if np.count_nonzero(np.asarray(self.pad_amounts)) == 0:
            return img_obj
        img = img_obj.get_data()
        msk = img_obj.get_mask()
        kwargs = {}
        if self.mode == 'constant':
            kwargs = {'constant_values': self.pad_value}
        if len(img.shape) == 2:
            img_out = np.pad(img, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3])), mode=self.mode, **kwargs)
        elif len(img.shape) == 3:
            img_out = np.pad(img, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3]), (0, 0)), mode=self.mode, **kwargs)
        else:
            raise RuntimeError('Unexpected image shape: {}'.format(img.shape))
        if len(msk.shape) == 2:
            mask_out = np.pad(msk, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3])), mode=self.mode, **kwargs)
        elif len(msk.shape) == 3:
            mask_out = np.pad(msk, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3]), (0, 0)), mode=self.mode, **kwargs)
        else:
            raise RuntimeError('Unexpected mask shape: {}'.format(msk.shape))
        logger.info('Padded image of shape=%s to shape=%s' % (str(img_obj.get_data().shape), str(img.shape)))
        return GenericImageEntity(img_out, mask_out)
<|end_body_1|>

Solution:
class Pad:
    """Resizes an Entity"""

    def __init__(self, pad_amounts: tuple=(0, 0, 0, 0), mode: str='constant', pad_value: int=0) -> None:
        """Initialize the resizer object :param pad_amounts: a tuple of the pixel count o add to each side (y_pre, y_post, x_pre, x_post) :param mode: what type of padding to use, supports numpy.pad options :param pad_value: the value to use when padding"""
        self.pad_amounts = pad_amounts
        self.mode = mode
        self.pad_value = pad_value

    def do(self, img_obj: ImageEntity, random_state_obj: RandomState) -> ImageEntity:
        """Perform the resizing :param img_obj: The input object to be resized according the specified configuration :param random_state_obj: ignored :return: The resized object"""
        if np.count_nonzero(np.asarray(self.pad_amounts)) == 0:
            return img_obj
        img = img_obj.get_data()
        msk = img_obj.get_mask()
        kwargs = {}
        if self.mode == 'constant':
            kwargs = {'constant_values': self.pad_value}
        if len(img.shape) == 2:
            img_out = np.pad(img, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3])), mode=self.mode, **kwargs)
        elif len(img.shape) == 3:
            img_out = np.pad(img, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3]), (0, 0)), mode=self.mode, **kwargs)
        else:
            raise RuntimeError('Unexpected image shape: {}'.format(img.shape))
        if len(msk.shape) == 2:
            mask_out = np.pad(msk, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3])), mode=self.mode, **kwargs)
        elif len(msk.shape) == 3:
            mask_out = np.pad(msk, pad_width=((self.pad_amounts[0], self.pad_amounts[1]), (self.pad_amounts[2], self.pad_amounts[3]), (0, 0)), mode=self.mode, **kwargs)
        else:
            raise RuntimeError('Unexpected mask shape: {}'.format(msk.shape))
        logger.info('Padded image of shape=%s to shape=%s' % (str(img_obj.get_data().shape), str(img.shape)))
        return GenericImageEntity(img_out, mask_out)


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

Class description:
Lazy sampling from an infinite Dirchlet distribution.

Method signatures and docstrings:
- def __init__(self, strength, alpha): Initialise with a strength parameter and an infinite prior. The infinite prior should be an infinite multinomial parameterisation itself.
- def _extend(self): Extend the stick breaking construction by one partition.

Implement the Python class `InfiniteDirichlet` described below.

Class description:
Lazy sampling from an infinite Dirchlet distribution.

Method signatures and docstrings:
- def __init__(self, strength, alpha): Initialise with a strength parameter and an infinite prior. The infinite prior should be an infinite multinomial parameterisation itself.
- def _extend(self): Extend the stick breaking construction by one partition.

<|skeleton|>
class InfiniteDirichlet:
    """Lazy sampling from an infinite Dirchlet distribution."""

    def __init__(self, strength, alpha):
        """Initialise with a strength parameter and an infinite prior. The infinite prior should be an infinite multinomial parameterisation itself."""
        <|body_0|>

    def _extend(self):
        """Extend the stick breaking construction by one partition."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        InfiniteMultinomial.__init__(self)
        self.strength = strength
        self.alpha = alpha
<|end_body_0|>

<|body_start_1|>
        j = self.num_partitions()
        phi_j = rBeta(self.strength * self.alpha.x(j), self.strength * (1.0 - self.alpha.partition_start(j + 1)))
        assert 0.0 <= phi_j
        assert phi_j <= 1.0
        top = self.last()
        top = top + (1.0 - self.partition_end(j - 1)) * phi_j
        return top
<|end_body_1|>

Solution:
class InfiniteDirichlet:
    """Lazy sampling from an infinite Dirchlet distribution."""

    def __init__(self, strength, alpha):
        """Initialise with a strength parameter and an infinite prior. The infinite prior should be an infinite multinomial parameterisation itself."""
        InfiniteMultinomial.__init__(self)
        self.strength = strength
        self.alpha = alpha

    def _extend(self):
        """Extend the stick breaking construction by one partition."""
        j = self.num_partitions()
        phi_j = rBeta(self.strength * self.alpha.x(j), self.strength * (1.0 - self.alpha.partition_start(j + 1)))
        assert 0.0 <= phi_j
        assert phi_j <= 1.0
        top = self.last()
        top = top + (1.0 - self.partition_end(j - 1)) * phi_j
        return top


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

Class description:
This class is used to parse the json object for category.

