





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)


__lowerCamelCase			:	Optional[Any]					    =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCamelCase			:	Union[str, Any]					    =	{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase			:	List[Any]					    =	{
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Optional[int]		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :int		       =  PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :Optional[int]		       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Union[str, Any]		       =  ['input_ids', 'attention_mask']
      _UpperCAmelCase      :Optional[int]		       =  BartTokenizer





      def __init__(					self			,				A_=None			,				A_=None			,				A_=None			,				A_="replace"			,				A_="<s>"			,				A_="</s>"			,				A_="</s>"			,				A_="<s>"			,				A_="<unk>"			,				A_="<pad>"			,				A_="<mask>"			,				A_=False			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(
           A_			,				A_			,				tokenizer_file=A_			,				errors=A_			,				bos_token=A_			,				eos_token=A_			,				sep_token=A_			,				cls_token=A_			,				unk_token=A_			,				pad_token=A_			,				mask_token=A_			,				add_prefix_space=A_			,				trim_offsets=A_			,				**A_			,				)

       UpperCamelCase      :   List[str]			      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
       if pre_tok_state.get("add_prefix_space"			,				A_  ) != add_prefix_space:
        UpperCamelCase      :   Optional[int]			      =  getattr(A_			,				pre_tok_state.pop("type"  )  )
        UpperCamelCase      :   List[str]			      =  add_prefix_space
        UpperCamelCase      :   Any			      =  pre_tok_class(**A_  )

       UpperCamelCase      :   List[str]			      =  add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase      :   str			      =  "post_processor"
       UpperCamelCase      :   List[str]			      =  getattr(self.backend_tokenizer			,				A_			,				A_  )
       if tokenizer_component_instance:
        UpperCamelCase      :   Union[str, Any]			      =  json.loads(tokenizer_component_instance.__getstate__()  )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
         UpperCamelCase      :   str			      =  tuple(state["sep"]  )
        if "cls" in state:
         UpperCamelCase      :   Any			      =  tuple(state["cls"]  )

        UpperCamelCase      :   Tuple			      =  False

        if state.get("add_prefix_space"			,				A_  ) != add_prefix_space:
         UpperCamelCase      :   str			      =  add_prefix_space
         UpperCamelCase      :   Any			      =  True

        if state.get("trim_offsets"			,				A_  ) != trim_offsets:
         UpperCamelCase      :   List[Any]			      =  trim_offsets
         UpperCamelCase      :   int			      =  True

        if changes_to_apply:
         UpperCamelCase      :   Any			      =  getattr(A_			,				state.pop("type"  )  )
         UpperCamelCase      :   List[str]			      =  component_class(**A_  )
         setattr(self.backend_tokenizer			,				A_			,				A_  )





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if self._mask_token is None:
        if self.verbose:
         logger.error("Using mask_token, but it is not set yet."  )
        return None
       return str(self._mask_token  )





      @mask_token.setter
      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else value
       UpperCamelCase      :   Dict			      =  value





      def 			__UpperCamelCase(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  kwargs.get("is_split_into_words"			,				A_  )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."  )

       return super()._batch_encode_plus(*A_			,				**A_  )





      def 			__UpperCamelCase(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  kwargs.get("is_split_into_words"			,				A_  )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."  )

       return super()._encode_plus(*A_			,				**A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self._tokenizer.model.save(A_			,				name=A_  )
       return tuple(A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_=None  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  [self.sep_token_id]
       UpperCamelCase      :   Dict			      =  [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







from random import shuffle

import tensorflow as tf
from numpy import array



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  int(_lowerCAmelCase	)
 assert noofclusters < len(_lowerCAmelCase	)

 # Find out the dimensionality
 UpperCamelCase      :   str			      =  len(vectors[0]	)

 # Will help select random centroids from among the available vectors
 UpperCamelCase      :   List[str]			      =  list(range(len(_lowerCAmelCase	)	)	)
 shuffle(_lowerCAmelCase	)

 # 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      :   Any			      =  tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  UpperCamelCase      :   Union[str, Any]			      =  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      :   Optional[int]			      =  [
      tf.Variable(vectors[vector_indices[i]]	) for i in range(_lowerCAmelCase	)
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float64"				,  [dim]	)
  UpperCamelCase      :   str			      =  []
  for centroid in centroids:
   cent_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

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

  ##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      :   List[str]			      =  tf.reduce_mean(_lowerCAmelCase				,  0	)

  ##Node for computing Euclidean distances
  # Placeholders for input
  UpperCamelCase      :   Optional[int]			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   Any			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   List[str]			      =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCAmelCase				,  _lowerCAmelCase	)				,  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      :   Tuple			      =  tf.placeholder("float"				,  [noofclusters]	)
  UpperCamelCase      :   str			      =  tf.argmin(_lowerCAmelCase				,  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      :   Optional[Any]			      =  tf.initialize_all_variables()

  # Initialize all variables
  sess.run(_lowerCAmelCase	)

  ##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      :   Any			      =  100
  for _ in range(_lowerCAmelCase	):
   ##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(_lowerCAmelCase	)	):
    UpperCamelCase      :   Dict			      =  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      :   Optional[Any]			      =  [
        sess.run(_lowerCAmelCase				,  feed_dict={va: vect, va: sess.run(_lowerCAmelCase	)}	)
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    UpperCamelCase      :   Tuple			      =  sess.run(
        _lowerCAmelCase				,  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(_lowerCAmelCase	):
    # Collect all the vectors assigned to this cluster
    UpperCamelCase      :   str			      =  [
        vectors[i]
        for i in range(len(_lowerCAmelCase	)	)
        if sess.run(assignments[i]	) == cluster_n
    ]
    # Compute new centroid location
    UpperCamelCase      :   Optional[int]			      =  sess.run(
        _lowerCAmelCase				,  feed_dict={mean_input: array(_lowerCAmelCase	)}	)
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n]				,  feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
  UpperCamelCase      :   Union[str, Any]			      =  sess.run(_lowerCAmelCase	)
  UpperCamelCase      :   Tuple			      =  sess.run(_lowerCAmelCase	)
  return centroids, assignments
