






import copy

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


lowercase	:				List[str]      	=     logging.get_logger(__name__)

lowercase	:				Optional[int]      	=     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}




class 		UpperCAmelCase_    (						lowerCAmelCase__       ):


						'''simple docstring'''




						A  : Union[str, Any]							=       """deta"""
						A  : str							=       {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}






						def __init__(						self		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=900		,					_SCREAMING_SNAKE_CASE=2048		,					_SCREAMING_SNAKE_CASE=6		,					_SCREAMING_SNAKE_CASE=2048		,					_SCREAMING_SNAKE_CASE=8		,					_SCREAMING_SNAKE_CASE=6		,					_SCREAMING_SNAKE_CASE=1024		,					_SCREAMING_SNAKE_CASE=8		,					_SCREAMING_SNAKE_CASE=0.0		,					_SCREAMING_SNAKE_CASE=True		,					_SCREAMING_SNAKE_CASE="relu"		,					_SCREAMING_SNAKE_CASE=256		,					_SCREAMING_SNAKE_CASE=0.1		,					_SCREAMING_SNAKE_CASE=0.0		,					_SCREAMING_SNAKE_CASE=0.0		,					_SCREAMING_SNAKE_CASE=0.02		,					_SCREAMING_SNAKE_CASE=1.0		,					_SCREAMING_SNAKE_CASE=True		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE="sine"		,					_SCREAMING_SNAKE_CASE=5		,					_SCREAMING_SNAKE_CASE=4		,					_SCREAMING_SNAKE_CASE=4		,					_SCREAMING_SNAKE_CASE=True		,					_SCREAMING_SNAKE_CASE=300		,					_SCREAMING_SNAKE_CASE=True		,					_SCREAMING_SNAKE_CASE=True		,					_SCREAMING_SNAKE_CASE=1		,					_SCREAMING_SNAKE_CASE=5		,					_SCREAMING_SNAKE_CASE=2		,					_SCREAMING_SNAKE_CASE=1		,					_SCREAMING_SNAKE_CASE=1		,					_SCREAMING_SNAKE_CASE=5		,					_SCREAMING_SNAKE_CASE=2		,					_SCREAMING_SNAKE_CASE=0.1		,					_SCREAMING_SNAKE_CASE=0.25		,					**_SCREAMING_SNAKE_CASE		,					)			->					List[Any]:
								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
										snake_case_					:				List[Any]    			=							CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
										if isinstance(__a		,					__a					):
												snake_case_					:				Optional[Any]    			=							backbone_config.pop("model_type"					)
												snake_case_					:				str    			=							CONFIG_MAPPING[backbone_model_type]
												snake_case_					:				Tuple    			=							config_class.from_dict(__a					)

								snake_case_					:				Union[str, Any]    			=							backbone_config
								snake_case_					:				Optional[int]    			=							num_queries
								snake_case_					:				int    			=							max_position_embeddings
								snake_case_					:				List[Any]    			=							d_model
								snake_case_					:				Optional[int]    			=							encoder_ffn_dim
								snake_case_					:				List[Any]    			=							encoder_layers
								snake_case_					:				int    			=							encoder_attention_heads
								snake_case_					:				Any    			=							decoder_ffn_dim
								snake_case_					:				List[Any]    			=							decoder_layers
								snake_case_					:				Tuple    			=							decoder_attention_heads
								snake_case_					:				Optional[Any]    			=							dropout
								snake_case_					:				Dict    			=							attention_dropout
								snake_case_					:				Dict    			=							activation_dropout
								snake_case_					:				Optional[Any]    			=							activation_function
								snake_case_					:				Dict    			=							init_std
								snake_case_					:				List[Any]    			=							init_xavier_std
								snake_case_					:				Tuple    			=							encoder_layerdrop
								snake_case_					:				int    			=							auxiliary_loss
								snake_case_					:				List[Any]    			=							position_embedding_type
								# deformable attributes
								snake_case_					:				Optional[int]    			=							num_feature_levels
								snake_case_					:				int    			=							encoder_n_points
								snake_case_					:				Optional[Any]    			=							decoder_n_points
								snake_case_					:				List[str]    			=							two_stage
								snake_case_					:				Any    			=							two_stage_num_proposals
								snake_case_					:				List[Any]    			=							with_box_refine
								snake_case_					:				Tuple    			=							assign_first_stage
								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
								snake_case_					:				str    			=							class_cost
								snake_case_					:				str    			=							bbox_cost
								snake_case_					:				Optional[Any]    			=							giou_cost
								# Loss coefficients
								snake_case_					:				Optional[int]    			=							mask_loss_coefficient
								snake_case_					:				List[Any]    			=							dice_loss_coefficient
								snake_case_					:				Tuple    			=							bbox_loss_coefficient
								snake_case_					:				str    			=							giou_loss_coefficient
								snake_case_					:				List[str]    			=							eos_coefficient
								snake_case_					:				List[Any]    			=							focal_alpha
								super().__init__(is_encoder_decoder=__a		,					**__a					)






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






						@property
						def    _lowerCAmelCase							(						self					)			->					Union[str, Any]:
								return self.d_model






						def    _lowerCAmelCase							(						self					)			->					Optional[int]:
								snake_case_					:				Any    			=							copy.deepcopy(self.__dict__					)
								snake_case_					:				str    			=							self.backbone_config.to_dict()
								snake_case_					:				List[str]    			=							self.__class__.model_type
								return output




"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

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




