



from random import shuffle

import tensorflow as tf
from numpy import array






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int(__lowerCamelCase     )
 assert noofclusters < len(__lowerCamelCase     )

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

 # Will help select random centroids from among the available vectors
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	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.

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

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

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  __SCREAMING_SNAKE_CASE       :    Dict       =	[tf.Variable(0     ) for i in range(len(__lowerCamelCase     )     )]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.placeholder("int32"     )
  __SCREAMING_SNAKE_CASE       :    str       =	[]
  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
  __SCREAMING_SNAKE_CASE       :    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
  __SCREAMING_SNAKE_CASE       :    Any       =	tf.reduce_mean(__lowerCamelCase		,	0     )

  ##Node for computing Euclidean distances
  # Placeholders for input
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	tf.placeholder("float"		,	[dim]     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.placeholder("float"		,	[dim]     )
  __SCREAMING_SNAKE_CASE       :    Any       =	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
  __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.placeholder("float"		,	[noofclusters]     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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.
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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.
  __SCREAMING_SNAKE_CASE       :    Optional[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     )     ):
    __SCREAMING_SNAKE_CASE       :    List[Any]       =	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.
    __SCREAMING_SNAKE_CASE       :    Tuple       =	[
        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
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
    __SCREAMING_SNAKE_CASE       :    Any       =	[
        vectors[i]
        for i in range(len(__lowerCamelCase     )     )
        if sess.run(assignments[i]     ) == cluster_n
    ]
    # Compute new centroid location
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	sess.run(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Dict       =	sess.run(__lowerCamelCase     )
  return centroids, assignments











# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""philschmid/bart-large-cnn-samsum"""
     lowerCAmelCase					=			(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     lowerCAmelCase					=			"""summarizer"""
     lowerCAmelCase					=			AutoTokenizer
     lowerCAmelCase					=			AutoModelForSeqaSeqLM

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






