






'''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_		(    lowerCamelCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   str:



        """simple docstring"""






        super().__init__(
            _SCREAMING_SNAKE_CASE    ,	split=_SCREAMING_SNAKE_CASE    ,	features=_SCREAMING_SNAKE_CASE    ,	cache_dir=_SCREAMING_SNAKE_CASE    ,	keep_in_memory=_SCREAMING_SNAKE_CASE    ,	streaming=_SCREAMING_SNAKE_CASE    ,	num_proc=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)
        UpperCamelCase       						=      field
        UpperCamelCase       						=      path_or_paths if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
        UpperCamelCase       						=      Json(
            cache_dir=_SCREAMING_SNAKE_CASE    ,	data_files=_SCREAMING_SNAKE_CASE    ,	features=_SCREAMING_SNAKE_CASE    ,	field=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)


  def 						A__		(   self   )						->   List[Any]:



        """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=_SCREAMING_SNAKE_CASE    ,	download_mode=_SCREAMING_SNAKE_CASE    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	base_path=_SCREAMING_SNAKE_CASE    ,	num_proc=self.num_proc    ,	)
              UpperCamelCase       						=      self.builder.as_dataset(
                  split=self.split    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	in_memory=self.keep_in_memory   )
        return dataset






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   str:



        """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_json_kwargs

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.to_json_kwargs.pop("""path_or_buf"""    ,	_SCREAMING_SNAKE_CASE   )
        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"""    ,	_SCREAMING_SNAKE_CASE   )

        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=_SCREAMING_SNAKE_CASE   ) as buffer:
                    UpperCamelCase       						=      self._write(file_obj=_SCREAMING_SNAKE_CASE    ,	orient=_SCREAMING_SNAKE_CASE    ,	lines=_SCREAMING_SNAKE_CASE    ,	index=_SCREAMING_SNAKE_CASE    ,	**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=_SCREAMING_SNAKE_CASE    ,	lines=_SCREAMING_SNAKE_CASE    ,	index=_SCREAMING_SNAKE_CASE    ,	**self.to_json_kwargs   )
        return written

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      args

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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   int:



        """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 json from Arrow format"""    ,	):
                    UpperCamelCase       						=      self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
                    written += file_obj.write(_SCREAMING_SNAKE_CASE   )
        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    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_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 json from Arrow format"""    ,	):
                          written += file_obj.write(_SCREAMING_SNAKE_CASE   )

        return written








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__											=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwiftFormerForImageClassification',
         'SwiftFormerModel',
         'SwiftFormerPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_swiftformer import (
         SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwiftFormerConfig,
         SwiftFormerOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swiftformer import (
              SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwiftFormerForImageClassification,
              SwiftFormerModel,
              SwiftFormerPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