Method signatures and docstrings:
- def get_categories(self, resp): This method parses the json for categories list. Args: resp(dict):Response containing json for categories list. Returns: list of instance: List of categories object.
- def to_category(self, resp): This method parses the given response and returns category object. Args: resp(dict): Dictionary containing json object for category. Returns: instance: Category object.
- def to_json(self, category): This method is used to convert category object to json format. Args: category(instance): Category object. Returns: dict: Dictionary containing json object for category object.

Implement the Python class `CategoryParser` described below.

Class description:
This class is used to parse the json object for category.

Method signatures and docstrings:
- def get_categories(self, resp): This method parses the json for categories list. Args: resp(dict):Response containing json for categories list. Returns: list of instance: List of categories object.
- def to_category(self, resp): This method parses the given response and returns category object. Args: resp(dict): Dictionary containing json object for category. Returns: instance: Category object.
- def to_json(self, category): This method is used to convert category object to json format. Args: category(instance): Category object. Returns: dict: Dictionary containing json object for category object.

<|skeleton|>
class CategoryParser:
    """This class is used to parse the json object for category."""

    def get_categories(self, resp):
        """This method parses the json for categories list. Args: resp(dict):Response containing json for categories list. Returns: list of instance: List of categories object."""
        <|body_0|>

    def to_category(self, resp):
        """This method parses the given response and returns category object. Args: resp(dict): Dictionary containing json object for category. Returns: instance: Category object."""
        <|body_1|>

    def to_json(self, category):
        """This method is used to convert category object to json format. Args: category(instance): Category object. Returns: dict: Dictionary containing json object for category object."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        categories = []
        for value in resp['categories']:
            category = self.to_category(value)
            categories.append(category)
        return categories
<|end_body_0|>

<|body_start_1|>
        category = Category()
        if 'id' in resp:
            category.set_id(resp['id'])
        if 'name' in resp:
            category.set_name(resp['name'])
        return category
<|end_body_1|>

<|body_start_2|>
        data = {}
        if category.get_name() != '':
            data['name'] = category.get_name()
        return data
<|end_body_2|>

Solution:
class CategoryParser:
    """This class is used to parse the json object for category."""

    def get_categories(self, resp):
        """This method parses the json for categories list. Args: resp(dict):Response containing json for categories list. Returns: list of instance: List of categories object."""
        categories = []
        for value in resp['categories']:
            category = self.to_category(value)
            categories.append(category)
        return categories

    def to_category(self, resp):
        """This method parses the given response and returns category object. Args: resp(dict): Dictionary containing json object for category. Returns: instance: Category object."""
        category = Category()
        if 'id' in resp:
            category.set_id(resp['id'])
        if 'name' in resp:
            category.set_name(resp['name'])
        return category

    def to_json(self, category):
        """This method is used to convert category object to json format. Args: category(instance): Category object. Returns: dict: Dictionary containing json object for category object."""
        data = {}
        if category.get_name() != '':
            data['name'] = category.get_name()
        return data


delete

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

Class description:
This view is for handling operation for Artist Information. It will perform GET, POST, DELETE request for artist information.

Method signatures and docstrings:
- def get(self, pk=None): Performs GET operation. Returns a Response with all artist information
- def post(self, request): Performs POST operation. Creates new artist record. Returns a Response with artist information
- def delete(self, request): Performs DELETE operation. Deleted the artisrt ID record from the DB. Returns a Response with all artist information

Implement the Python class `ArtistInformationView` described below.

Class description:
This view is for handling operation for Artist Information. It will perform GET, POST, DELETE request for artist information.

Method signatures and docstrings:
- def get(self, pk=None): Performs GET operation. Returns a Response with all artist information
- def post(self, request): Performs POST operation. Creates new artist record. Returns a Response with artist information
- def delete(self, request): Performs DELETE operation. Deleted the artisrt ID record from the DB. Returns a Response with all artist information

<|skeleton|>
class ArtistInformationView:
    """This view is for handling operation for Artist Information. It will perform GET, POST, DELETE request for artist information."""

    def get(self, pk=None):
        """Performs GET operation. Returns a Response with all artist information"""
        <|body_0|>

    def post(self, request):
        """Performs POST operation. Creates new artist record. Returns a Response with artist information"""
        <|body_1|>

    def delete(self, request):
        """Performs DELETE operation. Deleted the artisrt ID record from the DB. Returns a Response with all artist information"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        """This will handel all the GET request."""
        track_info = ArtistInformation.objects.all()
        serializer = ArtistInformationSerializer(track_info, many=True)
        return Response({'artist_info': serializer.data})
<|end_body_0|>

