



"""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 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:NestedDataStructureLike[PathLike]       ,  lowercase_			:Optional[NamedSplit] = None       ,  lowercase_			:Optional[Features] = None       ,  lowercase_			:str = None       ,  lowercase_			:bool = False       ,  lowercase_			:bool = False       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Optional[int] = None       ,  **lowercase_			:Tuple       ,  )    ->      List[str]:
												super().__init__(
												    lowercase_       ,  split=lowercase_       ,  features=lowercase_       ,  cache_dir=lowercase_       ,  keep_in_memory=lowercase_       ,  streaming=lowercase_       ,  num_proc=lowercase_       ,  **lowercase_       ,  )
												UpperCAmelCase					=			field
												UpperCAmelCase					=			path_or_paths if isinstance(lowercase_       ,  lowercase_ ) else {self.split: path_or_paths}
												UpperCAmelCase					=			Json(
												    cache_dir=lowercase_       ,  data_files=lowercase_       ,  features=lowercase_       ,  field=lowercase_       ,  **lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Dict:
												# Build iterable dataset
												if self.streaming:
																		UpperCAmelCase					=			self.builder.as_streaming_dataset(split=self.split )
												# Build regular (map-style) dataset
												else:
																		UpperCAmelCase					=			None
																		UpperCAmelCase					=			None
																		UpperCAmelCase					=			None
																		UpperCAmelCase					=			None

																		self.builder.download_and_prepare(
																		    download_config=lowercase_       ,  download_mode=lowercase_       ,  verification_mode=lowercase_       ,  base_path=lowercase_       ,  num_proc=self.num_proc       ,  )
																		UpperCAmelCase					=			self.builder.as_dataset(
																		    split=self.split       ,  verification_mode=lowercase_       ,  in_memory=self.keep_in_memory )
												return dataset







class 				A_      :



						"""simple docstring"""






						def __init__( self			:int       ,  lowercase_			:Dataset       ,  lowercase_			:Union[PathLike, BinaryIO]       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:Optional[int] = None       ,  **lowercase_			:Optional[Any]       ,  )    ->      int:
												if num_proc is not None and num_proc <= 0:
																		raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

												UpperCAmelCase					=			dataset
												UpperCAmelCase					=			path_or_buf
												UpperCAmelCase					=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												UpperCAmelCase					=			num_proc
												UpperCAmelCase					=			'utf-8'
												UpperCAmelCase					=			to_json_kwargs






						def      UpperCAmelCase__					( self			:int )    ->      int:
												UpperCAmelCase					=			self.to_json_kwargs.pop('path_or_buf'       ,  lowercase_ )
												UpperCAmelCase					=			self.to_json_kwargs.pop('orient'       ,  'records' )
												UpperCAmelCase					=			self.to_json_kwargs.pop('lines'       ,  True if orient == 'records' else False )
												UpperCAmelCase					=			self.to_json_kwargs.pop('index'       ,  False if orient in ['split', 'table'] else True )
												UpperCAmelCase					=			self.to_json_kwargs.pop('compression'       ,  lowercase_ )

												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=lowercase_ ) as buffer:
																								UpperCAmelCase					=			self._write(file_obj=lowercase_       ,  orient=lowercase_       ,  lines=lowercase_       ,  index=lowercase_       ,  **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.' )
																		UpperCAmelCase					=			self._write(
																		    file_obj=self.path_or_buf       ,  orient=lowercase_       ,  lines=lowercase_       ,  index=lowercase_       ,  **self.to_json_kwargs )
												return written






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Dict )    ->      List[str]:
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			args

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






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:BinaryIO       ,  lowercase_			:int       ,  lowercase_			:Any       ,  lowercase_			:Tuple       ,  **lowercase_			:Tuple       ,  )    ->      int:
												UpperCAmelCase					=			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'       ,  ):
																								UpperCAmelCase					=			self._batch_json((offset, orient, lines, index, to_json_kwargs) )
																								written += file_obj.write(lowercase_ )
												else:
																		UpperCAmelCase				,   UpperCAmelCase					=			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       ,  lowercase_       ,  lowercase_ )]       ,  )       ,  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(lowercase_ )

												return written










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





