



"""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			(      A_       ):







		'''simple docstring'''





		def __init__(      self		:    List[str]     ,_snake_case		:    Dict     ,_snake_case		:    Optional[int] = None     ,_snake_case		:    str = None     ,_snake_case		:    Union[str, Any] = None     ,_snake_case		:    Any = False     ,_snake_case		:    List[Any] = False     ,_snake_case		:    Dict = None     ,_snake_case		:    int = None     ,**_snake_case		:    Tuple     ,)   ->						int:






				"""simple docstring"""



				super().__init__(
				    _snake_case     ,split=_snake_case     ,features=_snake_case     ,cache_dir=_snake_case     ,keep_in_memory=_snake_case     ,streaming=_snake_case     ,num_proc=_snake_case     ,**_snake_case     ,)
				lowercase__  :       List[Any]												=     field
				lowercase__  :       Tuple												=     path_or_paths if isinstance(_snake_case     ,_snake_case		) else {self.split: path_or_paths}
				lowercase__  :       Union[str, Any]												=     Json(
				    cache_dir=_snake_case     ,data_files=_snake_case     ,features=_snake_case     ,field=_snake_case     ,**_snake_case     ,)







		def    UpperCAmelCase       (      self		:    Tuple		)   ->						Any:






				"""simple docstring"""



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

						self.builder.download_and_prepare(
						    download_config=_snake_case     ,download_mode=_snake_case     ,verification_mode=_snake_case     ,base_path=_snake_case     ,num_proc=self.num_proc     ,)
						lowercase__  :       str												=     self.builder.as_dataset(
						    split=self.split     ,verification_mode=_snake_case     ,in_memory=self.keep_in_memory		)
				return dataset





class 			__A			:







		'''simple docstring'''





		def __init__(      self		:    Any     ,_snake_case		:    Optional[Any]     ,_snake_case		:    Union[str, Any]     ,_snake_case		:    Tuple = None     ,_snake_case		:    Any = None     ,**_snake_case		:    Union[str, Any]     ,)   ->						Tuple:






				"""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__  :       Optional[int]												=     dataset
				lowercase__  :       Union[str, Any]												=     path_or_buf
				lowercase__  :       Any												=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				lowercase__  :       List[str]												=     num_proc
				lowercase__  :       Optional[int]												=     "utf-8"
				lowercase__  :       str												=     to_json_kwargs







		def    UpperCAmelCase       (      self		:    str		)   ->						Tuple:






				"""simple docstring"""



				lowercase__  :       Union[str, Any]												=     self.to_json_kwargs.pop('''path_or_buf'''     ,_snake_case		)
				lowercase__  :       Tuple												=     self.to_json_kwargs.pop('''orient'''     ,'''records'''		)
				lowercase__  :       Dict												=     self.to_json_kwargs.pop('''lines'''     ,True if orient == '''records''' else False		)
				lowercase__  :       Optional[int]												=     self.to_json_kwargs.pop('''index'''     ,False if orient in ['''split''', '''table'''] else True		)
				lowercase__  :       Dict												=     self.to_json_kwargs.pop('''compression'''     ,_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=_snake_case		) as buffer:
								lowercase__  :       List[str]												=     self._write(file_obj=_snake_case     ,orient=_snake_case     ,lines=_snake_case     ,index=_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.'''		)
						lowercase__  :       Union[str, Any]												=     self._write(
						    file_obj=self.path_or_buf     ,orient=_snake_case     ,lines=_snake_case     ,index=_snake_case     ,**self.to_json_kwargs		)
				return written







		def    UpperCAmelCase       (      self		:    Any     ,_snake_case		:    str		)   ->						Any:






				"""simple docstring"""



				lowercase__  :       Dict												=     args

				lowercase__  :       Union[str, Any]												=     query_table(
				    table=self.dataset.data     ,key=slice(_snake_case     ,offset + self.batch_size		)     ,indices=self.dataset._indices     ,)
				lowercase__  :       Optional[Any]												=     batch.to_pandas().to_json(
				    path_or_buf=_snake_case     ,orient=_snake_case     ,lines=_snake_case     ,index=_snake_case     ,**_snake_case		)
				if not json_str.endswith('''\n'''		):
						json_str += "\n"
				return json_str.encode(self.encoding		)







		def    UpperCAmelCase       (      self		:    Optional[Any]     ,_snake_case		:    Union[str, Any]     ,_snake_case		:    Tuple     ,_snake_case		:    List[Any]     ,_snake_case		:    Tuple     ,**_snake_case		:    Optional[Any]     ,)   ->						Any:






				"""simple docstring"""



				lowercase__  :       Any												=     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'''     ,):
								lowercase__  :       Union[str, Any]												=     self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
								written += file_obj.write(_snake_case		)
				else:
						lowercase__  :       str												=     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     ,_snake_case     ,_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(_snake_case		)

				return written




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_UpperCamelCase 							=  {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  ["""ViTFeatureExtractor"""]
      _UpperCamelCase 							=  ["""ViTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ViTForImageClassification""",
          """ViTForMaskedImageModeling""",
          """ViTModel""",
          """ViTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """TFViTForImageClassification""",
          """TFViTModel""",
          """TFViTPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """FlaxViTForImageClassification""",
          """FlaxViTModel""",
          """FlaxViTPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_vit import ViTFeatureExtractor
            from .image_processing_vit import ViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vit import (
                VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViTForImageClassification,
                ViTForMaskedImageModeling,
                ViTModel,
                ViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

