


"""simple docstring"""

from typing import List

import numpy as np


def lowerCAmelCase_   (					UpperCamelCase__						:			dict			):






 """simple docstring"""







 __lowercase						=						{key: len(UpperCamelCase__			) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__					, UpperCamelCase__			)}
 if len(set(lists_lengths.values()			)			) > 1:
  raise RuntimeError(
      (
          """Sharding is ambiguous for this dataset: """
          + """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
          + """\n""".join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()			)
          + """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
          + """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
      )			)
 __lowercase						=						max(lists_lengths.values()					, default=0			)
 return max(1					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[]
 for group_idx in range(UpperCamelCase__			):
  __lowercase						=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
  if num_shards_to_add == 0:
   break
  __lowercase						=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
  __lowercase						=						range(UpperCamelCase__					, start + num_shards_to_add			)
  shards_indices_per_group.append(UpperCamelCase__			)
 return shards_indices_per_group


def lowerCAmelCase_   (					UpperCamelCase__						:			dict					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						_number_of_shards_in_gen_kwargs(UpperCamelCase__			)
 if num_shards == 1:
  return [dict(UpperCamelCase__			)]
 else:
  __lowercase						=						_distribute_shards(num_shards=UpperCamelCase__					, max_num_jobs=UpperCamelCase__			)
  return [
      {
          key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
          if isinstance(UpperCamelCase__					, UpperCamelCase__			)
          else value
          for key, value in gen_kwargs.items()
      }
      for group_idx in range(len(UpperCamelCase__			)			)
  ]


def lowerCAmelCase_   (					UpperCamelCase__						:			List[dict]			):






 """simple docstring"""







 return {
     key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
     if isinstance(gen_kwargs_list[0][key]					, UpperCamelCase__			)
     else gen_kwargs_list[0][key]
     for key in gen_kwargs_list[0]
 }


def lowerCAmelCase_   (					UpperCamelCase__						:			np.random.Generator					, UpperCamelCase__						:			dict			):






 """simple docstring"""







 __lowercase						=						{len(UpperCamelCase__			) for value in gen_kwargs.values() if isinstance(UpperCamelCase__					, UpperCamelCase__			)}
 __lowercase						=						{}
 for size in list_sizes:
  __lowercase						=						list(range(UpperCamelCase__			)			)
  rng.shuffle(indices_per_size[size]			)
 # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
 __lowercase						=						dict(UpperCamelCase__			)
 for key, value in shuffled_kwargs.items():
  if isinstance(UpperCamelCase__					, UpperCamelCase__			):
   __lowercase						=						[value[i] for i in indices_per_size[len(UpperCamelCase__			)]]
 return shuffled_kwargs




"""simple docstring"""

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  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						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_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						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				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

       __lowercase						=						"""I was born in 92000, and this is falsé."""

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

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

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






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
       __lowercase						=						[
           """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_   ,							)
