





'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase      :  List[Any]      							={'''configuration_bit''': ['''BIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BitConfig''', '''BitOnnxConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase      :  str      							=[
     '''BIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BitForImageClassification''',
     '''BitModel''',
     '''BitPreTrainedModel''',
     '''BitBackbone''',
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase      :  Tuple      							=['''BitImageProcessor''']


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 lowerCAmelCase      :  List[str]      							=_LazyModule(__name__, globals()['''__file__'''], _import_structure)









'''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							):





     def __init__(				self	:      Dict			,   lowercase	:      NestedDataStructureLike[PathLike]			,   lowercase	:      Optional[NamedSplit] = None			,   lowercase	:      Optional[Features] = None			,   lowercase	:      str = None			,   lowercase	:      bool = False			,   lowercase	:      bool = False			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




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







     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




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

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







class    a_     :





     def __init__(				self	:      Dict			,   lowercase	:      Dataset			,   lowercase	:      Union[PathLike, BinaryIO]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




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

        lowercase_    :str					    =				dataset
        lowercase_    :List[Any]					    =				path_or_buf
        lowercase_    :Any					    =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        lowercase_    :int					    =				num_proc
        lowercase_    :Optional[Any]					    =				"utf-8"
        lowercase_    :Dict					    =				to_csv_kwargs





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :int					    =				self.to_csv_kwargs.pop("path_or_buf"			,   lowercase		)
        lowercase_    :List[str]					    =				self.to_csv_kwargs.pop("header"			,   lowercase		)
        lowercase_    :Optional[Any]					    =				self.to_csv_kwargs.pop("index"			,   lowercase		)

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





     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    :Optional[Any]					    =				args

        lowercase_    :Dict					    =				query_table(
            table=self.dataset.data			,   key=slice(lowercase			,   offset + self.batch_size		)			,   indices=self.dataset._indices			,   )
        lowercase_    :List[str]					    =				batch.to_pandas().to_csv(
            path_or_buf=lowercase			,   header=header if (offset == 0) else False			,   index=lowercase			,   **lowercase		)
        return csv_str.encode(self.encoding		)







     def 							lowercase__   (				self	:      Dict			,   lowercase	:      BinaryIO			,   lowercase	:      Dict			,   lowercase	:      int			,   **lowercase	:      Any		):





        """simple docstring"""




        lowercase_    :Dict					    =				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"			,   ):
              lowercase_    :Optional[Any]					    =				self._batch_csv((offset, header, index, to_csv_kwargs)		)
              written += file_obj.write(lowercase		)

        else:
           lowercase_    ,							lowercase_    :Optional[int]					    =				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			,   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 CSV from Arrow format"			,   ):
                 written += file_obj.write(lowercase		)

        return written


