




from random import shuffle

import tensorflow as tf
from numpy import array







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      int(_lowercase				)
     assert noofclusters < len(_lowercase				)

     # Find out the dimensionality
     UpperCAmelCase_      :							Optional[int]       =      len(vectors[0]				)

     # Will help select random centroids from among the available vectors
     UpperCAmelCase_      :							Optional[int]       =      list(range(len(_lowercase				)				)				)
     shuffle(_lowercase				)

     # GRAPH OF COMPUTATION
     # We initialize a new graph and set it as the default during each run
     # of this algorithm. This ensures that as this function is called
     # multiple times, the default graph doesn't keep getting crowded with
     # unused ops and Variables from previous function calls.

     UpperCAmelCase_      :							List[str]       =      tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

          UpperCAmelCase_      :							Tuple       =      tf.Session()

          ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

          ##First lets ensure we have a Variable vector for each centroid,
          ##initialized to one of the vectors from the available data points
          UpperCAmelCase_      :							str       =      [
              tf.Variable(vectors[vector_indices[i]]				) for i in range(_lowercase				)
          ]
          ##These nodes will assign the centroid Variables the appropriate
          ##values
          UpperCAmelCase_      :							List[Any]       =      tf.placeholder('''float64'''			,						[dim]				)
          UpperCAmelCase_      :							List[Any]       =      []
          for centroid in centroids:
               cent_assigns.append(tf.assign(_lowercase			,						_lowercase				)				)

          ##Variables for cluster assignments of individual vectors(initialized
          ##to 0 at first)
          UpperCAmelCase_      :							Optional[int]       =      [tf.Variable(0				) for i in range(len(_lowercase				)				)]
          ##These nodes will assign an assignment Variable the appropriate
          ##value
          UpperCAmelCase_      :							List[Any]       =      tf.placeholder('''int32'''				)
          UpperCAmelCase_      :							Union[str, Any]       =      []
          for assignment in assignments:
               cluster_assigns.append(tf.assign(_lowercase			,						_lowercase				)				)

          ##Now lets construct the node that will compute the mean
          # The placeholder for the input
          UpperCAmelCase_      :							Tuple       =      tf.placeholder('''float'''			,						[None, dim]				)
          # The Node/op takes the input and computes a mean along the 0th
          # dimension, i.e. the list of input vectors
          UpperCAmelCase_      :							str       =      tf.reduce_mean(_lowercase			,						0				)

          ##Node for computing Euclidean distances
          # Placeholders for input
          UpperCAmelCase_      :							List[Any]       =      tf.placeholder('''float'''			,						[dim]				)
          UpperCAmelCase_      :							Tuple       =      tf.placeholder('''float'''			,						[dim]				)
          UpperCAmelCase_      :							Optional[Any]       =      tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowercase			,						_lowercase				)			,						2				)				)				)

          ##This node will figure out which cluster to assign a vector to,
          ##based on Euclidean distances of the vector from the centroids.
          # Placeholder for input
          UpperCAmelCase_      :							int       =      tf.placeholder('''float'''			,						[noofclusters]				)
          UpperCAmelCase_      :							Any       =      tf.argmin(_lowercase			,						0				)

          ##INITIALIZING STATE VARIABLES

          ##This will help initialization of all Variables defined with respect
          ##to the graph. The Variable-initializer should be defined after
          ##all the Variables have been constructed, so that each of them
          ##will be included in the initialization.
          UpperCAmelCase_      :							List[Any]       =      tf.initialize_all_variables()

