


'''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 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = False			,     lowerCamelCase = False			,     lowerCamelCase = None			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(
              lowerCamelCase			,     split=lowerCamelCase			,     features=lowerCamelCase			,     cache_dir=lowerCamelCase			,     keep_in_memory=lowerCamelCase			,     streaming=lowerCamelCase			,     num_proc=lowerCamelCase			,     **lowerCamelCase			,     )
          __A	:       List[Any]      =							field
          __A	:       Tuple      =							path_or_paths if isinstance(lowerCamelCase			,     lowerCamelCase		) else {self.split: path_or_paths}
          __A	:       Union[str, Any]      =							Json(
              cache_dir=lowerCamelCase			,     data_files=lowerCamelCase			,     features=lowerCamelCase			,     field=lowerCamelCase			,     **lowerCamelCase			,     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

               self.builder.download_and_prepare(
                   download_config=lowerCamelCase			,     download_mode=lowerCamelCase			,     verification_mode=lowerCamelCase			,     base_path=lowerCamelCase			,     num_proc=self.num_proc			,     )
               __A	:       str      =							self.builder.as_dataset(
                   split=self.split			,     verification_mode=lowerCamelCase			,     in_memory=self.keep_in_memory		)
          return dataset







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          if num_proc is not None and num_proc <= 0:
               raise ValueError(f"num_proc {num_proc} must be an integer > 0."		)

          __A	:       Optional[int]      =							dataset
          __A	:       Union[str, Any]      =							path_or_buf
          __A	:       Any      =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          __A	:       List[str]      =							num_proc
          __A	:       Optional[int]      =							"utf-8"
          __A	:       str      =							to_json_kwargs


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.to_json_kwargs.pop("path_or_buf"			,     lowerCamelCase		)
          __A	:       Tuple      =							self.to_json_kwargs.pop("orient"			,     "records"		)
          __A	:       Dict      =							self.to_json_kwargs.pop("lines"			,     True if orient == "records" else False		)
          __A	:       Optional[int]      =							self.to_json_kwargs.pop("index"			,     False if orient in ["split", "table"] else True		)
          __A	:       Dict      =							self.to_json_kwargs.pop("compression"			,     lowerCamelCase		)

          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=lowerCamelCase		) as buffer:
                    __A	:       List[str]      =							self._write(file_obj=lowerCamelCase			,     orient=lowerCamelCase			,     lines=lowerCamelCase			,     index=lowerCamelCase			,     **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."		)
               __A	:       Union[str, Any]      =							self._write(
                   file_obj=self.path_or_buf			,     orient=lowerCamelCase			,     lines=lowerCamelCase			,     index=lowerCamelCase			,     **self.to_json_kwargs		)
          return written


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A  ,__A  ,__A  ,__A  ,__A	:       Dict      =							args

          __A	:       Union[str, Any]      =							query_table(
              table=self.dataset.data			,     key=slice(lowerCamelCase			,     offset + self.batch_size		)			,     indices=self.dataset._indices			,     )
          __A	:       Optional[Any]      =							batch.to_pandas().to_json(
              path_or_buf=lowerCamelCase			,     orient=lowerCamelCase			,     lines=lowerCamelCase			,     index=lowerCamelCase			,     **lowerCamelCase		)
          if not json_str.endswith("\n"		):
               json_str += "\n"
          return json_str.encode(self.encoding		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       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"			,     ):
                    __A	:       Union[str, Any]      =							self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
                    written += file_obj.write(lowerCamelCase		)
          else:
               __A  ,__A	:       str      =							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			,     lowerCamelCase			,     lowerCamelCase		)]			,     )			,     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(lowerCamelCase		)

          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 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				CamembertTokenizer
     lowerCamelCase__       						=				CamembertTokenizerFast
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()

          # We have a SentencePiece fixture for testing
          __A	:       List[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							"<pad>"
          __A	:       Union[str, Any]      =							1

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, 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(lowerCamelCase		)			,     1004		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertEqual(self.get_tokenizer().vocab_size			,     1005		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          __A	:       Optional[int]      =							CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          __A	:       str      =							"I was born in 92000, and this is falsé."

          __A	:       List[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Tuple      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Dict      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       str      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          # <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)
          __A	:       int      =							tokenizer.convert_ids_to_tokens(lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          if not self.test_rust_tokenizer:
               return

          __A	:       Tuple      =							self.get_tokenizer()
          __A	:       List[Any]      =							self.get_rust_tokenizer()

          __A	:       Any      =							"I was born in 92000, and this is falsé."

          __A	:       int      =							tokenizer.tokenize(lowerCamelCase		)
          __A	:       Any      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

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

          __A	:       Union[str, Any]      =							self.get_rust_tokenizer()
          __A	:       Optional[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Dict      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							{"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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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.
          __A	:       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=lowerCamelCase			,     model_name="camembert-base"			,     revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"			,     sequences=lowerCamelCase			,     )

