




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __a          =			None


__a          =			logging.get_logger(__name__)


__a          =			'▁'

__a          =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a          =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__a          =			{
    'google/pegasus-xsum': 512,
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				PegasusTokenizer
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<mask_2>"       ,_SCREAMING_SNAKE_CASE="<mask_1>"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=103       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      offset

         if additional_special_tokens is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   raise TypeError(
                       f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE  )}, but is'''
                       f''' {type(_SCREAMING_SNAKE_CASE  )}'''  )

              UpperCAmelCase_      :							str       =      (
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE  )       ,self.offset - 1  )
              ]

              if len(set(_SCREAMING_SNAKE_CASE  )  ) != len(_SCREAMING_SNAKE_CASE  ):
                   raise ValueError(
                       '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                       f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
              UpperCAmelCase_      :							int       =      additional_special_tokens_extended
         else:
              UpperCAmelCase_      :							Any       =      [mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [f'''<unk_{i}>''' for i in range(2       ,self.offset  )]

         super().__init__(
             _SCREAMING_SNAKE_CASE       ,tokenizer_file=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,mask_token_sent=_SCREAMING_SNAKE_CASE       ,offset=_SCREAMING_SNAKE_CASE       ,additional_special_tokens=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							str       =      vocab_file
         UpperCAmelCase_      :							Dict       =      False if not self.vocab_file else True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Dict       =      set(self.all_special_ids  )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
              raise ValueError(
                  '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
                  f''' {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}'''  )

         return [1 if x in all_special_ids else 0 for x in seq]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:
         if already_has_special_tokens:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  )
         elif token_ids_a is None:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  ) + [1]
         else:
              return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''  )

         if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Optional[int]       =      os.path.join(
             _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(_SCREAMING_SNAKE_CASE  ):
              copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE  )

         return (out_vocab_file,)





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