


"""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


_snake_case        =				logging.get_logger(__name__)

_snake_case        =				{
    "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 	_a   (   SCREAMING_SNAKE_CASE_			):
    a_     :					int   							=     'detr'
    a_     :					List[str]   							=     ['past_key_values']
    a_     :					Tuple   							=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(			self				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       List[str]=True				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=None				,       SCREAMING_SNAKE_CASE__				:       Tuple=3				,       SCREAMING_SNAKE_CASE__				:       str=1_00				,       SCREAMING_SNAKE_CASE__				:       Any=6				,       SCREAMING_SNAKE_CASE__				:       List[str]=20_48				,       SCREAMING_SNAKE_CASE__				:       List[str]=8				,       SCREAMING_SNAKE_CASE__				:       Tuple=6				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=20_48				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=8				,       SCREAMING_SNAKE_CASE__				:       List[Any]=0.0				,       SCREAMING_SNAKE_CASE__				:       List[str]=0.0				,       SCREAMING_SNAKE_CASE__				:       Any=True				,       SCREAMING_SNAKE_CASE__				:       Optional[int]="relu"				,       SCREAMING_SNAKE_CASE__				:       Dict=2_56				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.1				,       SCREAMING_SNAKE_CASE__				:       int=0.0				,       SCREAMING_SNAKE_CASE__				:       Dict=0.0				,       SCREAMING_SNAKE_CASE__				:       Tuple=0.02				,       SCREAMING_SNAKE_CASE__				:       List[str]=1.0				,       SCREAMING_SNAKE_CASE__				:       List[str]=False				,       SCREAMING_SNAKE_CASE__				:       Dict="sine"				,       SCREAMING_SNAKE_CASE__				:       Any="resnet50"				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=True				,       SCREAMING_SNAKE_CASE__				:       List[Any]=False				,       SCREAMING_SNAKE_CASE__				:       Any=1				,       SCREAMING_SNAKE_CASE__				:       List[str]=5				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=2				,       SCREAMING_SNAKE_CASE__				:       str=1				,       SCREAMING_SNAKE_CASE__				:       str=1				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=5				,       SCREAMING_SNAKE_CASE__				:       Any=2				,       SCREAMING_SNAKE_CASE__				:       Tuple=0.1				,       **SCREAMING_SNAKE_CASE__				:       Union[str, 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.'					)
                      lowerCamelCase__							     =		CONFIG_MAPPING['resnet'](out_features=['stage4']					)
                elif isinstance(_a				,       _a					):
                      lowerCamelCase__							     =		backbone_config.get('model_type'					)
                      lowerCamelCase__							     =		CONFIG_MAPPING[backbone_model_type]
                      lowerCamelCase__							     =		config_class.from_dict(_a					)
                # set timm attributes to None
                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=_a				,       **_a					)
    @property
    def   _UpperCamelCase							(			self				:       List[Any]					):
          return self.encoder_attention_heads
    @property
    def   _UpperCamelCase							(			self				:       List[str]					):
          return self.d_model
    @classmethod
    def   _UpperCamelCase							(			cls				:       str				,       SCREAMING_SNAKE_CASE__				:       PretrainedConfig				,       **SCREAMING_SNAKE_CASE__				:       List[str]					):
          return cls(backbone_config=_a				,       **_a					)


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





class 	_a   (   SCREAMING_SNAKE_CASE_			):
    a_     :					Dict   							=     version.parse('1.11'			)
    @property
    def   _UpperCamelCase							(			self				:       List[Any]					):
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                  ('pixel_mask', {0: 'batch'}),
              ]					)
    @property
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          return 1e-5


    @property
    def   _UpperCamelCase							(			self				:       int					):
          return 12








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






