
"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   _lowercase						(       __lowercase				):
     _A								=							['''image_processor''', '''tokenizer''']
     _A								=							'''AutoImageProcessor'''
     _A								=							'''AutoTokenizer'''



     def __init__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					Tuple:
          UpperCAmelCase__							: int 							=						None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead." ,			__a ,			)
               UpperCAmelCase__							: str 							=						kwargs.pop("feature_extractor"  )

          UpperCAmelCase__							: List[Any] 							=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."  )
          if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."  )

          super().__init__(__a ,			__a  )
          UpperCAmelCase__							: Any 							=						self.image_processor
          UpperCAmelCase__							: Any 							=						False



     def __call__(      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[Any]:
          # For backward compatibility
          if self._in_target_context_manager:
               return self.current_processor(*__a ,			**__a  )

          UpperCAmelCase__							: List[Any] 							=						kwargs.pop("images" ,			__a  )
          UpperCAmelCase__							: List[str] 							=						kwargs.pop("text" ,			__a  )
          if len(__a  ) > 0:
               UpperCAmelCase__							: Any 							=						args[0]
               UpperCAmelCase__							: List[Any] 							=						args[1:]

          if images is None and text is None:
               raise ValueError("You need to specify either an `images` or `text` input to process."  )

          if images is not None:
               UpperCAmelCase__							: str 							=						self.image_processor(__a ,			*__a ,			**__a  )
          if text is not None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.tokenizer(__a ,			**__a  )

          if text is None:
               return inputs
          elif images is None:
               return encodings
          else:
               UpperCAmelCase__							: List[Any] 							=						encodings["""input_ids"""]
               return inputs



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Union[str, Any]:
          return self.tokenizer.batch_decode(*__a ,			**__a  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Union[str, Any]:
          return self.tokenizer.decode(*__a ,			**__a  )



     @contextmanager
     def  lowerCAmelCase__       (      self  )->					int:
          warnings.warn(
              "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
              "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
              "your images inputs, or in a separate call."  )
          UpperCAmelCase__							: List[str] 							=						True
          UpperCAmelCase__							: int 							=						self.tokenizer
          yield
          UpperCAmelCase__							: str 							=						self.image_processor
          UpperCAmelCase__							: Union[str, Any] 							=						False



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			__UpperCamelCase=None  )->					Union[str, Any]:
          if added_vocab is None:
               UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer.get_added_vocab()

          UpperCAmelCase__							: Dict 							=						{}

          while tokens:
               UpperCAmelCase__							: Union[str, Any] 							=						re.search(r"<s_(.*?)>" ,			__a ,			re.IGNORECASE  )
               if start_token is None:
                    break
               UpperCAmelCase__							: List[Any] 							=						start_token.group(1  )
               UpperCAmelCase__							: Optional[int] 							=						re.search(rF"</s_{key}>" ,			__a ,			re.IGNORECASE  )
               UpperCAmelCase__							: int 							=						start_token.group()
               if end_token is None:
                    UpperCAmelCase__							: Any 							=						tokens.replace(__a ,			""  )
               else:
                    UpperCAmelCase__							: Any 							=						end_token.group()
                    UpperCAmelCase__							: Tuple 							=						re.escape(__a  )
                    UpperCAmelCase__							: Optional[Any] 							=						re.escape(__a  )
                    UpperCAmelCase__							: Dict 							=						re.search(F"{start_token_escaped}(.*?){end_token_escaped}" ,			__a ,			re.IGNORECASE  )
                    if content is not None:
                         UpperCAmelCase__							: List[str] 							=						content.group(1  ).strip()
                         if r"<s_" in content and r"</s_" in content:  # non-leaf node
                              UpperCAmelCase__							: Any 							=						self.tokenajson(__a ,			is_inner_value=__a ,			added_vocab=__a  )
                              if value:
                                   if len(__a  ) == 1:
                                        UpperCAmelCase__							: str 							=						value[0]
                                   UpperCAmelCase__							: List[Any] 							=						value
                         else:  # leaf nodes
                              UpperCAmelCase__							: List[Any] 							=						[]
                              for leaf in content.split(r"<sep/>"  ):
                                   UpperCAmelCase__							: Optional[Any] 							=						leaf.strip()
                                   if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                        UpperCAmelCase__							: Optional[Any] 							=						leaf[1:-2]  # for categorical special tokens
                                   output[key].append(__a  )
                              if len(output[key]  ) == 1:
                                   UpperCAmelCase__							: Optional[Any] 							=						output[key][0]

                    UpperCAmelCase__							: Tuple 							=						tokens[tokens.find(__a  ) + len(__a  ) :].strip()
                    if tokens[:6] == r"<sep/>":  # non-leaf nodes
                         return [output] + self.tokenajson(tokens[6:] ,			is_inner_value=__a ,			added_vocab=__a  )

          if len(__a  ):
               return [output] if is_inner_value else output
          else:
               return [] if is_inner_value else {"text_sequence": tokens}



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,			__a ,			)
          return self.image_processor_class



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,			__a ,			)
          return self.image_processor





"""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__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	Optional[int]          =    {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'table-transformer'
     _A								=							['past_key_values']
     _A								=							{
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }



     def __init__(      self ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=3 ,			__UpperCamelCase=1_00 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=True ,			__UpperCamelCase="relu" ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=False ,			__UpperCamelCase="sine" ,			__UpperCamelCase="resnet50" ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=0.1 ,			**__UpperCamelCase ,			)->					List[Any]:
          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__							: Any 							=						CONFIG_MAPPING["resnet"](out_features=["stage4"]  )
               elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: int 							=						backbone_config.get("model_type"  )
                    UpperCAmelCase__							: Optional[Any] 							=						CONFIG_MAPPING[backbone_model_type]
                    UpperCAmelCase__							: int 							=						config_class.from_dict(__UpperCamelCase  )
               # set timm attributes to None
               UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						None, None, None

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



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



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







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



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



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-5



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



