




'''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_      :		int							      =       logging.get_logger(__name__)

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


class   __UpperCamelCase       ( lowerCamelCase__				):
       lowercase	:    int      						='table-transformer'
       lowercase	:    Union[str, Any]      						=['past_key_values']
       lowercase	:    str      						={
           'hidden_size': 'd_model',
           'num_attention_heads': 'encoder_attention_heads',
       }






       def __init__(							self,		lowerCAmelCase=True,		lowerCAmelCase=None,		lowerCAmelCase=3,		lowerCAmelCase=100,		lowerCAmelCase=6,		lowerCAmelCase=2_048,		lowerCAmelCase=8,		lowerCAmelCase=6,		lowerCAmelCase=2_048,		lowerCAmelCase=8,		lowerCAmelCase=0.0,		lowerCAmelCase=0.0,		lowerCAmelCase=True,		lowerCAmelCase="relu",		lowerCAmelCase=256,		lowerCAmelCase=0.1,		lowerCAmelCase=0.0,		lowerCAmelCase=0.0,		lowerCAmelCase=0.0_2,		lowerCAmelCase=1.0,		lowerCAmelCase=False,		lowerCAmelCase="sine",		lowerCAmelCase="resnet50",		lowerCAmelCase=True,		lowerCAmelCase=False,		lowerCAmelCase=1,		lowerCAmelCase=5,		lowerCAmelCase=2,		lowerCAmelCase=1,		lowerCAmelCase=1,		lowerCAmelCase=5,		lowerCAmelCase=2,		lowerCAmelCase=0.1,		**lowerCAmelCase,		):



              """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_							=CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']  )
                     elif isinstance(lowerCAmelCase,		lowerCAmelCase  ):
                            lowerCamelCase_							=backbone_config.get('''model_type'''  )
                            lowerCamelCase_							=CONFIG_MAPPING[backbone_model_type]
                            lowerCamelCase_							=config_class.from_dict(lowerCAmelCase  )
                     # set timm attributes to None
                     lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=None, None, None

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






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return self.encoder_attention_heads






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return self.d_model





class   __UpperCamelCase       ( lowerCamelCase__				):
       lowercase	:    Tuple      						=version.parse('1.11'				)






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







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






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return 1e-5






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return 12












"""simple docstring"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

