



"""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 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



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





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

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



class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A       ,	A = None       ,	A = None       ,	**A       ,	)							->					List[str]:
         if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'		)

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





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[Any]											=    self.to_json_kwargs.pop('''path_or_buf'''       ,	A		)
         _UpperCAmelCase							:  Tuple											=    self.to_json_kwargs.pop('''orient'''       ,	'''records'''		)
         _UpperCAmelCase							:  Dict											=    self.to_json_kwargs.pop('''lines'''       ,	True if orient == '''records''' else False		)
         _UpperCAmelCase							:  Any											=    self.to_json_kwargs.pop('''index'''       ,	False if orient in ['''split''', '''table'''] else True		)
         _UpperCAmelCase							:  int											=    self.to_json_kwargs.pop('''compression'''       ,	A		)

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





     def  __lowerCAmelCase	(					self       ,	A		)							->					Union[str, Any]:
         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    args

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	**A       ,	)							->					int:
         _UpperCAmelCase							:  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'''       ,	):
                 _UpperCAmelCase							:  Dict											=    self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
                 written += file_obj.write(A		)
         else:
             _UpperCAmelCase							,				_UpperCAmelCase							:  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       ,	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 json from Arrow format'''       ,	):
                     written += file_obj.write(A		)

         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__)


