





'''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


__magic_name__   :						Tuple    =			logging.get_logger(__name__)

__magic_name__   :						List[Any]    =			{
    '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       __SCREAMING_SNAKE_CASE	(						SCREAMING_SNAKE_CASE_			):



     '''simple docstring'''






     UpperCAmelCase__       :						Dict      =   '''detr'''
     UpperCAmelCase__       :						Tuple      =   ['''past_key_values''']
     UpperCAmelCase__       :						List[Any]      =   {
         '''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=0.02						,   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						,   ):
       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."	)
           _snake_case     		=				CONFIG_MAPPING['''resnet'''](out_features=["stage4"]	)
         elif isinstance(UpperCamelCase__						,   UpperCamelCase__	):
           _snake_case     		=				backbone_config.get("model_type"	)
           _snake_case     		=				CONFIG_MAPPING[backbone_model_type]
           _snake_case     		=				config_class.from_dict(UpperCamelCase__	)
         # set timm attributes to None
         _snake_case     		=				None, None, None

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

     @property
     def 	UpperCamelCase(					self	):
       return self.encoder_attention_heads

     @property
     def 	UpperCamelCase(					self	):
       return self.d_model

     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   **lowerCamelCase	):
       return cls(backbone_config=UpperCamelCase__						,   **UpperCamelCase__	)






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







class       __SCREAMING_SNAKE_CASE	(						SCREAMING_SNAKE_CASE_			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ("pixel_mask", {0: "batch"}),
           ]	)

     @property
     def 	UpperCamelCase(					self	):
       return 1e-5






     @property
     def 	UpperCamelCase(					self	):
       return 12









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__   :						Any    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   '''bit'''
     UpperCAmelCase__       :						Union[str, Any]      =   ['''preactivation''', '''bottleneck''']
     UpperCAmelCase__       :						int      =   ['''SAME''', '''VALID''']

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=64						,   lowerCamelCase=[256, 512, 1_024, 2_048]						,   lowerCamelCase=[3, 4, 6, 3]						,   lowerCamelCase="preactivation"						,   lowerCamelCase="relu"						,   lowerCamelCase=None						,   lowerCamelCase=32						,   lowerCamelCase=0.0						,   lowerCamelCase=False						,   lowerCamelCase=32						,   lowerCamelCase=1						,   lowerCamelCase=None						,   lowerCamelCase=None						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       if layer_type not in self.layer_types:
         raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types	)}'''	)
       if global_padding is not None:
         if global_padding.upper() in self.supported_padding:
           _snake_case     		=				global_padding.upper()
         else:
           raise ValueError(F'''Padding strategy {global_padding} not supported'''	)
       _snake_case     		=				num_channels
       _snake_case     		=				embedding_size
       _snake_case     		=				hidden_sizes
       _snake_case     		=				depths
       _snake_case     		=				layer_type
       _snake_case     		=				hidden_act
       _snake_case     		=				global_padding
       _snake_case     		=				num_groups
       _snake_case     		=				drop_path_rate
       _snake_case     		=				embedding_dynamic_padding
       _snake_case     		=				output_stride
       _snake_case     		=				width_factor

       _snake_case     		=				["stem"] + [F'''stage{idx}''' for idx in range(1						,   len(lowerCamelCase	) + 1	)]
       _snake_case		,		_snake_case     		=				get_aligned_output_features_output_indices(
           out_features=lowerCamelCase						,   out_indices=lowerCamelCase						,   stage_names=self.stage_names	)


