


"""simple docstring"""
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   __A  (__lowerCamelCase):





							'''simple docstring'''

							def __init__(					self				:   List[Any]		,							UpperCAmelCase_				:   NestedDataStructureLike[PathLike]		,							UpperCAmelCase_				:   Optional[NamedSplit] = None		,							UpperCAmelCase_				:   Optional[Features] = None		,							UpperCAmelCase_				:   str = None		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[int] = None		,							**UpperCAmelCase_				:   str		,							)       ->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Tuple:



														"""simple docstring"""



														if self.streaming:
																					snake_case_     							=		self.builder.as_streaming_dataset(split=self.split							)
														# Build regular (map-style) dataset
														else:
																					snake_case_     							=		None
																					snake_case_     							=		None
																					snake_case_     							=		None
																					snake_case_     							=		None

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





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   str		,							UpperCAmelCase_				:   Dataset		,							UpperCAmelCase_				:   Union[PathLike, BinaryIO]		,							UpperCAmelCase_				:   Optional[int] = None		,							UpperCAmelCase_				:   Optional[int] = None		,							**UpperCAmelCase_				:   Tuple		,							)       ->int:



														"""simple docstring"""



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

														snake_case_     							=		dataset
														snake_case_     							=		path_or_buf
														snake_case_     							=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
														snake_case_     							=		num_proc
														snake_case_     							=		"""utf-8"""
														snake_case_     							=		to_csv_kwargs



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		self.to_csv_kwargs.pop("""path_or_buf"""		,							UpperCAmelCase_							)
														snake_case_     							=		self.to_csv_kwargs.pop("""header"""		,							UpperCAmelCase_							)
														snake_case_     							=		self.to_csv_kwargs.pop("""index"""		,							UpperCAmelCase_							)

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



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Optional[int]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		args

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



							def 		lowerCAmelCase							(					self				:   Tuple		,							UpperCAmelCase_				:   BinaryIO		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Any		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		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"""		,							):
																												snake_case_     							=		self._batch_csv((offset, header, index, to_csv_kwargs)							)
																												written += file_obj.write(UpperCAmelCase_							)

														else:
																					snake_case_     							=		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		,							UpperCAmelCase_		,							UpperCAmelCase_							)]		,							)		,							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(UpperCAmelCase_							)

														return written










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase			:  List[str]										=				{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  Any										=				["LevitFeatureExtractor"]
      __UpperCAmelCase			:  Tuple										=				["LevitImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LevitForImageClassification",
          "LevitForImageClassificationWithTeacher",
          "LevitModel",
          "LevitPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






