





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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       def __init__(    self					:       List[Any]				,							A					:       NestedDataStructureLike[PathLike]				,							A					:       Optional[NamedSplit] = None				,							A					:       Optional[Features] = None				,							A					:       str = None				,							A					:       bool = False				,							A					:       bool = False				,							A					:       Optional[int] = None				,							**A					:       str				,							):
         super().__init__(
             A				,							split=A				,							features=A				,							cache_dir=A				,							keep_in_memory=A				,							streaming=A				,							num_proc=A				,							**A				,							)
         __snake_case:		List[Any]					    =		path_or_paths if isinstance(A				,							A						) else {self.split: path_or_paths}
         __snake_case:		str					    =		Csv(
             cache_dir=A				,							data_files=A				,							features=A				,							**A				,							)






       def 				UpperCAmelCase__					(    self					:       Dict						):
         # Build iterable dataset
         if self.streaming:
           __snake_case:		Any					    =		self.builder.as_streaming_dataset(split=self.split						)
         # Build regular (map-style) dataset
         else:
           __snake_case:		Optional[int]					    =		None
           __snake_case:		Dict					    =		None
           __snake_case:		List[Any]					    =		None
           __snake_case:		List[Any]					    =		None

           self.builder.download_and_prepare(
               download_config=A				,							download_mode=A				,							verification_mode=A				,							base_path=A				,							num_proc=self.num_proc				,							)
           __snake_case:		Any					    =		self.builder.as_dataset(
               split=self.split				,							verification_mode=A				,							in_memory=self.keep_in_memory						)
         return dataset







class        __snake_case			:





       '''simple docstring'''







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

         __snake_case:		Any					    =		dataset
         __snake_case:		Any					    =		path_or_buf
         __snake_case:		Union[str, Any]					    =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         __snake_case:		Optional[Any]					    =		num_proc
         __snake_case:		Dict					    =		"""utf-8"""
         __snake_case:		Union[str, Any]					    =		to_csv_kwargs




       def 				UpperCAmelCase__					(    self					:       Tuple						):
         __snake_case:		str					    =		self.to_csv_kwargs.pop("""path_or_buf"""				,							A						)
         __snake_case:		Optional[Any]					    =		self.to_csv_kwargs.pop("""header"""				,							A						)
         __snake_case:		Dict					    =		self.to_csv_kwargs.pop("""index"""				,							A						)

         if isinstance(self.path_or_buf				,							(str, bytes, os.PathLike)						):
           with open(self.path_or_buf				,							"""wb+"""						) as buffer:
             __snake_case:		Dict					    =		self._write(file_obj=A				,							header=A				,							index=A				,							**self.to_csv_kwargs						)
         else:
           __snake_case:		Union[str, Any]					    =		self._write(file_obj=self.path_or_buf				,							header=A				,							index=A				,							**self.to_csv_kwargs						)
         return written




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Optional[int]						):
         __snake_case    ,  __snake_case    ,  __snake_case    ,  __snake_case:		str					    =		args

         __snake_case:		Optional[int]					    =		query_table(
             table=self.dataset.data				,							key=slice(A				,							offset + self.batch_size						)				,							indices=self.dataset._indices				,							)
         __snake_case:		Any					    =		batch.to_pandas().to_csv(
             path_or_buf=A				,							header=header if (offset == 0) else False				,							index=A				,							**A						)
         return csv_str.encode(self.encoding						)






       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       BinaryIO				,							A					:       str				,							A					:       Any				,							**A					:       Union[str, Any]						):
         __snake_case:		Optional[int]					    =		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:		Union[str, Any]					    =		self._batch_csv((offset, header, index, to_csv_kwargs)						)
             written += file_obj.write(A						)

         else:
           __snake_case    ,  __snake_case:		Dict					    =		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				,							A				,							A						)]				,							)				,							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(A						)

         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__)






