



'''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


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''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 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Dict  =      "detr"
 __magic_name__					: Dict  =      ["past_key_values"]
 __magic_name__					: int  =      {
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }




 def __init__(   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=None							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_0_0							,  lowerCAmelCase_=6							,  lowerCAmelCase_=2_0_4_8							,  lowerCAmelCase_=8							,  lowerCAmelCase_=6							,  lowerCAmelCase_=2_0_4_8							,  lowerCAmelCase_=8							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=True							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=2_5_6							,  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_							,  ) ->      Optional[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.")
                a_       		=CONFIG_MAPPING["resnet"](out_features=["stage4"])
           elif isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
                a_       		=backbone_config.get("model_type")
                a_       		=CONFIG_MAPPING[backbone_model_type]
                a_       		=config_class.from_dict(lowerCAmelCase_)
           # set timm attributes to None
           a_		,						a_		,						a_       		=None, None, None

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




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return self.encoder_attention_heads




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return self.d_model




 @classmethod
 def       lowercase_  (   cls							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      return cls(backbone_config=lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self) ->      Dict[str, any]:



      """simple docstring"""







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







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      version.parse("1.11")




 @property
 def       lowercase_  (   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       lowercase_  (   self) ->      float:



      """simple docstring"""







      return 1e-5




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return 1_2







'''simple docstring'''


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class 	UpperCAmelCase	:


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      return self.get_dummy_input()




 @property
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      if self.block_type == "down":
           return (4, 3_2, 1_6, 1_6)
      elif self.block_type == "mid":
           return (4, 3_2, 3_2, 3_2)
      elif self.block_type == "up":
           return (4, 3_2, 6_4, 6_4)

      raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")




 def       lowercase_  (   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  ) ->      Dict:



      """simple docstring"""







      a_       		=4
      a_       		=3_2
      a_       		=(3_2, 3_2)

      a_       		=torch.manual_seed(0)
      a_       		=torch.device(lowerCAmelCase_)
      a_       		=(batch_size, num_channels) + sizes
      a_       		=randn_tensor(lowerCAmelCase_							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)
      a_       		={"hidden_states": hidden_states}

      if include_temb:
           a_       		=1_2_8
           a_       		=randn_tensor((batch_size, temb_channels)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      if include_res_hidden_states_tuple:
           a_       		=torch.manual_seed(1)
           a_       		=(randn_tensor(lowerCAmelCase_							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_),)

      if include_encoder_hidden_states:
           a_       		=floats_tensor((batch_size, 3_2, 3_2)).to(lowerCAmelCase_)

      if include_skip_sample:
           a_       		=randn_tensor(((batch_size, 3) + sizes)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      return dummy_input




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		={
          "in_channels": 3_2,
          "out_channels": 3_2,
          "temb_channels": 1_2_8,
      }
      if self.block_type == "up":
           a_       		=3_2

      if self.block_type == "mid":
           init_dict.pop("out_channels")

      a_       		=self.dummy_input
      return init_dict, inputs_dict




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      unet_block.to(lowerCAmelCase_)
      unet_block.eval()

      with torch.no_grad():
           a_       		=unet_block(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

      self.assertEqual(output.shape							,  self.output_shape)

      a_       		=output[0, -1, -3:, -3:]
      a_       		=torch.tensor(lowerCAmelCase_).to(lowerCAmelCase_)
      assert torch_all_close(output_slice.flatten()							,  lowerCAmelCase_							,  atol=5e-3)




 @unittest.skipIf(torch_device == "mps"							,  "Training is not supported in mps")
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.train()
      a_       		=model(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

      a_       		=torch.device(lowerCAmelCase_)
      a_       		=randn_tensor(output.shape							,  device=lowerCAmelCase_)
      a_       		=torch.nn.functional.mse_loss(lowerCAmelCase_							,  lowerCAmelCase_)
      loss.backward()


