





import timeit

import numpy as np

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

def     a_       (					lowerCAmelCase_ :     int      ):
   def wrapper(*lowerCAmelCase_ :     List[str],      **lowerCAmelCase_ :     Dict      ):
      __lowerCAmelCase				      =      timeit.default_timer()
      __lowerCAmelCase				      =      func(*lowerCAmelCase_,      **lowerCAmelCase_      )
      __lowerCAmelCase				      =      timeit.default_timer() - starttime
      return delta

   __lowerCAmelCase				      =      func.__name__

   return wrapper

def     a_       (					lowerCAmelCase_ :     List[Any],      lowerCAmelCase_ :     str=100,      lowerCAmelCase_ :     str=None      ):
   __lowerCAmelCase				      =      []
   __lowerCAmelCase				      =      seq_shapes or {}
   for i in range(lowerCAmelCase_      ):
      __lowerCAmelCase				      =      {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(lowerCAmelCase_,      _ArrayXD      ):
            __lowerCAmelCase				      =      np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(lowerCAmelCase_,      datasets.Value      ):
            if v.dtype == "string":
               __lowerCAmelCase				      =      'The small grey turtle was surprisingly fast when challenged.'
            else:
               __lowerCAmelCase				      =      np.random.randint(10,      size=1      ).astype(v.dtype      ).item()
         elif isinstance(lowerCAmelCase_,      datasets.Sequence      ):
            while isinstance(lowerCAmelCase_,      datasets.Sequence      ):
               __lowerCAmelCase				      =      v.feature
            __lowerCAmelCase				      =      seq_shapes[k]
            __lowerCAmelCase				      =      np.random.rand(*lowerCAmelCase_      ).astype(v.dtype      )
         __lowerCAmelCase				      =      data

      dummy_data.append((i, example)      )

   return dummy_data




def     a_       (					lowerCAmelCase_ :     Dict,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     int=100,      lowerCAmelCase_ :     Dict=None      ):
   __lowerCAmelCase				      =      generate_examples(lowerCAmelCase_,      num_examples=lowerCAmelCase_,      seq_shapes=lowerCAmelCase_      )

   with ArrowWriter(features=lowerCAmelCase_,      path=lowerCAmelCase_      ) as writer:
      for key, record in dummy_data:
         __lowerCAmelCase				      =      features.encode_example(lowerCAmelCase_      )
         writer.write(lowerCAmelCase_      )

      __lowerCAmelCase      ,      __lowerCAmelCase				      =      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				      =      datasets.Dataset.from_file(filename=lowerCAmelCase_,      info=datasets.DatasetInfo(features=lowerCAmelCase_      )      )

   return dataset


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Any       			=		{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

				_lowercase:	Tuple       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






