





'''simple docstring'''




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

_A:					int							=				{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """detr"""
    _A :			List[str]						   =       ["""past_key_values"""]
    _A :			Dict						   =       {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }



    def __init__(  self     ,			__A=True     ,			__A=None     ,			__A=3     ,			__A=100     ,			__A=6     ,			__A=2_048     ,			__A=8     ,			__A=6     ,			__A=2_048     ,			__A=8     ,			__A=0.0     ,			__A=0.0     ,			__A=True     ,			__A="relu"     ,			__A=256     ,			__A=0.1     ,			__A=0.0     ,			__A=0.0     ,			__A=0.0_2     ,			__A=1.0     ,			__A=False     ,			__A="sine"     ,			__A="resnet50"     ,			__A=True     ,			__A=False     ,			__A=1     ,			__A=5     ,			__A=2     ,			__A=1     ,			__A=1     ,			__A=5     ,			__A=2     ,			__A=0.1     ,			**__A     ,			):
     if backbone_config is not None and use_timm_backbone:
      raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'					)

     if not use_timm_backbone:
      if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
       __UpperCAmelCase					=   CONFIG_MAPPING['resnet'](out_features=['stage4']					)
      elif isinstance(__A     ,			__A					):
       __UpperCAmelCase					=   backbone_config.get('model_type'					)
       __UpperCAmelCase					=   CONFIG_MAPPING[backbone_model_type]
       __UpperCAmelCase					=   config_class.from_dict(__A					)
      # set timm attributes to None
      __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   None, None, None

     __UpperCAmelCase					=   use_timm_backbone
     __UpperCAmelCase					=   backbone_config
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   num_queries
     __UpperCAmelCase					=   d_model
     __UpperCAmelCase					=   encoder_ffn_dim
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   encoder_attention_heads
     __UpperCAmelCase					=   decoder_ffn_dim
     __UpperCAmelCase					=   decoder_layers
     __UpperCAmelCase					=   decoder_attention_heads
     __UpperCAmelCase					=   dropout
     __UpperCAmelCase					=   attention_dropout
     __UpperCAmelCase					=   activation_dropout
     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   init_std
     __UpperCAmelCase					=   init_xavier_std
     __UpperCAmelCase					=   encoder_layerdrop
     __UpperCAmelCase					=   decoder_layerdrop
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   auxiliary_loss
     __UpperCAmelCase					=   position_embedding_type
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   dilation
     # Hungarian matcher
     __UpperCAmelCase					=   class_cost
     __UpperCAmelCase					=   bbox_cost
     __UpperCAmelCase					=   giou_cost
     # Loss coefficients
     __UpperCAmelCase					=   mask_loss_coefficient
     __UpperCAmelCase					=   dice_loss_coefficient
     __UpperCAmelCase					=   bbox_loss_coefficient
     __UpperCAmelCase					=   giou_loss_coefficient
     __UpperCAmelCase					=   eos_coefficient
     super().__init__(is_encoder_decoder=__A     ,			**__A					)



    @property
    def 					__lowerCamelCase						(  self					):
     return self.encoder_attention_heads



    @property
    def 					__lowerCamelCase						(  self					):
     return self.d_model



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			**__A					):
     return cls(backbone_config=__A     ,			**__A					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   copy.deepcopy(self.__dict__					)
     if output["backbone_config"] is not None:
      __UpperCAmelCase					=   self.backbone_config.to_dict()
     __UpperCAmelCase					=   self.__class__.model_type
     return output






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Union[str, Any]						   =       version.parse("""1.11"""							)



    @property
    def 					__lowerCamelCase						(  self					):
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ('pixel_mask', {0: 'batch'}),
         ]					)



    @property
    def 					__lowerCamelCase						(  self					):
     return 1E-5




    @property
    def 					__lowerCamelCase						(  self					):
     return 12












'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   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(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





