



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

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


class    _lowercase (a_   ):
				'''simple docstring'''







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

									'''simple docstring'''


									super().__init__(
									    snake_case__  ,			split=snake_case__  ,			features=snake_case__  ,			cache_dir=snake_case__  ,			keep_in_memory=snake_case__  ,			streaming=snake_case__  ,			num_proc=snake_case__  ,			**snake_case__  ,			)
									UpperCamelCase_			= path_or_paths if isinstance(snake_case__  ,			snake_case__     ) else {self.split: path_or_paths}
									UpperCamelCase_			= Csv(
									    cache_dir=snake_case__  ,			data_files=snake_case__  ,			features=snake_case__  ,			**snake_case__  ,			)

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									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=snake_case__  ,			download_mode=snake_case__  ,			verification_mode=snake_case__  ,			base_path=snake_case__  ,			num_proc=self.num_proc  ,			)
														UpperCamelCase_			= self.builder.as_dataset(
														    split=self.split  ,			verification_mode=snake_case__  ,			in_memory=self.keep_in_memory     )
									return dataset




class    _lowercase :
				'''simple docstring'''







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

									'''simple docstring'''


									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_csv_kwargs

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.to_csv_kwargs.pop("path_or_buf"  ,			snake_case__     )
									UpperCamelCase_			= self.to_csv_kwargs.pop("header"  ,			snake_case__     )
									UpperCamelCase_			= self.to_csv_kwargs.pop("index"  ,			snake_case__     )

									if isinstance(self.path_or_buf  ,			(str, bytes, os.PathLike)     ):
														with open(self.path_or_buf  ,			"wb+"     ) as buffer:
																			UpperCamelCase_			= self._write(file_obj=snake_case__  ,			header=snake_case__  ,			index=snake_case__  ,			**self.to_csv_kwargs     )
									else:
														UpperCamelCase_			= self._write(file_obj=self.path_or_buf  ,			header=snake_case__  ,			index=snake_case__  ,			**self.to_csv_kwargs     )
									return written

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= args

									UpperCamelCase_			= query_table(
									    table=self.dataset.data  ,			key=slice(snake_case__  ,			offset + self.batch_size     )  ,			indices=self.dataset._indices  ,			)
									UpperCamelCase_			= batch.to_pandas().to_csv(
									    path_or_buf=snake_case__  ,			header=header if (offset == 0) else False  ,			index=snake_case__  ,			**snake_case__     )
									return csv_str.encode(self.encoding     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									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 CSV from Arrow format"  ,			):
																			UpperCamelCase_			= self._batch_csv((offset, header, index, to_csv_kwargs)     )
																			written += file_obj.write(snake_case__     )

									else:
														UpperCamelCase_	,     UpperCamelCase_			= len(self.dataset     ), self.batch_size
														with multiprocessing.Pool(self.num_proc     ) as pool:
																			for csv_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_csv  ,			[(offset, header, index, to_csv_kwargs) for offset in range(0  ,			snake_case__  ,			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 CSV from Arrow format"  ,			):
																								written += file_obj.write(snake_case__     )

									return written





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
