





import timeit

import numpy as np

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






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   def wrapper(*A				,							**A     ):
      UpperCAmelCase__						=timeit.default_timer()
      UpperCAmelCase__						=func(*A				,							**A     )
      UpperCAmelCase__						=timeit.default_timer() - starttime
      return delta

   UpperCAmelCase__						=func.__name__

   return wrapper






def 				_UpperCAmelCase			(    A				,							A=100				,							A=None     ):




   '''simple docstring'''

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

      dummy_data.append((i, example)     )

   return dummy_data






def 				_UpperCAmelCase			(    A				,							A				,							A=100				,							A=None     ):




   '''simple docstring'''

   UpperCAmelCase__						=generate_examples(A				,							num_examples=A				,							seq_shapes=A     )

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

      UpperCAmelCase__			,							UpperCAmelCase__						=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}."""     )

   UpperCAmelCase__						=datasets.Dataset.from_file(filename=A				,							info=datasets.DatasetInfo(features=A     )     )

   return dataset










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