<|body_start_1|>
        artist_name = request.data.get('name')
        serializer = ArtistInformationSerializer(data={'artistName': artist_name})
        if serializer.is_valid(raise_exception=True):
            artist_save = serializer.save()
        return Response({'response': 'SUCCESS', 'artistId': '{}'.format(artist_save.artistId), 'message': "Artist'{}' created successfully".format(artist_save.artistName)})
<|end_body_1|>

<|body_start_2|>
        artistId = request.data.get('artistId')
        try:
            del_obj = ArtistInformation.objects.get(artistId=artistId)
            del_obj.delete()
            return Response({'response': 'SUCCESS', 'message': "Artist '{}' Deleted Successfully".format(artistId)})
        except ArtistInformation.DoesNotExist:
            return Response({'response': 'FAIL'})
<|end_body_2|>

Solution:
class ArtistInformationView:
    """This view is for handling operation for Artist Information. It will perform GET, POST, DELETE request for artist information."""

    def get(self, pk=None):
        """Performs GET operation. Returns a Response with all artist information"""
        """This will handel all the GET request."""
        track_info = ArtistInformation.objects.all()
        serializer = ArtistInformationSerializer(track_info, many=True)
        return Response({'artist_info': serializer.data})

    def post(self, request):
        """Performs POST operation. Creates new artist record. Returns a Response with artist information"""
        artist_name = request.data.get('name')
        serializer = ArtistInformationSerializer(data={'artistName': artist_name})
        if serializer.is_valid(raise_exception=True):
            artist_save = serializer.save()
        return Response({'response': 'SUCCESS', 'artistId': '{}'.format(artist_save.artistId), 'message': "Artist'{}' created successfully".format(artist_save.artistName)})

    def delete(self, request):
        """Performs DELETE operation. Deleted the artisrt ID record from the DB. Returns a Response with all artist information"""
        artistId = request.data.get('artistId')
        try:
            del_obj = ArtistInformation.objects.get(artistId=artistId)
            del_obj.delete()
            return Response({'response': 'SUCCESS', 'message': "Artist '{}' Deleted Successfully".format(artistId)})
        except ArtistInformation.DoesNotExist:
            return Response({'response': 'FAIL'})


iteration

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

Class description:
Implement the VoterModel class.

Method signatures and docstrings:
- def __init__(self, graph, seed=None): Model Constructor :param graph: A networkx graph object
- def iteration(self, node_status=True): Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->status)

Implement the Python class `VoterModel` described below.

Class description:
Implement the VoterModel class.

Method signatures and docstrings:
- def __init__(self, graph, seed=None): Model Constructor :param graph: A networkx graph object
- def iteration(self, node_status=True): Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->status)

<|skeleton|>
class VoterModel:

    def __init__(self, graph, seed=None):
        """Model Constructor :param graph: A networkx graph object"""
        <|body_0|>

    def iteration(self, node_status=True):
        """Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->status)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(self.__class__, self).__init__(graph, seed)
        self.available_statuses = {'Susceptible': 0, 'Infected': 1}
        self.name = 'Voter'
<|end_body_0|>

<|body_start_1|>
        self.clean_initial_status(self.available_statuses.values())
        if self.actual_iteration == 0:
            self.actual_iteration += 1
            delta, node_count, status_delta = self.status_delta(self.status)
            if node_status:
                return {'iteration': 0, 'status': self.status.copy(), 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
            else:
                return {'iteration': 0, 'status': {}, 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
        listener = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
        neighbours = list(self.graph.neighbors(listener))
        if self.graph.directed:
            neighbours = list(self.graph.predecessors(listener))
        speaker = neighbours[np.random.randint(0, len(neighbours))]
        delta = {listener: self.status[speaker]}
        self.status[listener] = self.status[speaker]
        node_count = {st: len([n for n in self.status if self.status[n] == st]) for st in self.available_statuses.values()}
        status_delta = {st: 0 for st in self.available_statuses.values()}
        status_delta[self.status[speaker]] += 1
        for x in self.available_statuses.values():
            if x != self.status[speaker]:
                status_delta[x] -= 1
        self.actual_iteration += 1
        if node_status:
            return {'iteration': self.actual_iteration - 1, 'status': delta.copy(), 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
        else:
            return {'iteration': self.actual_iteration - 1, 'status': {}, 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
<|end_body_1|>

