




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 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      BarthezTokenizer
    UpperCAmelCase__							  =      BarthezTokenizerFast
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      True


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Dict:





           '''simple docstring'''

           super().setUp()

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__       =    '''<pad>'''
           A__       =    1

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    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(UpperCAmelCase__)					, 101_122)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size					, 101_122)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->str:





           '''simple docstring'''

           A__       =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           A__       =    [0, 57, 3_018, 70_307, 91, 2]

           A__       =    self.tokenizer(
               UpperCAmelCase__					, max_length=len(UpperCAmelCase__)					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, return_tensors='''pt''')
           self.assertIsInstance(UpperCAmelCase__					, UpperCAmelCase__)

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[Any]:





           '''simple docstring'''

           if not self.test_rust_tokenizer:
                  return

           A__       =    self.get_tokenizer()
           A__       =    self.get_rust_tokenizer()

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

           A__       =    tokenizer.tokenize(UpperCAmelCase__)
           A__       =    rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    self.get_rust_tokenizer()
           A__       =    tokenizer.encode(UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    {'''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.
           A__       =    [
               '''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=UpperCAmelCase__					, model_name='''moussaKam/mbarthez'''					, revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					, sequences=UpperCAmelCase__					, )






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					np.inf

  def set_batch_size(SCREAMING_SNAKE_CASE       ) -> None:
    nonlocal batch_size
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				Tuple		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				str		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and feature.dtype == "binary":
      A_      :				Union[str, Any]		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS       )

  _visit(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  return None if batch_size is np.inf else batch_size






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->str:




         '''simple docstring'''





         super().__init__(
             _SCREAMING_SNAKE_CASE				,       split=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       cache_dir=_SCREAMING_SNAKE_CASE				,       keep_in_memory=_SCREAMING_SNAKE_CASE				,       streaming=_SCREAMING_SNAKE_CASE				,       num_proc=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
         A_      :				str		     =					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
         A_      :				Optional[int]		     =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
         A_      :				Union[str, Any]		     =					Parquet(
             cache_dir=_SCREAMING_SNAKE_CASE				,       data_files=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       hash=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )







       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         if self.streaming:
           A_      :				List[str]		     =					self.builder.as_streaming_dataset(split=self.split		)
         # Build regular (map-style) dataset
         else:
           A_      :				List[str]		     =					None
           A_      :				List[str]		     =					None
           A_      :				List[Any]		     =					None
           A_      :				Dict		     =					None

           self.builder.download_and_prepare(
               download_config=_SCREAMING_SNAKE_CASE				,       download_mode=_SCREAMING_SNAKE_CASE				,       verification_mode=_SCREAMING_SNAKE_CASE				,       base_path=_SCREAMING_SNAKE_CASE				,       num_proc=self.num_proc				,       )
           A_      :				Union[str, Any]		     =					self.builder.as_dataset(
               split=self.split				,       verification_mode=_SCREAMING_SNAKE_CASE				,       in_memory=self.keep_in_memory		)
         return dataset






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					dataset
         A_      :				Union[str, Any]		     =					path_or_buf
         A_      :				Any		     =					batch_size or get_writer_batch_size(dataset.features		)
         A_      :				Optional[int]		     =					parquet_writer_kwargs



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf				,       (str, bytes, os.PathLike)		):
           with open(self.path_or_buf				,       '''wb+'''		) as buffer:
             A_      :				str		     =					self._write(file_obj=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         else:
           A_      :				Tuple		     =					self._write(file_obj=self.path_or_buf				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         return written







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         A_      :				List[Any]		     =					0
         A_      :				int		     =					parquet_writer_kwargs.pop('''path_or_buf'''				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					self.dataset.features.arrow_schema

         A_      :				List[str]		     =					pq.ParquetWriter(_SCREAMING_SNAKE_CASE				,       schema=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         for offset in logging.tqdm(
             range(0				,       len(self.dataset		)				,       _SCREAMING_SNAKE_CASE		)				,       unit='''ba'''				,       disable=not logging.is_progress_bar_enabled()				,       desc='''Creating parquet from Arrow format'''				,       ):
           A_      :				List[Any]		     =					query_table(
               table=self.dataset._data				,       key=slice(_SCREAMING_SNAKE_CASE				,       offset + batch_size		)				,       indices=self.dataset._indices if self.dataset._indices is not None else None				,       )
           writer.write_table(_SCREAMING_SNAKE_CASE		)
           written += batch.nbytes
         writer.close()
         return written




