



'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Dict       ):
    lowercase__		       =						int(__lowerCAmelCase       )
    assert noofclusters < len(__lowerCAmelCase       )

    # Find out the dimensionality
    lowercase__		       =						len(vectors[0]       )

    # Will help select random centroids from among the available vectors
    lowercase__		       =						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.

    lowercase__		       =						tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        lowercase__		       =						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
        lowercase__		       =						[
            tf.Variable(vectors[vector_indices[i]]       ) for i in range(__lowerCAmelCase       )
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        lowercase__		       =						tf.placeholder("float64"     ,[dim]       )
        lowercase__		       =						[]
        for centroid in centroids:
            cent_assigns.append(tf.assign(__lowerCAmelCase     ,__lowerCAmelCase       )       )

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

        ##Node for computing Euclidean distances
        # Placeholders for input
        lowercase__		       =						tf.placeholder("float"     ,[dim]       )
        lowercase__		       =						tf.placeholder("float"     ,[dim]       )
        lowercase__		       =						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
        lowercase__		       =						tf.placeholder("float"     ,[noofclusters]       )
        lowercase__		       =						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.
        lowercase__		       =						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.
        lowercase__		       =						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       )       ):
                lowercase__		       =						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.
                lowercase__		       =						[
                    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
                lowercase__		       =						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
                lowercase__		       =						[
                    vectors[i]
                    for i in range(len(__lowerCAmelCase       )       )
                    if sess.run(assignments[i]       ) == cluster_n
                ]
                # Compute new centroid location
                lowercase__		       =						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
        lowercase__		       =						sess.run(__lowerCAmelCase       )
        lowercase__		       =						sess.run(__lowerCAmelCase       )
        return centroids, assignments









'''simple docstring'''





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 				snake_case (tf.keras.layers.Layer   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None      )  ->							List[Any]:
           super().__init__()
           lowercase__		       =						pad_token_id
           lowercase__		       =						max_length
           lowercase__		       =						vocab
           lowercase__		       =						merges
           lowercase__		       =						BytePairTokenizer(UpperCAmelCase_     ,UpperCAmelCase_     ,sequence_length=UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[" ".join(UpperCAmelCase_      ) for m in tokenizer.bpe_ranks.keys()]
           lowercase__		       =						tokenizer.get_vocab()
           return cls(UpperCAmelCase_     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						GPTaTokenizer.from_pretrained(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           return cls.from_tokenizer(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_      )  ->							Optional[Any]:
           return cls(**UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple:
           lowercase__		       =						self.tf_tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tf.ones_like(UpperCAmelCase_      )

           if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__		       =						max_length if max_length is not None else self.max_length

               if max_length is not None:
                   lowercase__       ,       lowercase__		       =						pad_model_inputs(
                       UpperCAmelCase_     ,max_seq_length=UpperCAmelCase_     ,pad_value=self.pad_token_id      )

           return {"attention_mask": attention_mask, "input_ids": input_ids}




