

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


lowerCAmelCase  : List[Any]      =	logging.get_logger(__name__)

lowerCAmelCase  : int      =	{
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}
class 			_A		(	_UpperCamelCase ,  _UpperCamelCase):
  SCREAMING_SNAKE_CASE     :   Dict							=	'dinat'

  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	{
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }

  def __init__(					self			,       _SCREAMING_SNAKE_CASE=4			,       _SCREAMING_SNAKE_CASE=3			,       _SCREAMING_SNAKE_CASE=64			,       _SCREAMING_SNAKE_CASE=[3, 4, 6, 5]			,       _SCREAMING_SNAKE_CASE=[2, 4, 8, 16]			,       _SCREAMING_SNAKE_CASE=7			,       _SCREAMING_SNAKE_CASE=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]			,       _SCREAMING_SNAKE_CASE=3.0			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE="gelu"			,       _SCREAMING_SNAKE_CASE=0.02			,       _SCREAMING_SNAKE_CASE=1e-5			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE			,       ):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE       )

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












import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						str	)	->       List[Any]:







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Union[str, Any]             =      re.compile(R"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""	)
											__UpperCamelCase					:	str             =      input_file.read()
											__UpperCamelCase					:	Optional[int]             =      regexp.search(lowerCAmelCase	)

						return match






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						str	)	->       Union[str, Any]:







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Dict             =      re.compile(R"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""					,     re.DOTALL	)
											__UpperCamelCase					:	str             =      input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											__UpperCamelCase					:	List[Any]             =      regexp.finditer(lowerCAmelCase	)

						__UpperCamelCase					:	int             =      [match for match in matches if match is not None and match.group(1	) is not None]
						return matches[0] if matches else None






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(lowerCAmelCase	)	):
																raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_print_statements(str(lowerCAmelCase	)	):
																raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''	)






