
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """microsoft/swin-tiny-patch4-window7-224""": (
        """https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class     __UpperCamelCase (							lowerCAmelCase__ ,						lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''swin'''

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



     def __init__(				self							:							int     ,						_A							:							int=224     ,						_A							:							Any=4     ,						_A							:							List[Any]=3     ,						_A							:							int=96     ,						_A							:							Dict=[2, 2, 6, 2]     ,						_A							:							Optional[int]=[3, 6, 12, 24]     ,						_A							:							int=7     ,						_A							:							Optional[Any]=4.0     ,						_A							:							Optional[int]=True     ,						_A							:							Optional[int]=0.0     ,						_A							:							int=0.0     ,						_A							:							Tuple=0.1     ,						_A							:							Tuple="gelu"     ,						_A							:							List[Any]=False     ,						_A							:							Optional[int]=0.02     ,						_A							:							Optional[int]=1e-5     ,						_A							:							Any=32     ,						_A							:							int=None     ,						_A							:							Dict=None     ,						**_A							:							Dict     ,						):







       """simple docstring"""




       super().__init__(**_A )

       __SCREAMING_SNAKE_CASE      :	Any					=		image_size
       __SCREAMING_SNAKE_CASE      :	List[str]					=		patch_size
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		num_channels
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		embed_dim
       __SCREAMING_SNAKE_CASE      :	List[str]					=		depths
       __SCREAMING_SNAKE_CASE      :	List[str]					=		len(_A )
       __SCREAMING_SNAKE_CASE      :	str					=		num_heads
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		window_size
       __SCREAMING_SNAKE_CASE      :	Tuple					=		mlp_ratio
       __SCREAMING_SNAKE_CASE      :	Tuple					=		qkv_bias
       __SCREAMING_SNAKE_CASE      :	Tuple					=		hidden_dropout_prob
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE      :	int					=		drop_path_rate
       __SCREAMING_SNAKE_CASE      :	Any					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	Dict					=		use_absolute_embeddings
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		encoder_stride
       # 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
       __SCREAMING_SNAKE_CASE      :	Any					=		int(embed_dim * 2 ** (len(_A ) - 1) )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		['''stem'''] + [F'''stage{idx}''' for idx in range(1     ,						len(_A ) + 1 )]
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		get_aligned_output_features_output_indices(
           out_features=_A     ,						out_indices=_A     ,						stage_names=self.stage_names )






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			version.parse('''1.11'''       )



     @property
     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ] )




     @property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       return 1e-4



"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def   _A					(	_a	:						str     ,   _a	:						str				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count += 1
                  A				  =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(_a				)



def   _A					(	_a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      while True:
            A				  =		["""$"""] * len(_a				)
            A				  =		[]
            for i in range(len(_a				)				):
                  for j in range(i + 1     ,   len(_a				)				):
                        A				  =		compare_string(binary[i]     ,   binary[j]				)
                        if k is False:
                              A				  =		"""*"""
                              A				  =		"""*"""
                              temp.append("""X"""				)
            for i in range(len(_a				)				):
                  if checka[i] == "$":
                        pi.append(binary[i]				)
            if len(_a				) == 0:
                  return pi
            A				  =		list(set(_a				)				)



def   _A					(	_a	:						int     ,   _a	:						Sequence[float]				):



      """simple docstring"""






      A				  =		[]
      for minterm in minterms:
            A				  =		""""""
            for _ in range(_a				):
                  A				  =		str(minterm % 2				) + string
                  minterm //= 2
            temp.append(_a				)
      return temp



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						int				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count



def   _A					(	_a	:						list[list[int]]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      A				  =		[0] * len(_a				)
      for i in range(len(chart[0]				)				):
            A				  =		0
            A				  =		-1
            for j in range(len(_a				)				):
                  if chart[j][i] == 1:
                        count += 1
                        A				  =		j
            if count == 1:
                  A				  =		1
      for i in range(len(_a				)				):
            if select[i] == 1:
                  for j in range(len(chart[0]				)				):
                        if chart[i][j] == 1:
                              for k in range(len(_a				)				):
                                    A				  =		0
                  temp.append(prime_implicants[i]				)
      while True:
            A				  =		0
            A				  =		-1
            A				  =		0
            for i in range(len(_a				)				):
                  A				  =		chart[i].count(1				)
                  if count_n > max_n:
                        A				  =		count_n
                        A				  =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]				)

            for i in range(len(chart[0]				)				):
                  if chart[rem][i] == 1:
                        for j in range(len(_a				)				):
                              A				  =		0



def   _A					(	_a	:						list[str]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[[0 for x in range(len(_a				)				)] for x in range(len(_a				)				)]
      for i in range(len(_a				)				):
            A				  =		prime_implicants[i].count("""_"""				)
            for j in range(len(_a				)				):
                  if is_for_table(prime_implicants[i]     ,   binary[j]     ,   _a				):
                        A				  =		1

      return chart



def   _A					(	):



      """simple docstring"""






      A				  =		int(input("""Enter the no. of variables\n"""				)				)
      A				  =		[
          float(_a				)
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""				).split()
      ]
      A				  =		decimal_to_binary(_a     ,   _a				)

      A				  =		check(_a				)
      print("""Prime Implicants are:"""				)
      print(_a				)
      A				  =		prime_implicant_chart(_a     ,   _a				)

      A				  =		selection(_a     ,   _a				)
      print("""Essential Prime Implicants are:"""				)
      print(_a				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




