






"""simple docstring"""



import copy

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


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

__A		:       int  				=		{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   "deta"
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   {
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(				self     :						Union[str, Any]    ,						A     :						Optional[int]=None    ,						A     :						Union[str, Any]=9_00    ,						A     :						Tuple=20_48    ,						A     :						int=6    ,						A     :						str=20_48    ,						A     :						Any=8    ,						A     :						Optional[int]=6    ,						A     :						Dict=10_24    ,						A     :						str=8    ,						A     :						Dict=0.0    ,						A     :						Union[str, Any]=True    ,						A     :						List[Any]="relu"    ,						A     :						Tuple=2_56    ,						A     :						Optional[int]=0.1    ,						A     :						int=0.0    ,						A     :						str=0.0    ,						A     :						List[Any]=0.02    ,						A     :						Union[str, Any]=1.0    ,						A     :						str=True    ,						A     :						str=False    ,						A     :						Optional[int]="sine"    ,						A     :						Optional[Any]=5    ,						A     :						str=4    ,						A     :						Union[str, Any]=4    ,						A     :						Tuple=True    ,						A     :						Union[str, Any]=3_00    ,						A     :						Optional[Any]=True    ,						A     :						int=True    ,						A     :						Dict=1    ,						A     :						Tuple=5    ,						A     :						Optional[Any]=2    ,						A     :						Optional[Any]=1    ,						A     :						Any=1    ,						A     :						int=5    ,						A     :						Optional[Any]=2    ,						A     :						List[str]=0.1    ,						A     :						Dict=0.25    ,						**A     :						Tuple    ,						)  ->  Dict:
							if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
									lowercase_ : Optional[int]							       =   CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']   )
							else:
									if isinstance(A    ,						A   ):
											lowercase_ : List[str]							       =   backbone_config.pop('''model_type'''   )
											lowercase_ : List[str]							       =   CONFIG_MAPPING[backbone_model_type]
											lowercase_ : Union[str, Any]							       =   config_class.from_dict(A   )

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




					@property
					def   A  (				self     :						Any   )  ->  int:
							return self.encoder_attention_heads




					@property
					def   A  (				self     :						Optional[int]   )  ->  int:
							return self.d_model






					def   A  (				self     :						List[Any]   )  ->  Dict:
							lowercase_ : str							       =   copy.deepcopy(self.__dict__   )
							lowercase_ : Union[str, Any]							       =   self.backbone_config.to_dict()
							lowercase_ : List[Any]							       =   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		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        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=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        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=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

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





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        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=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

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