          # Initialize all variables
          sess.run(_lowercase				)

          ##CLUSTERING ITERATIONS

          # Now perform the Expectation-Maximization steps of K-Means clustering
          # iterations. To keep things simple, we will only do a set number of
          # iterations, instead of using a Stopping Criterion.
          UpperCAmelCase_      :							Tuple       =      100
          for _ in range(_lowercase				):
               ##EXPECTATION STEP
               ##Based on the centroid locations till last iteration, compute
               ##the _expected_ centroid assignments.
               # Iterate over each vector
               for vector_n in range(len(_lowercase				)				):
                    UpperCAmelCase_      :							Optional[int]       =      vectors[vector_n]
                    # Compute Euclidean distance between this vector and each
                    # centroid. Remember that this list cannot be named
                    #'centroid_distances', since that is the input to the
                    # cluster assignment node.
                    UpperCAmelCase_      :							Tuple       =      [
                        sess.run(_lowercase			,						feed_dict={va: vect, va: sess.run(_lowercase				)}				)
                        for centroid in centroids
                    ]
                    # Now use the cluster assignment node, with the distances
                    # as the input
                    UpperCAmelCase_      :							Dict       =      sess.run(
                        _lowercase			,						feed_dict={centroid_distances: distances}				)
                    # Now assign the value to the appropriate state variable
                    sess.run(
                        cluster_assigns[vector_n]			,						feed_dict={assignment_value: assignment}				)

               ##MAXIMIZATION STEP
               # Based on the expected state computed from the Expectation Step,
               # compute the locations of the centroids so as to maximize the
               # overall objective of minimizing within-cluster Sum-of-Squares
               for cluster_n in range(_lowercase				):
                    # Collect all the vectors assigned to this cluster
                    UpperCAmelCase_      :							List[str]       =      [
                        vectors[i]
                        for i in range(len(_lowercase				)				)
                        if sess.run(assignments[i]				) == cluster_n
                    ]
                    # Compute new centroid location
                    UpperCAmelCase_      :							Any       =      sess.run(
                        _lowercase			,						feed_dict={mean_input: array(_lowercase				)}				)
                    # Assign value to appropriate variable
                    sess.run(
                        cent_assigns[cluster_n]			,						feed_dict={centroid_value: new_location}				)

        # Return centroids and assignments
          UpperCAmelCase_      :							Optional[int]       =      sess.run(_lowercase				)
          UpperCAmelCase_      :							int       =      sess.run(_lowercase				)
          return centroids, assignments





from ...processing_utils import ProcessorMixin

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''SpeechT5FeatureExtractor'''
    lowerCAmelCase        =				'''SpeechT5Tokenizer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def __call__(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''audio'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''text'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''text_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''audio_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''sampling_rate'''       ,_SCREAMING_SNAKE_CASE  )

         if audio is not None and text is not None:
              raise ValueError(
                  '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''  )
         if audio_target is not None and text_target is not None:
              raise ValueError(
                  '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''  )
         if audio is None and audio_target is None and text is None and text_target is None:
              raise ValueError(
                  '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''  )

         if audio is not None:
              UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif text is not None:
              UpperCAmelCase_      :							Dict       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if audio_target is not None:
              UpperCAmelCase_      :							List[Any]       =      self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      targets['''input_values''']
         elif text_target is not None:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      targets['''input_ids''']
         else:
              UpperCAmelCase_      :							Tuple       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      labels

              UpperCAmelCase_      :							Optional[int]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      kwargs.pop('''input_values'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      kwargs.pop('''input_ids'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''labels'''       ,_SCREAMING_SNAKE_CASE  )

         if input_values is not None and input_ids is not None:
              raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''  )
         if input_values is None and input_ids is None and labels is None:
              raise ValueError(
                  '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''  )

         if input_values is not None:
              UpperCAmelCase_      :							Tuple       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif input_ids is not None:
              UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if labels is not None:
              if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and "input_ids" in labels[0]):
                   UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      targets['''input_ids''']
              else:
                   UpperCAmelCase_      :							int       =      self.feature_extractor.feature_size
                   UpperCAmelCase_      :							List[str]       =      self.feature_extractor.num_mel_bins
                   UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      feature_size_hack
                   UpperCAmelCase_      :							List[Any]       =      targets['''input_values''']
         else:
              UpperCAmelCase_      :							Optional[int]       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Optional[int]       =      labels

              UpperCAmelCase_      :							Union[str, Any]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )