





import copy
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


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[Any]       =	"""conditional_detr"""
		__a       :       Optional[Any]       =	["""past_key_values"""]
		__a       :       int       =	{
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """encoder_attention_heads""",
		}






		def __init__(							self,   snake_case__=True,   snake_case__=None,   snake_case__=3,   snake_case__=3_00,   snake_case__=6,   snake_case__=20_48,   snake_case__=8,   snake_case__=6,   snake_case__=20_48,   snake_case__=8,   snake_case__=0.0,   snake_case__=0.0,   snake_case__=True,   snake_case__="relu",   snake_case__=2_56,   snake_case__=0.1,   snake_case__=0.0,   snake_case__=0.0,   snake_case__=0.02,   snake_case__=1.0,   snake_case__=False,   snake_case__="sine",   snake_case__="resnet50",   snake_case__=True,   snake_case__=False,   snake_case__=2,   snake_case__=5,   snake_case__=2,   snake_case__=1,   snake_case__=1,   snake_case__=2,   snake_case__=5,   snake_case__=2,   snake_case__=0.25,   **snake_case__,   )       -> List[Any]:






									"""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."""  )
																							lowercase_	:   Optional[Any]  				=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
																elif isinstance(snake_case__,   snake_case__  ):
																							lowercase_	:   Union[str, Any]  				=     backbone_config.get("""model_type"""  )
																							lowercase_	:   Tuple  				=     CONFIG_MAPPING[backbone_model_type]
																							lowercase_	:   int  				=     config_class.from_dict(snake_case__  )

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






		@property
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									return self.encoder_attention_heads






		@property
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									return self.d_model






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     copy.deepcopy(self.__dict__  )
									if self.backbone_config is not None:
																lowercase_	:   int  				=     self.backbone_config.to_dict()
									lowercase_	:   int  				=     self.__class__.model_type
									return output




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str       =	version.parse("""1.11"""				)






		@property
		def 						snake_case__				(							self  )       -> Mapping[str, Mapping[int, str]]:






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






		@property
		def 						snake_case__				(							self  )       -> float:






									"""simple docstring"""
									return 1E-5






		@property
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									return 12






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask