




"""simple docstring"""


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 ..auto import CONFIG_MAPPING


_A													=      logging.get_logger(__name__)

_A													=      {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class _lowerCamelCase    (					_snake_case						):
   _lowerCamelCase    :Any     				=     "table-transformer"
   _lowerCamelCase    :List[str]     				=     ["past_key_values"]
   _lowerCamelCase    :Any     				=     {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		List[str]=None   ,     UpperCamelCase       :		Dict=3   ,     UpperCamelCase       :		Dict=1_00   ,     UpperCamelCase       :		Optional[int]=6   ,     UpperCamelCase       :		List[str]=20_48   ,     UpperCamelCase       :		Optional[Any]=8   ,     UpperCamelCase       :		Any=6   ,     UpperCamelCase       :		Dict=20_48   ,     UpperCamelCase       :		List[Any]=8   ,     UpperCamelCase       :		int=0.0   ,     UpperCamelCase       :		List[Any]=0.0   ,     UpperCamelCase       :		str=True   ,     UpperCamelCase       :		Tuple="relu"   ,     UpperCamelCase       :		Optional[Any]=2_56   ,     UpperCamelCase       :		Any=0.1   ,     UpperCamelCase       :		Optional[int]=0.0   ,     UpperCamelCase       :		Union[str, Any]=0.0   ,     UpperCamelCase       :		Dict=0.02   ,     UpperCamelCase       :		Dict=1.0   ,     UpperCamelCase       :		Optional[int]=False   ,     UpperCamelCase       :		Optional[int]="sine"   ,     UpperCamelCase       :		int="resnet50"   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		Optional[int]=False   ,     UpperCamelCase       :		Optional[int]=1   ,     UpperCamelCase       :		Dict=5   ,     UpperCamelCase       :		Dict=2   ,     UpperCamelCase       :		List[str]=1   ,     UpperCamelCase       :		Union[str, Any]=1   ,     UpperCamelCase       :		Optional[Any]=5   ,     UpperCamelCase       :		Tuple=2   ,     UpperCamelCase       :		List[str]=0.1   ,     **UpperCamelCase       :		Union[str, Any]   ,     )			->						str:

         """simple docstring"""
         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."""   )
                     lowerCAmelCase__      :			Tuple   =	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
               elif isinstance(UpperCamelCase__   ,     UpperCamelCase__   ):
                     lowerCAmelCase__      :			str   =	backbone_config.get("""model_type"""   )
                     lowerCAmelCase__      :			str   =	CONFIG_MAPPING[backbone_model_type]
                     lowerCAmelCase__      :			str   =	config_class.from_dict(UpperCamelCase__   )
               # set timm attributes to None
               lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Any   =	None, None, None

         lowerCAmelCase__      :			str   =	use_timm_backbone
         lowerCAmelCase__      :			Dict   =	backbone_config
         lowerCAmelCase__      :			Union[str, Any]   =	num_channels
         lowerCAmelCase__      :			int   =	num_queries
         lowerCAmelCase__      :			str   =	d_model
         lowerCAmelCase__      :			Any   =	encoder_ffn_dim
         lowerCAmelCase__      :			Any   =	encoder_layers
         lowerCAmelCase__      :			Union[str, Any]   =	encoder_attention_heads
         lowerCAmelCase__      :			List[str]   =	decoder_ffn_dim
         lowerCAmelCase__      :			int   =	decoder_layers
         lowerCAmelCase__      :			int   =	decoder_attention_heads
         lowerCAmelCase__      :			int   =	dropout
         lowerCAmelCase__      :			Any   =	attention_dropout
         lowerCAmelCase__      :			Union[str, Any]   =	activation_dropout
         lowerCAmelCase__      :			Union[str, Any]   =	activation_function
         lowerCAmelCase__      :			List[str]   =	init_std
         lowerCAmelCase__      :			List[str]   =	init_xavier_std
         lowerCAmelCase__      :			Optional[Any]   =	encoder_layerdrop
         lowerCAmelCase__      :			int   =	decoder_layerdrop
         lowerCAmelCase__      :			Tuple   =	encoder_layers
         lowerCAmelCase__      :			str   =	auxiliary_loss
         lowerCAmelCase__      :			Optional[int]   =	position_embedding_type
         lowerCAmelCase__      :			str   =	backbone
         lowerCAmelCase__      :			int   =	use_pretrained_backbone
         lowerCAmelCase__      :			Union[str, Any]   =	dilation
         # Hungarian matcher
         lowerCAmelCase__      :			List[Any]   =	class_cost
         lowerCAmelCase__      :			Optional[int]   =	bbox_cost
         lowerCAmelCase__      :			List[Any]   =	giou_cost
         # Loss coefficients
         lowerCAmelCase__      :			str   =	mask_loss_coefficient
         lowerCAmelCase__      :			int   =	dice_loss_coefficient
         lowerCAmelCase__      :			Any   =	bbox_loss_coefficient
         lowerCAmelCase__      :			int   =	giou_loss_coefficient
         lowerCAmelCase__      :			Tuple   =	eos_coefficient
         super().__init__(is_encoder_decoder=UpperCamelCase__   ,     **UpperCamelCase__   )






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						int:

         """simple docstring"""
         return self.encoder_attention_heads






   @property
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						int:

         """simple docstring"""
         return self.d_model






class _lowerCamelCase    (					_snake_case						):
   _lowerCamelCase    :Optional[int]     				=     version.parse("1.11"						)






   @property
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Mapping[str, Mapping[int, str]]:

         """simple docstring"""
         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ("""pixel_mask""", {0: """batch"""}),
             ]   )






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						float:

         """simple docstring"""
         return 1E-5






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						int:

         """simple docstring"""
         return 12











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

    # print entropy
      print(f"""{round(-1 * my_fir_sum				):.1f}"""				)

      # two len string
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	int(__UpperCAmelCase				) / all_sum
                        my_sec_sum += prob * math.loga(__UpperCAmelCase				)

    # print second entropy
      print(f"""{round(-1 * my_sec_sum				):.1f}"""				)

      # print the difference between them
      print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}"""				)



def 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,		len(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
  main()





