


'''simple docstring'''



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class   UpperCAmelCase_    (_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_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Dict:
         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_     ,	)
         __lowerCamelCase  :      Any														=						field
         __lowerCamelCase  :      Tuple														=						path_or_paths if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) else {self.split: path_or_paths}
         __lowerCamelCase  :      int														=						Json(
             cache_dir=SCREAMING_SNAKE_CASE_     ,	data_files=SCREAMING_SNAKE_CASE_     ,	features=SCREAMING_SNAKE_CASE_     ,	field=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	List[str]:
         # Build iterable dataset
         if self.streaming:
              __lowerCamelCase  :      str														=						self.builder.as_streaming_dataset(split=self.split      )
         # Build regular (map-style) dataset
         else:
              __lowerCamelCase  :      List[Any]														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
              __lowerCamelCase  :      Union[str, Any]														=						None
              __lowerCamelCase  :      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     ,	)
              __lowerCamelCase  :      List[str]														=						self.builder.as_dataset(
                  split=self.split     ,	verification_mode=SCREAMING_SNAKE_CASE_     ,	in_memory=self.keep_in_memory      )
         return dataset







class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         if num_proc is not None and num_proc <= 0:
              raise ValueError(f'num_proc {num_proc} must be an integer > 0.'      )

         __lowerCamelCase  :      Tuple														=						dataset
         __lowerCamelCase  :      Dict														=						path_or_buf
         __lowerCamelCase  :      int														=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         __lowerCamelCase  :      str														=						num_proc
         __lowerCamelCase  :      Union[str, Any]														=						'utf-8'
         __lowerCamelCase  :      str														=						to_json_kwargs







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[Any]														=						self.to_json_kwargs.pop('path_or_buf'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						self.to_json_kwargs.pop('orient'     ,	'records'      )
         __lowerCamelCase  :      List[Any]														=						self.to_json_kwargs.pop('lines'     ,	True if orient == 'records' else False      )
         __lowerCamelCase  :      List[str]														=						self.to_json_kwargs.pop('index'     ,	False if orient in ['split', 'table'] else True      )
         __lowerCamelCase  :      Tuple														=						self.to_json_kwargs.pop('compression'     ,	SCREAMING_SNAKE_CASE_      )

         if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f'`datasets` currently does not support {compression} compression'      )

         if isinstance(self.path_or_buf     ,	(str, bytes, os.PathLike)      ):
              with fsspec.open(self.path_or_buf     ,	'wb'     ,	compression=SCREAMING_SNAKE_CASE_      ) as buffer:
                   __lowerCamelCase  :      List[Any]														=						self._write(file_obj=SCREAMING_SNAKE_CASE_     ,	orient=SCREAMING_SNAKE_CASE_     ,	lines=SCREAMING_SNAKE_CASE_     ,	index=SCREAMING_SNAKE_CASE_     ,	**self.to_json_kwargs      )
         else:
              if compression:
                   raise NotImplementedError(
                       f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                       ' was passed. Please provide a local path instead.'      )
              __lowerCamelCase  :      Dict														=						self._write(
                  file_obj=self.path_or_buf     ,	orient=SCREAMING_SNAKE_CASE_     ,	lines=SCREAMING_SNAKE_CASE_     ,	index=SCREAMING_SNAKE_CASE_     ,	**self.to_json_kwargs      )
         return written







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      str														=						args

         __lowerCamelCase  :      List[str]														=						query_table(
             table=self.dataset.data     ,	key=slice(SCREAMING_SNAKE_CASE_     ,	offset + self.batch_size      )     ,	indices=self.dataset._indices     ,	)
         __lowerCamelCase  :      Tuple														=						batch.to_pandas().to_json(
             path_or_buf=SCREAMING_SNAKE_CASE_     ,	orient=SCREAMING_SNAKE_CASE_     ,	lines=SCREAMING_SNAKE_CASE_     ,	index=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         if not json_str.endswith('\n'      ):
              json_str += "\n"
         return json_str.encode(self.encoding      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         __lowerCamelCase  :      Any														=						0

         if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0     ,	len(self.dataset      )     ,	self.batch_size      )     ,	unit='ba'     ,	disable=not logging.is_progress_bar_enabled()     ,	desc='Creating json from Arrow format'     ,	):
                   __lowerCamelCase  :      Optional[int]														=						self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
                   written += file_obj.write(SCREAMING_SNAKE_CASE_      )
         else:
              __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						len(self.dataset      ), self.batch_size
              with multiprocessing.Pool(self.num_proc      ) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json     ,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )]     ,	)     ,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,	unit='ba'     ,	disable=not logging.is_progress_bar_enabled()     ,	desc='Creating json from Arrow format'     ,	):
                        written += file_obj.write(SCREAMING_SNAKE_CASE_      )

         return written







'''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


UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_      	=	'''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Tuple     		= CamembertTokenizer
						A_			:							Dict     		= CamembertTokenizerFast
						A_			:							Any     		= True
						A_			:							List[Any]     		= True




						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




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

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




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = 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(UpperCamelCase_      )							,							10_04      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertEqual(self.get_tokenizer().vocab_size							,							10_05      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )
													SCREAMING_SNAKE_CASE__     :Dict						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													# <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)
													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




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

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizer()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.tokenize(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :str						    = self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
													    '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='camembert-base'							,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'							,							sequences=UpperCamelCase_							,							)






