

'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Dict       =	{
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "deformable_detr"
 lowerCAmelCase__       = {
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=3   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=300   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1_024   ,				__SCREAMING_SNAKE_CASE   :			Tuple=6   ,				__SCREAMING_SNAKE_CASE   :			Dict=1_024   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=6   ,				__SCREAMING_SNAKE_CASE   :			int=1_024   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=8   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=True   ,				__SCREAMING_SNAKE_CASE   :			Tuple="relu"   ,				__SCREAMING_SNAKE_CASE   :			Any=256   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1.0   ,				__SCREAMING_SNAKE_CASE   :			Dict=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			Tuple="sine"   ,				__SCREAMING_SNAKE_CASE   :			Dict="resnet50"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=True   ,				__SCREAMING_SNAKE_CASE   :			Tuple=False   ,				__SCREAMING_SNAKE_CASE   :			Any=4   ,				__SCREAMING_SNAKE_CASE   :			int=4   ,				__SCREAMING_SNAKE_CASE   :			int=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			Dict=300   ,				__SCREAMING_SNAKE_CASE   :			Tuple=False   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Tuple=5   ,				__SCREAMING_SNAKE_CASE   :			int=2   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			str=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=5   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=0.1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.25   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				**__SCREAMING_SNAKE_CASE   :			str   ,				)    ->					Union[str, 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."""     )
                __SCREAMING_SNAKE_CASE      = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]     )
           elif isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                __SCREAMING_SNAKE_CASE      = backbone_config.get("""model_type"""     )
                __SCREAMING_SNAKE_CASE      = CONFIG_MAPPING[backbone_model_type]
                __SCREAMING_SNAKE_CASE      = config_class.from_dict(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = use_timm_backbone
      __SCREAMING_SNAKE_CASE      = backbone_config
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = num_queries
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = d_model
      __SCREAMING_SNAKE_CASE      = encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = encoder_attention_heads
      __SCREAMING_SNAKE_CASE      = decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = decoder_layers
      __SCREAMING_SNAKE_CASE      = decoder_attention_heads
      __SCREAMING_SNAKE_CASE      = dropout
      __SCREAMING_SNAKE_CASE      = attention_dropout
      __SCREAMING_SNAKE_CASE      = activation_dropout
      __SCREAMING_SNAKE_CASE      = activation_function
      __SCREAMING_SNAKE_CASE      = init_std
      __SCREAMING_SNAKE_CASE      = init_xavier_std
      __SCREAMING_SNAKE_CASE      = encoder_layerdrop
      __SCREAMING_SNAKE_CASE      = auxiliary_loss
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = backbone
      __SCREAMING_SNAKE_CASE      = use_pretrained_backbone
      __SCREAMING_SNAKE_CASE      = dilation
      # deformable attributes
      __SCREAMING_SNAKE_CASE      = num_feature_levels
      __SCREAMING_SNAKE_CASE      = encoder_n_points
      __SCREAMING_SNAKE_CASE      = decoder_n_points
      __SCREAMING_SNAKE_CASE      = two_stage
      __SCREAMING_SNAKE_CASE      = two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      = with_box_refine
      if two_stage is True and with_box_refine is False:
           raise ValueError("""If two_stage is True, with_box_refine must be True."""     )
      # Hungarian matcher
      __SCREAMING_SNAKE_CASE      = class_cost
      __SCREAMING_SNAKE_CASE      = bbox_cost
      __SCREAMING_SNAKE_CASE      = giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      = mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      = dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      = bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      = giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      = eos_coefficient
      __SCREAMING_SNAKE_CASE      = focal_alpha
      __SCREAMING_SNAKE_CASE      = disable_custom_kernels
      super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					int:


      """simple docstring"""




      return self.encoder_attention_heads
 @property
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					int:


      """simple docstring"""




      return self.d_model


 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = copy.deepcopy(self.__dict__     )
      if self.backbone_config is not None:
           __SCREAMING_SNAKE_CASE      = self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      = self.__class__.model_type
      return output









'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