Solution:
class VoterModel:
    def __init__(self, graph, seed=None):
        """Model Constructor :param graph: A networkx graph object"""
        super(self.__class__, self).__init__(graph, seed)
        self.available_statuses = {'Susceptible': 0, 'Infected': 1}
        self.name = 'Voter'

    def iteration(self, node_status=True):
        """Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->status)"""
        self.clean_initial_status(self.available_statuses.values())
        if self.actual_iteration == 0:
            self.actual_iteration += 1
            delta, node_count, status_delta = self.status_delta(self.status)
            if node_status:
                return {'iteration': 0, 'status': self.status.copy(), 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
            else:
                return {'iteration': 0, 'status': {}, 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
        listener = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
        neighbours = list(self.graph.neighbors(listener))
        if self.graph.directed:
            neighbours = list(self.graph.predecessors(listener))
        speaker = neighbours[np.random.randint(0, len(neighbours))]
        delta = {listener: self.status[speaker]}
        self.status[listener] = self.status[speaker]
        node_count = {st: len([n for n in self.status if self.status[n] == st]) for st in self.available_statuses.values()}
        status_delta = {st: 0 for st in self.available_statuses.values()}
        status_delta[self.status[speaker]] += 1
        for x in self.available_statuses.values():
            if x != self.status[speaker]:
                status_delta[x] -= 1
        self.actual_iteration += 1
        if node_status:
            return {'iteration': self.actual_iteration - 1, 'status': delta.copy(), 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}
        else:
            return {'iteration': self.actual_iteration - 1, 'status': {}, 'node_count': node_count.copy(), 'status_delta': status_delta.copy()}


forward

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

Class description:
Implement the ScaledDotProductAttention class.

Method signatures and docstrings:
- def __init__(self, device, p: float=None): scaled dot product attention 구현 클래스 Args: device: device type p (float): dropout probability
- def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, _r: torch.Tensor, uT: torch.Tensor, vT: torch.Tensor, mask: torch.Tensor): forward 함수 Args: q (torch.Tensor(bs, head, q_len, d_head)): query k (torch.Tensor(bs, head, k_len, d_head)): key v (torch.Tensor(bs, head, k_len, d_head)): value _r (torch.Tensor(head, m_len + q_len, d_head)): position embedding uT (torch.Tensor(n_head, 1, d_head)): segment bias vT (torch.Tensor(n_head, 1, d_head)): position bias mask (torch.Tenso(bs, 1, q_len, k_len)): masking idx Returns output (torch.Tensor(bs, head, q_len, d_head)): forward 출력값
- def left_shift(self, x: torch.Tensor): left shift 함수 Args: x (torch.Tensor(bs, head, q_len, k_len)): before left shift BD Returns: x (torch.Tensor(bs, head, q_len, k_len)): left shifted BD ex. (0) (2) (3) (4) (5) a00 a01 a02 0 a00 a01 a02 0 a00 a01 a02 0 a10 a02 0 0 a10 a11 a12 => 0 a10 a11 a12 => a02 0 a10 => a11 a12 0 => a11 a12 0 a20 a21 a22 0 a20 a21 a22 a11 a12 0 a20 a21 a22 a20 a21 a22 a19 a21 a22

Implement the Python class `ScaledDotProductAttention` described below.

Class description:
Implement the ScaledDotProductAttention class.

Method signatures and docstrings:
- def __init__(self, device, p: float=None): scaled dot product attention 구현 클래스 Args: device: device type p (float): dropout probability
- def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, _r: torch.Tensor, uT: torch.Tensor, vT: torch.Tensor, mask: torch.Tensor): forward 함수 Args: q (torch.Tensor(bs, head, q_len, d_head)): query k (torch.Tensor(bs, head, k_len, d_head)): key v (torch.Tensor(bs, head, k_len, d_head)): value _r (torch.Tensor(head, m_len + q_len, d_head)): position embedding uT (torch.Tensor(n_head, 1, d_head)): segment bias vT (torch.Tensor(n_head, 1, d_head)): position bias mask (torch.Tenso(bs, 1, q_len, k_len)): masking idx Returns output (torch.Tensor(bs, head, q_len, d_head)): forward 출력값
- def left_shift(self, x: torch.Tensor): left shift 함수 Args: x (torch.Tensor(bs, head, q_len, k_len)): before left shift BD Returns: x (torch.Tensor(bs, head, q_len, k_len)): left shifted BD ex. (0) (2) (3) (4) (5) a00 a01 a02 0 a00 a01 a02 0 a00 a01 a02 0 a10 a02 0 0 a10 a11 a12 => 0 a10 a11 a12 => a02 0 a10 => a11 a12 0 => a11 a12 0 a20 a21 a22 0 a20 a21 a22 a11 a12 0 a20 a21 a22 a20 a21 a22 a19 a21 a22

<|skeleton|>
class ScaledDotProductAttention:

    def __init__(self, device, p: float=None):
        """scaled dot product attention 구현 클래스 Args: device: device type p (float): dropout probability"""
        <|body_0|>

