
import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       BarthezTokenizer
		__lowercase : Dict			  =       BarthezTokenizerFast
		__lowercase : Optional[Any]			  =       True
		__lowercase : Dict			  =       True



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				super().setUp()

				UpperCamelCase						=  BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
				tokenizer.save_pretrained(self.tmpdirname )
				tokenizer.save_pretrained(self.tmpdirname       ,   legacy_format=A_ )
				UpperCamelCase						=  tokenizer



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  '<pad>'
				UpperCamelCase						=  1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ )       ,   A_ )
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ )       ,   A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  list(self.get_tokenizer().get_vocab().keys() )

				self.assertEqual(vocab_keys[0]       ,   '<s>' )
				self.assertEqual(vocab_keys[1]       ,   '<pad>' )
				self.assertEqual(vocab_keys[-1]       ,   '<mask>' )
				self.assertEqual(len(A_ )       ,   101_122 )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size       ,   101_122 )



		@require_torch
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
				UpperCamelCase						=  [0, 57, 3_018, 70_307, 91, 2]

				UpperCamelCase						=  self.tokenizer(
				    A_       ,   max_length=len(A_ )       ,   padding=A_       ,   truncation=A_       ,   return_tensors='pt' )
				self.assertIsInstance(A_       ,   A_ )

				self.assertEqual((2, 6)       ,   batch.input_ids.shape )
				self.assertEqual((2, 6)       ,   batch.attention_mask.shape )
				UpperCamelCase						=  batch.input_ids.tolist()[0]
				self.assertListEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				if not self.test_rust_tokenizer:
						return

				UpperCamelCase						=  self.get_tokenizer()
				UpperCamelCase						=  self.get_rust_tokenizer()

				UpperCamelCase						=  'I was born in 92000, and this is falsé.'

				UpperCamelCase						=  tokenizer.tokenize(A_ )
				UpperCamelCase						=  rust_tokenizer.tokenize(A_ )
				self.assertListEqual(A_       ,   A_ )

				UpperCamelCase						=  tokenizer.encode(A_       ,   add_special_tokens=A_ )
				UpperCamelCase						=  rust_tokenizer.encode(A_       ,   add_special_tokens=A_ )
				self.assertListEqual(A_       ,   A_ )

				UpperCamelCase						=  self.get_rust_tokenizer()
				UpperCamelCase						=  tokenizer.encode(A_ )
				UpperCamelCase						=  rust_tokenizer.encode(A_ )
				self.assertListEqual(A_       ,   A_ )



		@slow
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase						=  {'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
				# fmt: on

				# moussaKam/mbarthez is a french model. So we also use french texts.
				UpperCamelCase						=  [
				    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
				    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
				    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
				    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
				    'telles que la traduction et la synthèse de texte.',
				]

				self.tokenizer_integration_test_util(
				    expected_encoding=A_       ,   model_name='moussaKam/mbarthez'       ,   revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,   sequences=A_       ,   )


from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		assert noofclusters < len(lowercase				)

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

		# Will help select random centroids from among the available vectors
		UpperCamelCase						=  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						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

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