


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 		_snake_case  (      _snake_case							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(
           _lowerCamelCase					,  split=_lowerCamelCase					,  features=_lowerCamelCase					,  cache_dir=_lowerCamelCase					,  keep_in_memory=_lowerCamelCase					,  streaming=_lowerCamelCase					,  num_proc=_lowerCamelCase					,  **_lowerCamelCase					,  )
       a						:Optional[Any] 							=       field
       a						:Optional[Any] 							=       path_or_paths if isinstance(_lowerCamelCase					,  _lowerCamelCase    ) else {self.split: path_or_paths}
       a						:Union[str, Any] 							=       Json(
           cache_dir=_lowerCamelCase					,  data_files=_lowerCamelCase					,  features=_lowerCamelCase					,  field=_lowerCamelCase					,  **_lowerCamelCase					,  )




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

             self.builder.download_and_prepare(
                 download_config=_lowerCamelCase					,  download_mode=_lowerCamelCase					,  verification_mode=_lowerCamelCase					,  base_path=_lowerCamelCase					,  num_proc=self.num_proc					,  )
             a						:Optional[int] 							=       self.builder.as_dataset(
                 split=self.split					,  verification_mode=_lowerCamelCase					,  in_memory=self.keep_in_memory    )
       return dataset







class 		_snake_case  :




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

       a						:Union[str, Any] 							=       dataset
       a						:Tuple 							=       path_or_buf
       a						:Union[str, Any] 							=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       a						:int 							=       num_proc
       a						:int 							=       '''utf-8'''
       a						:Any 							=       to_json_kwargs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.to_json_kwargs.pop('''path_or_buf'''					,  _lowerCamelCase    )
       a						:Tuple 							=       self.to_json_kwargs.pop('''orient'''					,  '''records'''    )
       a						:Dict 							=       self.to_json_kwargs.pop('''lines'''					,  True if orient == '''records''' else False    )
       a						:Any 							=       self.to_json_kwargs.pop('''index'''					,  False if orient in ['''split''', '''table'''] else True    )
       a						:int 							=       self.to_json_kwargs.pop('''compression'''					,  _lowerCamelCase    )

       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=_lowerCamelCase    ) as buffer:
                   a						:Optional[int] 							=       self._write(file_obj=_lowerCamelCase					,  orient=_lowerCamelCase					,  lines=_lowerCamelCase					,  index=_lowerCamelCase					,  **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.'''    )
             a						:List[Any] 							=       self._write(
                 file_obj=self.path_or_buf					,  orient=_lowerCamelCase					,  lines=_lowerCamelCase					,  index=_lowerCamelCase					,  **self.to_json_kwargs    )
       return written




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       args

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  **_lowerCamelCase					,  ):
       a						:Tuple 							=       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'''					,  ):
                   a						:Dict 							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)    )
                   written += file_obj.write(_lowerCamelCase    )
       else:
             a						:Optional[int] 							=       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					,  _lowerCamelCase					,  _lowerCamelCase    )]					,  )					,  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(_lowerCamelCase    )

       return written










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