    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, _r: torch.Tensor, uT: torch.Tensor, vT: torch.Tensor, mask: torch.Tensor):
        """forward 함수 Args: q (torch.Tensor(bs, head, q_len, d_head)): query k (torch.Tensor(bs, head, k_len, d_head)): key v (torch.Tensor(bs, head, k_len, d_head)): value _r (torch.Tensor(head, m_len + q_len, d_head)): position embedding uT (torch.Tensor(n_head, 1, d_head)): segment bias vT (torch.Tensor(n_head, 1, d_head)): position bias mask (torch.Tenso(bs, 1, q_len, k_len)): masking idx Returns output (torch.Tensor(bs, head, q_len, d_head)): forward 출력값"""
        <|body_1|>

    def left_shift(self, x: torch.Tensor):
        """left shift 함수 Args: x (torch.Tensor(bs, head, q_len, k_len)): before left shift BD Returns: x (torch.Tensor(bs, head, q_len, k_len)): left shifted BD ex. (0) (2) (3) (4) (5) a00 a01 a02 0 a00 a01 a02 0 a00 a01 a02 0 a10 a02 0 0 a10 a11 a12 => 0 a10 a11 a12 => a02 0 a10 => a11 a12 0 => a11 a12 0 a20 a21 a22 0 a20 a21 a22 a11 a12 0 a20 a21 a22 a20 a21 a22 a19 a21 a22"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.device = device
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = None if p is None else nn.Dropout(p=p)
<|end_body_0|>

<|body_start_1|>
        d_head = q.size(-1)
        Q_uT = q + uT
        AC = Q_uT @ k.transpose(-1, -2)
        Q_vT = q + vT
        BD = Q_vT @ _r.transpose(-1, -2)
        BD = self.left_shift(BD)
        weight = AC + BD
        weight /= math.sqrt(d_head)
        if mask is not None:
            weight.masked_fill(mask == False, 1e-12)
        weight = self.softmax(weight)
        if self.dropout is not None:
            weight = self.dropout(weight)
        output = weight @ v
        return output
<|end_body_1|>

<|body_start_2|>
        bs, head, q_len, k_len = x.size()
        m_len = k_len - q_len
        row_pad = torch.zeros(bs, head, q_len, 1).to(self.device)
        col_pad = torch.zeros(bs, head, m_len, k_len + 1).to(self.device)
        x = torch.cat([row_pad, x], dim=-1)
        x = torch.cat([x, col_pad], dim=-2)
        x = x.view(bs, head, -1, k_len)
        x_1 = x[:, :, 1:q_len + 1, :q_len].tril(diagonal=1)
        x_2 = x[:, :, :q_len, q_len:k_len]
        output = torch.cat([x_2, x_1], dim=-1)
        return output
<|end_body_2|>

Solution:
class ScaledDotProductAttention:
    def __init__(self, device, p: float=None):
        """scaled dot product attention 구현 클래스 Args: device: device type p (float): dropout probability"""
        super().__init__()
        self.device = device
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = None if p is None else nn.Dropout(p=p)

    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, _r: torch.Tensor, uT: torch.Tensor, vT: torch.Tensor, mask: torch.Tensor):
        """forward 함수 Args: q (torch.Tensor(bs, head, q_len, d_head)): query k (torch.Tensor(bs, head, k_len, d_head)): key v (torch.Tensor(bs, head, k_len, d_head)): value _r (torch.Tensor(head, m_len + q_len, d_head)): position embedding uT (torch.Tensor(n_head, 1, d_head)): segment bias vT (torch.Tensor(n_head, 1, d_head)): position bias mask (torch.Tenso(bs, 1, q_len, k_len)): masking idx Returns output (torch.Tensor(bs, head, q_len, d_head)): forward 출력값"""
        d_head = q.size(-1)
        Q_uT = q + uT
        AC = Q_uT @ k.transpose(-1, -2)
        Q_vT = q + vT
        BD = Q_vT @ _r.transpose(-1, -2)
        BD = self.left_shift(BD)
        weight = AC + BD
        weight /= math.sqrt(d_head)
        if mask is not None:
            weight.masked_fill(mask == False, 1e-12)
        weight = self.softmax(weight)
        if self.dropout is not None:
            weight = self.dropout(weight)
        output = weight @ v
        return output

    def left_shift(self, x: torch.Tensor):
        """left shift 함수 Args: x (torch.Tensor(bs, head, q_len, k_len)): before left shift BD Returns: x (torch.Tensor(bs, head, q_len, k_len)): left shifted BD ex. (0) (2) (3) (4) (5) a00 a01 a02 0 a00 a01 a02 0 a00 a01 a02 0 a10 a02 0 0 a10 a11 a12 => 0 a10 a11 a12 => a02 0 a10 => a11 a12 0 => a11 a12 0 a20 a21 a22 0 a20 a21 a22 a11 a12 0 a20 a21 a22 a20 a21 a22 a19 a21 a22"""
        bs, head, q_len, k_len = x.size()
        m_len = k_len - q_len
        row_pad = torch.zeros(bs, head, q_len, 1).to(self.device)
        col_pad = torch.zeros(bs, head, m_len, k_len + 1).to(self.device)
        x = torch.cat([row_pad, x], dim=-1)
        x = torch.cat([x, col_pad], dim=-2)
        x = x.view(bs, head, -1, k_len)
        x_1 = x[:, :, 1:q_len + 1, :q_len].tril(diagonal=1)
        x_2 = x[:, :, :q_len, q_len:k_len]
        output = torch.cat([x_2, x_1], dim=-1)
        return output


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

Class description:
This code was taken from: https://github.com/jaantollander/OneEuroFilter and written by Jaan Tollander de Balsch We first need to init the OneEuroFilter with the base parameters and one data point. Then we repeatedly call OneEuroFilter(t,x) on the data points to filter i.e. oneEuro = OneEuroFilter(t0,x0,min_cutoff=50.0,beta=4.0) ... x_filt = oneEuro(ti,xi)

Method signatures and docstrings:
- def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, d_cutoff=1.0): Initialize the one euro filter.
- def __call__(self, t, x): Compute the filtered signal.

