





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       :   Dict				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   str				       =							{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """conditional_detr"""
       lowerCAmelCase__      		=     ["""past_key_values"""]
       lowerCAmelCase__      		=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }




       def __init__(  self :    Dict							,				A :    Optional[Any]=True							,				A :    Tuple=None							,				A :    Tuple=3							,				A :    int=300							,				A :    Union[str, Any]=6							,				A :    Union[str, Any]=2_048							,				A :    Optional[int]=8							,				A :    Any=6							,				A :    List[Any]=2_048							,				A :    Tuple=8							,				A :    List[str]=0.0							,				A :    int=0.0							,				A :    Any=True							,				A :    Any="relu"							,				A :    Tuple=256							,				A :    Union[str, Any]=0.1							,				A :    Optional[Any]=0.0							,				A :    str=0.0							,				A :    Dict=0.02							,				A :    str=1.0							,				A :    Union[str, Any]=False							,				A :    Tuple="sine"							,				A :    List[str]="resnet50"							,				A :    Dict=True							,				A :    Optional[int]=False							,				A :    Dict=2							,				A :    int=5							,				A :    int=2							,				A :    Dict=1							,				A :    List[str]=1							,				A :    Optional[Any]=2							,				A :    Optional[int]=5							,				A :    List[Any]=2							,				A :    Union[str, Any]=0.25							,				**A :    List[str]							,				):
         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:						Tuple   =						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
           elif isinstance(A							,				A		):
             __snake_case:						Optional[Any]   =						backbone_config.get("""model_type"""		)
             __snake_case:						List[str]   =						CONFIG_MAPPING[backbone_model_type]
             __snake_case:						str   =						config_class.from_dict(A		)

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




       @property
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         return self.encoder_attention_heads




       @property
       def 							UpperCAmelCase__ (  self :    Dict		):
         return self.d_model






       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Optional[Any]   =						copy.deepcopy(self.__dict__		)
         if self.backbone_config is not None:
           __snake_case:						Tuple   =						self.backbone_config.to_dict()
         __snake_case:						List[Any]   =						self.__class__.model_type
         return output







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     version.parse("""1.11"""			)




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




       @property
       def 							UpperCAmelCase__ (  self :    List[str]		):
         return 1E-5






       @property
       def 							UpperCAmelCase__ (  self :    Dict		):
         return 12













from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






