



'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Tuple		    =  int(_a			)
    assert noofclusters < len(_a			)

    # Find out the dimensionality
    __UpperCamelCase	:	Dict		    =  len(vectors[0]			)

    # Will help select random centroids from among the available vectors
    __UpperCamelCase	:	List[Any]		    =  list(range(len(_a			)			)			)
    shuffle(_a			)

    # 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	:	int		    =  tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        __UpperCamelCase	:	Optional[int]		    =  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(_a			)
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        __UpperCamelCase	:	Dict		    =  tf.placeholder('float64'				,     [dim]			)
        __UpperCamelCase	:	Tuple		    =  []
        for centroid in centroids:
            cent_assigns.append(tf.assign(_a				,     _a			)			)

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

        ##Now lets construct the node that will compute the mean
        # The placeholder for the input
        __UpperCamelCase	:	List[Any]		    =  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	:	Union[str, Any]		    =  tf.reduce_mean(_a				,     0			)

        ##Node for computing Euclidean distances
        # Placeholders for input
        __UpperCamelCase	:	Any		    =  tf.placeholder('float'				,     [dim]			)
        __UpperCamelCase	:	Union[str, Any]		    =  tf.placeholder('float'				,     [dim]			)
        __UpperCamelCase	:	str		    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_a				,     _a			)				,     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	:	List[Any]		    =  tf.placeholder('float'				,     [noofclusters]			)
        __UpperCamelCase	:	Dict		    =  tf.argmin(_a				,     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	:	Any		    =  tf.initialize_all_variables()

        # Initialize all variables
        sess.run(_a			)

        ##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	:	Dict		    =  1_0_0
        for _ in range(_a			):
            ##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(_a			)			):
                __UpperCamelCase	:	str		    =  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[int]		    =  [
                    sess.run(_a				,     feed_dict={va: vect, va: sess.run(_a			)}			)
                    for centroid in centroids
                ]
                # Now use the cluster assignment node, with the distances
                # as the input
                __UpperCamelCase	:	Optional[int]		    =  sess.run(
                    _a				,     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(_a			):
                # Collect all the vectors assigned to this cluster
                __UpperCamelCase	:	Tuple		    =  [
                    vectors[i]
                    for i in range(len(_a			)			)
                    if sess.run(assignments[i]			) == cluster_n
                ]
                # Compute new centroid location
                __UpperCamelCase	:	Optional[int]		    =  sess.run(
                    _a				,     feed_dict={mean_input: array(_a			)}			)
                # Assign value to appropriate variable
                sess.run(
                    cent_assigns[cluster_n]				,     feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
        __UpperCamelCase	:	Optional[Any]		    =  sess.run(_a			)
        __UpperCamelCase	:	Optional[Any]		    =  sess.run(_a			)
        return centroids, assignments




'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
  import datasets






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

         __UpperCamelCase	:	str		    =  datasets.load_dataset('cnn_dailymail'     ,			'3.0.0'     ,			split='train[:1%]'    )
         __UpperCamelCase	:	Tuple		    =  datasets.load_dataset('cnn_dailymail'     ,			'3.0.0'     ,			split='validation[:1%]'    )

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

         def _map_to_encoder_decoder_inputs(_lowerCamelCase    ):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(batch['article']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=5_1_2    )
             __UpperCamelCase	:	Optional[int]		    =  tokenizer(batch['highlights']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=1_2_8    )
             __UpperCamelCase	:	Any		    =  inputs.input_ids
             __UpperCamelCase	:	Optional[Any]		    =  inputs.attention_mask

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  outputs.attention_mask

             assert all(len(_lowerCamelCase    ) == 5_1_2 for x in inputs.input_ids    )
             assert all(len(_lowerCamelCase    ) == 1_2_8 for x in outputs.input_ids    )

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

             __UpperCamelCase	:	Dict		    =  sum([int(pred_str[i] == label_str[i]    ) for i in range(len(_lowerCamelCase    )    )]    ) / len(_lowerCamelCase    )

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	str		    =  train_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			remove_columns=['article', 'highlights']     ,			)
         train_dataset.set_format(
             type='torch'     ,			columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,			)

         # same for validation dataset
         __UpperCamelCase	:	Any		    =  val_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			remove_columns=['article', 'highlights']     ,			)
         val_dataset.set_format(
             type='torch'     ,			columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,			)

         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()

         __UpperCamelCase	:	List[Any]		    =  SeqaSeqTrainingArguments(
             output_dir=_lowerCamelCase     ,			per_device_train_batch_size=_lowerCamelCase     ,			per_device_eval_batch_size=_lowerCamelCase     ,			predict_with_generate=_lowerCamelCase     ,			evaluation_strategy='steps'     ,			do_train=_lowerCamelCase     ,			do_eval=_lowerCamelCase     ,			warmup_steps=0     ,			eval_steps=2     ,			logging_steps=2     ,			)

         # instantiate trainer
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()