Implement the Python class `OneEuroFilter` described below.

Class description:
This code was taken from: https://github.com/jaantollander/OneEuroFilter and written by Jaan Tollander de Balsch We first need to init the OneEuroFilter with the base parameters and one data point. Then we repeatedly call OneEuroFilter(t,x) on the data points to filter i.e. oneEuro = OneEuroFilter(t0,x0,min_cutoff=50.0,beta=4.0) ... x_filt = oneEuro(ti,xi)

Method signatures and docstrings:
- def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, d_cutoff=1.0): Initialize the one euro filter.
- def __call__(self, t, x): Compute the filtered signal.

<|skeleton|>
class OneEuroFilter:
    """This code was taken from: https://github.com/jaantollander/OneEuroFilter and written by Jaan Tollander de Balsch We first need to init the OneEuroFilter with the base parameters and one data point. Then we repeatedly call OneEuroFilter(t,x) on the data points to filter i.e. oneEuro = OneEuroFilter(t0,x0,min_cutoff=50.0,beta=4.0) ... x_filt = oneEuro(ti,xi)"""

    def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, d_cutoff=1.0):
        """Initialize the one euro filter."""
        <|body_0|>

    def __call__(self, t, x):
        """Compute the filtered signal."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.min_cutoff = float(min_cutoff)
        self.beta = float(beta)
        self.d_cutoff = float(d_cutoff)
        self.x_prev = float(x0)
        self.dx_prev = float(dx0)
        self.t_prev = float(t0)
<|end_body_0|>

<|body_start_1|>
        t_e = t - self.t_prev
        a_d = smoothing_factor(t_e, self.d_cutoff)
        dx = (x - self.x_prev) / t_e
        dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)
        cutoff = self.min_cutoff + self.beta * abs(dx_hat)
        a = smoothing_factor(t_e, cutoff)
        x_hat = exponential_smoothing(a, x, self.x_prev)
        self.x_prev = x_hat
        self.dx_prev = dx_hat
        self.t_prev = t
        return x_hat
<|end_body_1|>

Solution:
class OneEuroFilter:
    """This code was taken from: https://github.com/jaantollander/OneEuroFilter and written by Jaan Tollander de Balsch We first need to init the OneEuroFilter with the base parameters and one data point. Then we repeatedly call OneEuroFilter(t,x) on the data points to filter i.e. oneEuro = OneEuroFilter(t0,x0,min_cutoff=50.0,beta=4.0) ... x_filt = oneEuro(ti,xi)"""

    def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, d_cutoff=1.0):
        """Initialize the one euro filter."""
        self.min_cutoff = float(min_cutoff)
        self.beta = float(beta)
        self.d_cutoff = float(d_cutoff)
        self.x_prev = float(x0)
        self.dx_prev = float(dx0)
        self.t_prev = float(t0)

    def __call__(self, t, x):
        """Compute the filtered signal."""
        t_e = t - self.t_prev
        a_d = smoothing_factor(t_e, self.d_cutoff)
        dx = (x - self.x_prev) / t_e
        dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)
        cutoff = self.min_cutoff + self.beta * abs(dx_hat)
        a = smoothing_factor(t_e, cutoff)
        x_hat = exponential_smoothing(a, x, self.x_prev)
        self.x_prev = x_hat
        self.dx_prev = dx_hat
        self.t_prev = t
        return x_hat


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

Class description:
Tests get_cirq_analytical_expectation.

Method signatures and docstrings:
- def test_get_cirq_analytical_expectation_op(self): Input check the wrapper for the cirq analytical expectation op.
- def test_cirq_analytical_expectation_op_inputs(self): Test input checking in the state sim op.
- def test_analytic_expectation_empty_circuit(self): Test empty circuits
- def test_analytic_expectation_no_circuit(self): Test empty tensors with no circuits at all.

Implement the Python class `CirqAnalyticalExpectationTest` described below.

Class description:
Tests get_cirq_analytical_expectation.

Method signatures and docstrings:
- def test_get_cirq_analytical_expectation_op(self): Input check the wrapper for the cirq analytical expectation op.
- def test_cirq_analytical_expectation_op_inputs(self): Test input checking in the state sim op.
- def test_analytic_expectation_empty_circuit(self): Test empty circuits
- def test_analytic_expectation_no_circuit(self): Test empty tensors with no circuits at all.

