


'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase :List[str]             =     get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCAmelCase :Dict             =     get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCAmelCase :List[Any]             =     'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase			(		_lowercase						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     Tuple        =							CamembertTokenizer
		A_      :     Tuple        =							CamembertTokenizerFast
		A_      :     Dict        =							True
		A_      :     List[Any]        =							True







		def   __lowerCAmelCase						(   self						:		int     )    ->					List[Any]:
			super().setUp()

			# We have a SentencePiece fixture for testing
			__magic_name__			:       Optional[Any]	      =	CamembertTokenizer(_A     )
			tokenizer.save_pretrained(self.tmpdirname     )







		def   __lowerCAmelCase						(   self						:		List[str]     )    ->					Union[str, Any]:
			__magic_name__			:       Optional[int]	      =	'<pad>'
			__magic_name__			:       Optional[Any]	      =	1

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







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					int:
			__magic_name__			:       Tuple	      =	list(self.get_tokenizer().get_vocab().keys()     )

			self.assertEqual(vocab_keys[0]    ,	'<s>NOTUSED'     )
			self.assertEqual(vocab_keys[1]    ,	'<pad>'     )
			self.assertEqual(vocab_keys[-1]    ,	'<mask>'     )
			self.assertEqual(len(_A     )    ,	1004     )







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Any:
			self.assertEqual(self.get_tokenizer().vocab_size    ,	1005     )







		def   __lowerCAmelCase						(   self						:		str     )    ->					List[Any]:
			__magic_name__			:       Any	      =	CamembertTokenizer(_A     )
			tokenizer.save_pretrained(self.tmpdirname     )
			__magic_name__			:       Union[str, Any]	      =	CamembertTokenizerFast.from_pretrained(self.tmpdirname     )

			__magic_name__			:       Tuple	      =	'I was born in 92000, and this is falsé.'

			__magic_name__			:       str	      =	tokenizer.encode(_A     )
			__magic_name__			:       Any	      =	rust_tokenizer.encode(_A     )
			self.assertListEqual(_A    ,	_A     )

			__magic_name__			:       int	      =	tokenizer.encode(_A    ,	add_special_tokens=_A     )
			__magic_name__			:       Optional[int]	      =	rust_tokenizer.encode(_A    ,	add_special_tokens=_A     )
			self.assertListEqual(_A    ,	_A     )

			# <unk> tokens are not the same for `rust` than for `slow`.
			# Because spm gives back raw token instead of `unk` in EncodeAsPieces
			# tokens = tokenizer.tokenize(sequence)
			__magic_name__			:       Union[str, Any]	      =	tokenizer.convert_ids_to_tokens(_A     )
			__magic_name__			:       List[str]	      =	rust_tokenizer.tokenize(_A     )
			self.assertListEqual(_A    ,	_A     )







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					Optional[Any]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__			:       Any	      =	self.get_tokenizer()
			__magic_name__			:       int	      =	self.get_rust_tokenizer()

			__magic_name__			:       int	      =	'I was born in 92000, and this is falsé.'

			__magic_name__			:       int	      =	tokenizer.tokenize(_A     )
			__magic_name__			:       str	      =	rust_tokenizer.tokenize(_A     )
			self.assertListEqual(_A    ,	_A     )

			__magic_name__			:       Optional[Any]	      =	tokenizer.encode(_A    ,	add_special_tokens=_A     )
			__magic_name__			:       int	      =	rust_tokenizer.encode(_A    ,	add_special_tokens=_A     )
			self.assertListEqual(_A    ,	_A     )

			__magic_name__			:       Dict	      =	self.get_rust_tokenizer()
			__magic_name__			:       List[Any]	      =	tokenizer.encode(_A     )
			__magic_name__			:       Any	      =	rust_tokenizer.encode(_A     )
			self.assertListEqual(_A    ,	_A     )







		@slow
		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					Dict:
			# fmt: off
			__magic_name__			:       int	      =	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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]]}  # noqa: E501
			# fmt: on

			# camembert is a french model. So we also use french texts.
			__magic_name__			:       Optional[int]	      =	[
			    '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='camembert-base'    ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,	sequences=_A    ,	)




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





