






'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCAmelCase_       (     __A					,							__A					):




      """simple docstring"""




      UpperCamelCase_             =      '''nat'''

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






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Optional[Any]=4  ,   UpperCAmelCase      :		Any=3  ,   UpperCAmelCase      :		List[Any]=64  ,   UpperCAmelCase      :		Dict=[3, 4, 6, 5]  ,   UpperCAmelCase      :		List[str]=[2, 4, 8, 16]  ,   UpperCAmelCase      :		List[Any]=7  ,   UpperCAmelCase      :		Optional[Any]=3.0  ,   UpperCAmelCase      :		List[str]=True  ,   UpperCAmelCase      :		Optional[Any]=0.0  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Tuple=0.1  ,   UpperCAmelCase      :		Dict="gelu"  ,   UpperCAmelCase      :		Tuple=0.0_2  ,   UpperCAmelCase      :		List[Any]=1e-5  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Optional[Any]=None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	Dict:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)

       lowercase       : str						=patch_size
       lowercase       : List[Any]						=num_channels
       lowercase       : Optional[Any]						=embed_dim
       lowercase       : Any						=depths
       lowercase       : Optional[int]						=len(UpperCAmelCase		)
       lowercase       : Optional[Any]						=num_heads
       lowercase       : List[Any]						=kernel_size
       lowercase       : Optional[Any]						=mlp_ratio
       lowercase       : Tuple						=qkv_bias
       lowercase       : List[Any]						=hidden_dropout_prob
       lowercase       : Dict						=attention_probs_dropout_prob
       lowercase       : Any						=drop_path_rate
       lowercase       : Tuple						=hidden_act
       lowercase       : List[str]						=layer_norm_eps
       lowercase       : Dict						=initializer_range
       # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       lowercase       : Optional[Any]						=int(embed_dim * 2 ** (len(UpperCAmelCase		) - 1)		)
       lowercase       : Optional[int]						=layer_scale_init_value
       lowercase       : int						=['''stem'''] + [f'stage{idx}' for idx in range(1  ,   len(UpperCAmelCase		) + 1		)]
       lowercase	,		lowercase       : Dict						=get_aligned_output_features_output_indices(
           out_features=UpperCAmelCase  ,   out_indices=UpperCAmelCase  ,   stage_names=self.stage_names		)











'''simple docstring'''



import argparse
from collections import defaultdict


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Optional[int]       ,   __A			:							str       ,   __A			:							Tuple       ,   __A			:							int      )							->   Dict:



 """simple docstring"""




 lowercase       : int						=F'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : List[Any]						=f.readlines()

 lowercase       : Tuple						=F'class {class_name}('
 lowercase       : Optional[Any]						=F'{4 * " "}def {test_name}('
 lowercase       : Union[str, Any]						=F'{8 * " "}{correct_line.split()[0]}'
 lowercase       : List[Any]						=F'{1_6 * " "}{correct_line.split()[0]}'
 lowercase       : int						=False
 lowercase       : Dict						=False
 lowercase       : Optional[Any]						=False
 lowercase       : str						=False
 lowercase       : str						=0
 lowercase       : Optional[Any]						=0

 lowercase       : List[Any]						=[]
 for line in lines:
  if line.startswith(__A      ):
   lowercase       : List[str]						=True
  elif in_class and line.startswith(__A      ):
   lowercase       : int						=True
  elif in_class and in_func and (line.startswith(__A      ) or line.startswith(__A      )):
   lowercase       : str						=len(line.split(correct_line.split()[0]      )[0]      )
   count += 1

   if count == done_test[_id]:
    lowercase       : Dict						=True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    lowercase       : Any						=True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(F'{spaces * " "}{correct_line}'      )
   lowercase       : List[str]						=False
  else:
   new_lines.append(__A      )

 with open(__A       ,   '''w'''      ) as f:
  for line in new_lines:
   f.write(__A      )


def 				lowercase_					(     __A			:							Any       ,   __A			:							List[Any]=None      )							->   str:



 """simple docstring"""




 if fail is not None:
  with open(__A       ,   '''r'''      ) as f:
   lowercase       : Optional[Any]						={l.strip() for l in f.readlines()}
 else:
  lowercase       : int						=None

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : Union[str, Any]						=f.readlines()

 lowercase       : Union[str, Any]						=defaultdict(__A      )
 for line in correct_lines:
  lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[int]						=line.split(''';'''      )
  if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
   overwrite_file(__A       ,   __A       ,   __A       ,   __A       ,   __A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--correct_filename', help='filename of tests with expected result')
      parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      main(args.correct_filename, args.fail_filename)