<|skeleton|>
class CirqAnalyticalExpectationTest:
    """Tests get_cirq_analytical_expectation."""

    def test_get_cirq_analytical_expectation_op(self):
        """Input check the wrapper for the cirq analytical expectation op."""
        <|body_0|>

    def test_cirq_analytical_expectation_op_inputs(self):
        """Test input checking in the state sim op."""
        <|body_1|>

    def test_analytic_expectation_empty_circuit(self):
        """Test empty circuits"""
        <|body_2|>

    def test_analytic_expectation_no_circuit(self):
        """Test empty tensors with no circuits at all."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        with self.assertRaisesRegex(TypeError, 'cirq.sim.simulator.SimulatesExpectationValues.'):
            cirq_ops._get_cirq_analytical_expectation('junk')
        cirq_ops._get_cirq_analytical_expectation()
        cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        cirq_ops._get_cirq_analytical_expectation(cirq.DensityMatrixSimulator())
<|end_body_0|>

<|body_start_1|>
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        bits = cirq.GridQubit.rect(1, 5)
        test_circuit = serializer.serialize_circuit(cirq.testing.random_circuit(bits, MOMENT_DEPTH, 0.9)).SerializeToString()
        test_pauli_sum = serializer.serialize_paulisum(cirq.PauliSum.from_pauli_strings([cirq.Z(bits[0])])).SerializeToString()
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'symbol_names tensor must be of type string'):
            _ = test_op([test_circuit], [0], [[0]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'programs tensor must be of type string'):
            _ = test_op([0], ['rx'], [[0]], [test_pauli_sum])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'real-valued numeric tensor.'):
            _ = test_op([test_circuit], ['rx'], 'junk', [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'size of symbol_names tensor must match'):
            _ = test_op([test_circuit], ['rx'], [[1, 1]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'size of symbol_names tensor must match'):
            _ = test_op([test_circuit], ['rx', 'ry'], [[1]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'first dimension of symbol_values tensor'):
            _ = test_op([test_circuit, test_circuit], ['rx'], [[1]], [test_pauli_sum])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums tensor must be of type string.'):
            _ = test_op([test_circuit], ['rx'], [[1]], 0)
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums tensor must have the same batch shape'):
            _ = test_op([test_circuit], ['rx'], [[1]], [[test_pauli_sum], [test_pauli_sum]])
        _ = test_op([test_circuit], ['rx'], [[1]], [[test_pauli_sum]])
        _ = test_op([test_circuit], [], [[]], [[test_pauli_sum]])
<|end_body_1|>

<|body_start_2|>
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        bits = cirq.GridQubit.rect(1, 5)
        test_pauli_sum = serializer.serialize_paulisum(cirq.PauliSum.from_pauli_strings([cirq.Z(bits[0])])).SerializeToString()
        test_empty_circuit = serializer.serialize_circuit(cirq.Circuit()).SerializeToString()
        _ = test_op([test_empty_circuit], [], [[]], [[test_pauli_sum]])
<|end_body_2|>

<|body_start_3|>
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        empty_programs = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)
        empty_values = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32)
        empty_paulis = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string)
        _ = test_op(empty_programs, [], empty_values, empty_paulis)
<|end_body_3|>

Solution:
class CirqAnalyticalExpectationTest:
    """Tests get_cirq_analytical_expectation."""

    def test_get_cirq_analytical_expectation_op(self):
        """Input check the wrapper for the cirq analytical expectation op."""
        with self.assertRaisesRegex(TypeError, 'cirq.sim.simulator.SimulatesExpectationValues.'):
            cirq_ops._get_cirq_analytical_expectation('junk')
        cirq_ops._get_cirq_analytical_expectation()
        cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        cirq_ops._get_cirq_analytical_expectation(cirq.DensityMatrixSimulator())

    def test_cirq_analytical_expectation_op_inputs(self):
        """Test input checking in the state sim op."""
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        bits = cirq.GridQubit.rect(1, 5)
        test_circuit = serializer.serialize_circuit(cirq.testing.random_circuit(bits, MOMENT_DEPTH, 0.9)).SerializeToString()
        test_pauli_sum = serializer.serialize_paulisum(cirq.PauliSum.from_pauli_strings([cirq.Z(bits[0])])).SerializeToString()
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'symbol_names tensor must be of type string'):
            _ = test_op([test_circuit], [0], [[0]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'programs tensor must be of type string'):
            _ = test_op([0], ['rx'], [[0]], [test_pauli_sum])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'real-valued numeric tensor.'):
            _ = test_op([test_circuit], ['rx'], 'junk', [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'size of symbol_names tensor must match'):
            _ = test_op([test_circuit], ['rx'], [[1, 1]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'size of symbol_names tensor must match'):
            _ = test_op([test_circuit], ['rx', 'ry'], [[1]], [[test_pauli_sum]])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'first dimension of symbol_values tensor'):
            _ = test_op([test_circuit, test_circuit], ['rx'], [[1]], [test_pauli_sum])
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums tensor must be of type string.'):
            _ = test_op([test_circuit], ['rx'], [[1]], 0)
        with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums tensor must have the same batch shape'):
            _ = test_op([test_circuit], ['rx'], [[1]], [[test_pauli_sum], [test_pauli_sum]])
        _ = test_op([test_circuit], ['rx'], [[1]], [[test_pauli_sum]])
        _ = test_op([test_circuit], [], [[]], [[test_pauli_sum]])

