


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      def wrapper(*UpperCAmelCase      :			int						,	**UpperCAmelCase      :			Dict      ):
            __lowerCamelCase     :      int       						=						timeit.default_timer()
            __lowerCamelCase     :      Dict       						=						func(*__snake_case						,	**__snake_case      )
            __lowerCamelCase     :      List[str]       						=						timeit.default_timer() - starttime
            return delta

      __lowerCamelCase     :      List[str]       						=						func.__name__

      return wrapper





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[str]=100						,	UpperCAmelCase      :			str=None      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						[]
      __lowerCamelCase     :      Optional[int]       						=						seq_shapes or {}
      for i in range(__snake_case      ):
            __lowerCamelCase     :      Any       						=						{}
            for col_id, (k, v) in enumerate(features.items()      ):
                  if isinstance(__snake_case						,	_ArrayXD      ):
                        __lowerCamelCase     :      List[Any]       						=						np.random.rand(*v.shape      ).astype(v.dtype      )
                  elif isinstance(__snake_case						,	datasets.Value      ):
                        if v.dtype == "string":
                              __lowerCamelCase     :      Optional[int]       						=						"""The small grey turtle was surprisingly fast when challenged."""
                        else:
                              __lowerCamelCase     :      Any       						=						np.random.randint(10						,	size=1      ).astype(v.dtype      ).item()
                  elif isinstance(__snake_case						,	datasets.Sequence      ):
                        while isinstance(__snake_case						,	datasets.Sequence      ):
                              __lowerCamelCase     :      str       						=						v.feature
                        __lowerCamelCase     :      List[str]       						=						seq_shapes[k]
                        __lowerCamelCase     :      int       						=						np.random.rand(*__snake_case      ).astype(v.dtype      )
                  __lowerCamelCase     :      Optional[int]       						=						data

            dummy_data.append((i, example)      )

      return dummy_data






def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			Dict=100						,	UpperCAmelCase      :			Any=None      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						generate_examples(__snake_case						,	num_examples=__snake_case						,	seq_shapes=__snake_case      )

      with ArrowWriter(features=__snake_case						,	path=__snake_case      ) as writer:
            for key, record in dummy_data:
                  __lowerCamelCase     :      Any       						=						features.encode_example(__snake_case      )
                  writer.write(__snake_case      )

            __lowerCamelCase						, __lowerCamelCase     :      Optional[int]       						=						writer.finalize()

      if not num_final_examples == num_examples:
            raise ValueError(
                f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

      __lowerCamelCase     :      List[Any]       						=						datasets.Dataset.from_file(filename=__snake_case						,	info=datasets.DatasetInfo(features=__snake_case      )      )

      return dataset







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Optional[Any]     =							{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[Any]     =							[
        'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Swinv2ForImageClassification',
        'Swinv2ForMaskedImageModeling',
        'Swinv2Model',
        'Swinv2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swinva import (
            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinvaForImageClassification,
            SwinvaForMaskedImageModeling,
            SwinvaModel,
            SwinvaPreTrainedModel,
        )


else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



