


'''simple docstring'''



import timeit

import numpy as np

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[Any]							)     ->		Optional[Any]:
     def wrapper(*UpperCAmelCase_    :				int      ,    **UpperCAmelCase_    :				str							):
          __lowerCamelCase   :	Union[str, Any]								=		timeit.default_timer()
          __lowerCamelCase   :	Optional[int]								=		func(*UpperCAmelCase_      ,    **UpperCAmelCase_							)
          __lowerCamelCase   :	Optional[Any]								=		timeit.default_timer() - starttime
          return delta

     __lowerCamelCase   :	Optional[int]								=		func.__name__

     return wrapper



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				Tuple=1_00      ,    UpperCAmelCase_    :				Tuple=None							)     ->		Dict:
     __lowerCamelCase   :	List[str]								=		[]
     __lowerCamelCase   :	List[str]								=		seq_shapes or {}
     for i in range(UpperCAmelCase_							):
          __lowerCamelCase   :	Optional[int]								=		{}
          for col_id, (k, v) in enumerate(features.items()							):
               if isinstance(UpperCAmelCase_      ,    _ArrayXD							):
                    __lowerCamelCase   :	int								=		np.random.rand(*v.shape							).astype(v.dtype							)
               elif isinstance(UpperCAmelCase_      ,    datasets.Value							):
                    if v.dtype == "string":
                         __lowerCamelCase   :	Tuple								=		'The small grey turtle was surprisingly fast when challenged.'
                    else:
                         __lowerCamelCase   :	Tuple								=		np.random.randint(10      ,    size=1							).astype(v.dtype							).item()
               elif isinstance(UpperCAmelCase_      ,    datasets.Sequence							):
                    while isinstance(UpperCAmelCase_      ,    datasets.Sequence							):
                         __lowerCamelCase   :	List[Any]								=		v.feature
                    __lowerCamelCase   :	Optional[Any]								=		seq_shapes[k]
                    __lowerCamelCase   :	Tuple								=		np.random.rand(*UpperCAmelCase_							).astype(v.dtype							)
               __lowerCamelCase   :	Union[str, Any]								=		data

          dummy_data.append((i, example)							)

     return dummy_data



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				Tuple=1_00      ,    UpperCAmelCase_    :				List[str]=None							)     ->		Dict:
     __lowerCamelCase   :	Dict								=		generate_examples(UpperCAmelCase_      ,    num_examples=UpperCAmelCase_      ,    seq_shapes=UpperCAmelCase_							)

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

          __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		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   :	Any								=		datasets.Dataset.from_file(filename=UpperCAmelCase_      ,    info=datasets.DatasetInfo(features=UpperCAmelCase_							)							)

     return dataset










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