    def test_analytic_expectation_empty_circuit(self):
        """Test empty circuits"""
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        bits = cirq.GridQubit.rect(1, 5)
        test_pauli_sum = serializer.serialize_paulisum(cirq.PauliSum.from_pauli_strings([cirq.Z(bits[0])])).SerializeToString()
        test_empty_circuit = serializer.serialize_circuit(cirq.Circuit()).SerializeToString()
        _ = test_op([test_empty_circuit], [], [[]], [[test_pauli_sum]])

    def test_analytic_expectation_no_circuit(self):
        """Test empty tensors with no circuits at all."""
        test_op = cirq_ops._get_cirq_analytical_expectation(cirq.Simulator())
        empty_programs = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)
        empty_values = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32)
        empty_paulis = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string)
        _ = test_op(empty_programs, [], empty_values, empty_paulis)


backward

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

Class description:
Encoding the orientation information and generating orientation- sensitive features. The details are described in the paper `Align Deep Features for Oriented Object Detection <https://arxiv.org/abs/2008.09397>_`.

Method signatures and docstrings:
- def forward(ctx, input: torch.Tensor, indices: torch.Tensor) -> torch.Tensor: Args: input (torch.Tensor): Input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]. indices (torch.Tensor): Indices with shape [num_orientations, H, W, num_rotations]. Returns: torch.Tensor: Refined features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W].
- def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, None]: Args: grad_output (torch.Tensor): The gradiant of output features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]. Returns: torch.Tensor: The gradiant of input features with shape [num_output_planes, num_input_planes, num_orientations, H, W].

Implement the Python class `ActiveRotatedFilterFunction` described below.

Class description:
Encoding the orientation information and generating orientation- sensitive features. The details are described in the paper `Align Deep Features for Oriented Object Detection <https://arxiv.org/abs/2008.09397>_`.

Method signatures and docstrings:
- def forward(ctx, input: torch.Tensor, indices: torch.Tensor) -> torch.Tensor: Args: input (torch.Tensor): Input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]. indices (torch.Tensor): Indices with shape [num_orientations, H, W, num_rotations]. Returns: torch.Tensor: Refined features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W].
- def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, None]: Args: grad_output (torch.Tensor): The gradiant of output features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]. Returns: torch.Tensor: The gradiant of input features with shape [num_output_planes, num_input_planes, num_orientations, H, W].

<|skeleton|>
class ActiveRotatedFilterFunction:
    """Encoding the orientation information and generating orientation- sensitive features. The details are described in the paper `Align Deep Features for Oriented Object Detection <https://arxiv.org/abs/2008.09397>_`."""

    def forward(ctx, input: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
        """Args: input (torch.Tensor): Input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]. indices (torch.Tensor): Indices with shape [num_orientations, H, W, num_rotations]. Returns: torch.Tensor: Refined features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]."""
        <|body_0|>

    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, None]:
        """Args: grad_output (torch.Tensor): The gradiant of output features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]. Returns: torch.Tensor: The gradiant of input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ctx.save_for_backward(input, indices)
        op, ip, o, h, w = input.size()
        o, h, w, r = indices.size()
        output = input.new_zeros((op * r, ip * o, h, w))
        ext_module.active_rotated_filter_forward(input, indices, output)
        return output
<|end_body_0|>

<|body_start_1|>
        input, indices = ctx.saved_tensors
        grad_in = torch.zeros_like(input)
        ext_module.active_rotated_filter_backward(grad_out, indices, grad_in)
        return (grad_in, None)
<|end_body_1|>

Solution:
class ActiveRotatedFilterFunction:
    """Encoding the orientation information and generating orientation- sensitive features. The details are described in the paper `Align Deep Features for Oriented Object Detection <https://arxiv.org/abs/2008.09397>_`."""

    def forward(ctx, input: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
        """Args: input (torch.Tensor): Input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]. indices (torch.Tensor): Indices with shape [num_orientations, H, W, num_rotations]. Returns: torch.Tensor: Refined features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]."""
        ctx.save_for_backward(input, indices)
        op, ip, o, h, w = input.size()
        o, h, w, r = indices.size()
        output = input.new_zeros((op * r, ip * o, h, w))
        ext_module.active_rotated_filter_forward(input, indices, output)
        return output

    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, None]:
        """Args: grad_output (torch.Tensor): The gradiant of output features with shape [num_output_planes * num_rotations, num_input_planes * num_orientations, H, W]. Returns: torch.Tensor: The gradiant of input features with shape [num_output_planes, num_input_planes, num_orientations, H, W]."""
        input, indices = ctx.saved_tensors
        grad_in = torch.zeros_like(input)
        ext_module.active_rotated_filter_backward(grad_out, indices, grad_in)
        return (grad_in, None)