
import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[Any]			  =       RobertaTokenizer
		__lowercase : Union[str, Any]			  =       RobertaTokenizerFast
		__lowercase : Tuple			  =       True
		__lowercase : Any			  =       {"cls_token": "<s>"}



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

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase						=  [
				    'l',
				    'o',
				    'w',
				    'e',
				    'r',
				    's',
				    't',
				    'i',
				    'd',
				    'n',
				    '\u0120',
				    '\u0120l',
				    '\u0120n',
				    '\u0120lo',
				    '\u0120low',
				    'er',
				    '\u0120lowest',
				    '\u0120newer',
				    '\u0120wider',
				    '<unk>',
				]
				UpperCamelCase						=  dict(zip(A_       ,   range(len(A_ ) ) ) )
				UpperCamelCase						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
				UpperCamelCase						=  {'unk_token': '<unk>'}

				UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file'] )
				UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['merges_file'] )
				with open(self.vocab_file       ,   'w'       ,   encoding='utf-8' ) as fp:
						fp.write(json.dumps(A_ ) + '\n' )
				with open(self.merges_file       ,   'w'       ,   encoding='utf-8' ) as fp:
						fp.write('\n'.join(A_ ) )



		def __UpperCamelCase				(				self       ,   **A_ )						->      List[str]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map )
				return self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **A_ )



		def __UpperCamelCase				(				self       ,   **A_ )						->      Optional[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map )
				return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,   **A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  'lower newer'
				UpperCamelCase						=  'lower newer'
				return input_text, output_text



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer_class(self.vocab_file       ,   self.merges_file       ,   **self.special_tokens_map )
				UpperCamelCase						=  'lower newer'
				UpperCamelCase						=  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
				UpperCamelCase						=  tokenizer.tokenize(A_ )  # , add_prefix_space=True)
				self.assertListEqual(A_       ,   A_ )

				UpperCamelCase						=  tokens + [tokenizer.unk_token]
				UpperCamelCase						=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )       ,   A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.get_tokenizer()

				self.assertListEqual(tokenizer.encode('Hello world!'       ,   add_special_tokens=A_ )       ,   [0, 31_414, 232, 328, 2] )
				self.assertListEqual(
				    tokenizer.encode('Hello world! cécé herlolip 418'       ,   add_special_tokens=A_ )       ,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]       ,   )



		@slow
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer_class.from_pretrained('roberta-base' )

				UpperCamelCase						=  tokenizer.encode('sequence builders'       ,   add_special_tokens=A_ )
				UpperCamelCase						=  tokenizer.encode('multi-sequence build'       ,   add_special_tokens=A_ )

				UpperCamelCase						=  tokenizer.encode(
				    'sequence builders'       ,   add_special_tokens=A_       ,   add_prefix_space=A_ )
				UpperCamelCase						=  tokenizer.encode(
				    'sequence builders'       ,   'multi-sequence build'       ,   add_special_tokens=A_       ,   add_prefix_space=A_ )

				UpperCamelCase						=  tokenizer.build_inputs_with_special_tokens(A_ )
				UpperCamelCase						=  tokenizer.build_inputs_with_special_tokens(A_       ,   A_ )

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.get_tokenizer()

				UpperCamelCase						=  'Encode this sequence.'
				UpperCamelCase						=  tokenizer.byte_encoder[' '.encode('utf-8' )[0]]

				# Testing encoder arguments
				UpperCamelCase						=  tokenizer.encode(A_       ,   add_special_tokens=A_       ,   add_prefix_space=A_ )
				UpperCamelCase						=  tokenizer.convert_ids_to_tokens(encoded[0] )[0]
				self.assertNotEqual(A_       ,   A_ )

