





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __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    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.dummy_uncond_unet
         __snake_case							       =   PNDMScheduler()

         __snake_case							       =   PNDMPipeline(unet=a__				,			scheduler=a__      )
         pndm.to(a__      )
         pndm.set_progress_bar_config(disable=a__      )

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pndm(generator=a__				,			num_inference_steps=20				,			output_type='''numpy'''      ).images

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pndm(generator=a__				,			num_inference_steps=20				,			output_type='''numpy'''				,			return_dict=a__      )[0]

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

         assert image.shape == (1, 32, 32, 3)
         __snake_case							       =   np.array([1.0, 1.0, 0.0, 1.0, 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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''google/ddpm-cifar10-32'''

         __snake_case							       =   UNetaDModel.from_pretrained(a__      )
         __snake_case							       =   PNDMScheduler()

         __snake_case							       =   PNDMPipeline(unet=a__				,			scheduler=a__      )
         pndm.to(a__      )
         pndm.set_progress_bar_config(disable=a__      )
         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pndm(generator=a__				,			output_type='''numpy'''      ).images

         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __snake_case							       =   np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]      )

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




"""simple docstring"""


import copy

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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  """deformable_detr"""
				SCREAMING_SNAKE_CASE_   :      int         =  {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}

				def __init__(	self							:				int     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				int=3     ,  UpperCamelCase__							:				Dict=3_0_0     ,  UpperCamelCase__							:				Optional[int]=1_0_2_4     ,  UpperCamelCase__							:				int=6     ,  UpperCamelCase__							:				List[Any]=1_0_2_4     ,  UpperCamelCase__							:				List[Any]=8     ,  UpperCamelCase__							:				str=6     ,  UpperCamelCase__							:				str=1_0_2_4     ,  UpperCamelCase__							:				Union[str, Any]=8     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Tuple=2_5_6     ,  UpperCamelCase__							:				Tuple=0.1     ,  UpperCamelCase__							:				Optional[int]=0.0     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Dict=1.0     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				List[str]="sine"     ,  UpperCamelCase__							:				Any="resnet50"     ,  UpperCamelCase__							:				List[Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=4     ,  UpperCamelCase__							:				Tuple=4     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				Optional[int]=3_0_0     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[Any]=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.25     ,  UpperCamelCase__							:				List[Any]=False     ,  **UpperCamelCase__							:				Dict     ,  )->   Optional[int]:



						'''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.")
										__lowerCAmelCase:       List[Any]          =			CONFIG_MAPPING["resnet"](out_features=["stage4"])
								elif isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										__lowerCAmelCase:       int          =			backbone_config.get("model_type")
										__lowerCAmelCase:       List[str]          =			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase:       Any          =			config_class.from_dict(UpperCamelCase__)
						__lowerCAmelCase:       int          =			use_timm_backbone
						__lowerCAmelCase:       Any          =			backbone_config
						__lowerCAmelCase:       Tuple          =			num_channels
						__lowerCAmelCase:       str          =			num_queries
						__lowerCAmelCase:       List[str]          =			max_position_embeddings
						__lowerCAmelCase:       List[Any]          =			d_model
						__lowerCAmelCase:       Union[str, Any]          =			encoder_ffn_dim
						__lowerCAmelCase:       Tuple          =			encoder_layers
						__lowerCAmelCase:       List[str]          =			encoder_attention_heads
						__lowerCAmelCase:       Any          =			decoder_ffn_dim
						__lowerCAmelCase:       Union[str, Any]          =			decoder_layers
						__lowerCAmelCase:       List[Any]          =			decoder_attention_heads
						__lowerCAmelCase:       List[Any]          =			dropout
						__lowerCAmelCase:       Optional[Any]          =			attention_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_function
						__lowerCAmelCase:       Dict          =			init_std
						__lowerCAmelCase:       int          =			init_xavier_std
						__lowerCAmelCase:       str          =			encoder_layerdrop
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss
						__lowerCAmelCase:       List[Any]          =			position_embedding_type
						__lowerCAmelCase:       str          =			backbone
						__lowerCAmelCase:       Tuple          =			use_pretrained_backbone
						__lowerCAmelCase:       int          =			dilation
						# deformable attributes
						__lowerCAmelCase:       Union[str, Any]          =			num_feature_levels
						__lowerCAmelCase:       Optional[Any]          =			encoder_n_points
						__lowerCAmelCase:       Dict          =			decoder_n_points
						__lowerCAmelCase:       Optional[Any]          =			two_stage
						__lowerCAmelCase:       Tuple          =			two_stage_num_proposals
						__lowerCAmelCase:       int          =			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
						__lowerCAmelCase:       str          =			class_cost
						__lowerCAmelCase:       List[str]          =			bbox_cost
						__lowerCAmelCase:       List[str]          =			giou_cost
						# Loss coefficients
						__lowerCAmelCase:       Tuple          =			mask_loss_coefficient
						__lowerCAmelCase:       int          =			dice_loss_coefficient
						__lowerCAmelCase:       Any          =			bbox_loss_coefficient
						__lowerCAmelCase:       str          =			giou_loss_coefficient
						__lowerCAmelCase:       int          =			eos_coefficient
						__lowerCAmelCase:       Tuple          =			focal_alpha
						__lowerCAmelCase:       Optional[Any]          =			disable_custom_kernels
						super().__init__(is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__)

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



						'''simple docstring'''


						return self.encoder_attention_heads

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



						'''simple docstring'''


						return self.d_model




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



						'''simple docstring'''


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


