





"""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


lowerCamelCase__				  =   logging.get_logger(__name__)

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



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					"table-transformer"
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					["past_key_values"]
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					{
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
  }

  def __init__(		self   :       Optional[int]					,   __a   :       Dict=True					,   __a   :       int=None					,   __a   :       Optional[Any]=3					,   __a   :       Any=100					,   __a   :       List[str]=6					,   __a   :       Optional[int]=2048					,   __a   :       List[Any]=8					,   __a   :       str=6					,   __a   :       Union[str, Any]=2048					,   __a   :       Tuple=8					,   __a   :       Dict=0.0					,   __a   :       Any=0.0					,   __a   :       int=True					,   __a   :       int="relu"					,   __a   :       Tuple=256					,   __a   :       List[Any]=0.1					,   __a   :       Optional[int]=0.0					,   __a   :       Union[str, Any]=0.0					,   __a   :       List[str]=0.02					,   __a   :       Dict=1.0					,   __a   :       Union[str, Any]=False					,   __a   :       Optional[Any]="sine"					,   __a   :       List[Any]="resnet50"					,   __a   :       List[Any]=True					,   __a   :       List[str]=False					,   __a   :       Union[str, Any]=1					,   __a   :       str=5					,   __a   :       str=2					,   __a   :       Optional[Any]=1					,   __a   :       Tuple=1					,   __a   :       Dict=5					,   __a   :       Tuple=2					,   __a   :       Any=0.1					,   **__a   :       str					,   )		->				List[str]:
        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      :	Dict						=       CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
              elif isinstance(__a					,   __a	):
                    _UpperCamelCase      :	List[Any]						=       backbone_config.get("model_type"	)
                    _UpperCamelCase      :	int						=       CONFIG_MAPPING[backbone_model_type]
                    _UpperCamelCase      :	str						=       config_class.from_dict(__a	)
              # set timm attributes to None
              _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       None, None, None

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

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				int:
        return self.encoder_attention_heads


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				int:
        return self.d_model





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Dict	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                ("pixel_mask", {0: "batch"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				float:
        return 1e-5


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				int:
        return 12







"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__				  =   (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__				  =   [ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__				  =   {ord(char) for char in VALID_CHARS}

lowerCamelCase__				  =   ["the", "be", "to", "of", "and", "in", "that", "have"]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str | None:




      """simple docstring"""

      _UpperCamelCase      :	str						=       ""
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int

      for keychar, cipherchar in zip(cycle(lowercase_			)		,lowercase_			):
            _UpperCamelCase      :	Dict						=       cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(lowercase_			)

      return decoded




def 					lowercase__    (					lowercase_			)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	list[str]						=       []
      for key in product(lowercase_		,repeat=3			):
            _UpperCamelCase      :	int						=       try_key(lowercase_		,lowercase_			)
            if encoded is not None:
                  possibles.append(lowercase_			)
      return possibles




def 					lowercase__    (					lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

      return [possible for possible in possibles if common_word in possible.lower()]




def 					lowercase__    (					lowercase_ = "p059_cipher.txt"			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	list[int]
      _UpperCamelCase      :	list[str]
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str						=       Path(lowercase_			).parent.joinpath(lowercase_			).read_text(encoding="utf-8"			)

      _UpperCamelCase      :	Optional[Any]						=       [int(lowercase_			) for number in data.strip().split(","			)]

      _UpperCamelCase      :	List[str]						=       filter_valid_chars(lowercase_			)
      for common_word in COMMON_WORDS:
            _UpperCamelCase      :	Union[str, Any]						=       filter_common_word(lowercase_		,lowercase_			)
            if len(lowercase_			) == 1:
                  break

      _UpperCamelCase      :	Union[str, Any]						=       possibles[0]
      return sum(ord(lowercase_			) for char in decoded_text			)


if __name__ == "__main__":
  print(f"""{solution() = }""")