				UpperCamelCase						=  tokenizer.encode(A_       ,   add_special_tokens=A_       ,   add_prefix_space=A_ )
				UpperCamelCase						=  tokenizer.convert_ids_to_tokens(encoded[0] )[0]
				self.assertEqual(A_       ,   A_ )

				tokenizer.add_special_tokens({'bos_token': '<s>'} )
				UpperCamelCase						=  tokenizer.encode(A_       ,   add_special_tokens=A_ )
				UpperCamelCase						=  tokenizer.convert_ids_to_tokens(encoded[1] )[0]
				self.assertNotEqual(A_       ,   A_ )

				# Testing spaces after special tokens
				UpperCamelCase						=  '<mask>'
				tokenizer.add_special_tokens(
				    {'mask_token': AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ )} )  # mask token has a left space
				UpperCamelCase						=  tokenizer.convert_tokens_to_ids(A_ )

				UpperCamelCase						=  'Encode <mask> sequence'
				UpperCamelCase						=  'Encode <mask>sequence'

				UpperCamelCase						=  tokenizer.encode(A_ )
				UpperCamelCase						=  encoded.index(A_ )
				UpperCamelCase						=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
				self.assertEqual(A_       ,   A_ )

				UpperCamelCase						=  tokenizer.encode(A_ )
				UpperCamelCase						=  encoded.index(A_ )
				UpperCamelCase						=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
				self.assertNotEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(A_       ,   **A_ )
								UpperCamelCase						=  self.tokenizer_class.from_pretrained(A_       ,   **A_ )
								UpperCamelCase						=  'A, <mask> AllenNLP sentence.'
								UpperCamelCase						=  tokenizer_r.encode_plus(A_       ,   add_special_tokens=A_       ,   return_token_type_ids=A_ )
								UpperCamelCase						=  tokenizer_p.encode_plus(A_       ,   add_special_tokens=A_       ,   return_token_type_ids=A_ )

								# token_type_ids should put 0 everywhere
								self.assertEqual(sum(tokens_r['token_type_ids'] )       ,   sum(tokens_p['token_type_ids'] ) )

								# attention_mask should put 1 everywhere, so sum over length should be 1
								self.assertEqual(
								    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )       ,   sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )       ,   )

								UpperCamelCase						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
								UpperCamelCase						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

								# Rust correctly handles the space before the mask while python doesnt
								self.assertSequenceEqual(tokens_p['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
								self.assertSequenceEqual(tokens_r['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

								self.assertSequenceEqual(
								    A_       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
								self.assertSequenceEqual(
								    A_       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for trim_offsets, add_prefix_space in itertools.product([True, False]       ,   repeat=2 ):
						UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
						    self.tmpdirname       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )

						UpperCamelCase						=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
						UpperCamelCase						=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

						self.assertEqual(pre_tokenizer_state['add_prefix_space']       ,   A_ )

						self.assertEqual(post_processor_state['add_prefix_space']       ,   A_ )
						self.assertEqual(post_processor_state['trim_offsets']       ,   A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
				# `trim_offsets`
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
								UpperCamelCase						=  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
								UpperCamelCase						=  F'''{text_of_1_token} {text_of_1_token}'''

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (len(A_ ) + 1, len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (len(A_ ) + 1, len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (len(A_ ), len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (len(A_ ), len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  F''' {text}'''

								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
								# )
								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
								# self.assertEqual(
								#     encoding.offset_mapping[1],
								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
								# )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (1, 1 + len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (1 + len(A_ ) + 1, 1 + len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, 1 + len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (1 + len(A_ ), 1 + len(A_ ) + 1 + len(A_ ))       ,   )

								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(
								    A_       ,   use_fast=A_       ,   add_prefix_space=A_       ,   trim_offsets=A_ )
								UpperCamelCase						=  tokenizer_r(A_       ,   return_offsets_mapping=A_       ,   add_special_tokens=A_ )
								self.assertEqual(encoding.offset_mapping[0]       ,   (0, 1 + len(A_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]       ,   (1 + len(A_ ), 1 + len(A_ ) + 1 + len(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
