

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class _UpperCamelCase				(				__A				,						__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =StableDiffusionInpaintPipeline
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__							     =frozenset(
			    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase__							     =frozenset([] )
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[str]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							UNetaDConditionModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=9   ,   out_channels=4   ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,   cross_attention_dim=32   ,   attention_head_dim=(2, 4)   ,   use_linear_projection=a   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							PNDMScheduler(skip_prk_steps=a						)
				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoencoderKL(
				    block_out_channels=[32, 64]   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,   latent_channels=4   ,   sample_size=128   ,   )
				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							CLIPTextConfig(
				    bos_token_id=0   ,   eos_token_id=2   ,   hidden_size=32   ,   intermediate_size=37   ,   layer_norm_eps=1e-05   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   hidden_act="gelu"   ,   projection_dim=512   ,   )
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							CLIPTextModel(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							{
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Tuple   ,   a				:			List[Any]=0						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(a						)						).to(a						)
				SCREAMING_SNAKE_CASE :   str					  =							image.cpu().permute(0   ,   2   ,   3   ,   1						)[0]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							Image.fromarray(np.uinta(a						)						).convert("RGB"						).resize((64, 64)						)
				SCREAMING_SNAKE_CASE :   Any					  =							Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
				if str(a						).startswith("mps"						):
					SCREAMING_SNAKE_CASE :   Dict					  =							torch.manual_seed(a						)
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							torch.Generator(device=a						).manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": init_image,
				    "mask_image": mask_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs
			def 				__UpperCamelCase			(      self				:			int						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Tuple					  =							StableDiffusionInpaintPipeline(**a						)
				SCREAMING_SNAKE_CASE :   int					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE :   Dict					  =							np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]						)

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






			def 				__UpperCamelCase			(      self				:			Dict						)				->							List[str]:

				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






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







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

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/sd2-inpaint/init_image.png"						)
				SCREAMING_SNAKE_CASE :   str					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
				    "/yellow_cat_sitting_on_a_park_bench.npy"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							"stabilityai/stable-diffusion-2-inpainting"
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionInpaintPipeline.from_pretrained(a   ,   safety_checker=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"Face of a yellow cat, high resolution, sitting on a park bench"

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   int					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							output.images[0]

				assert image.shape == (512, 512, 3)
				assert np.abs(expected_image - image						).max() < 9e-3
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/sd2-inpaint/init_image.png"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
				    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"						)

				SCREAMING_SNAKE_CASE :   Any					  =							"stabilityai/stable-diffusion-2-inpainting"
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							StableDiffusionInpaintPipeline.from_pretrained(
				    a   ,   torch_dtype=torch.floataa   ,   safety_checker=a   ,   )
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE :   Dict					  =							"Face of a yellow cat, high resolution, sitting on a park bench"

				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							output.images[0]

				assert image.shape == (512, 512, 3)
				assert np.abs(expected_image - image						).max() < 5e-1






			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/sd2-inpaint/init_image.png"						)
				SCREAMING_SNAKE_CASE :   str					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)

				SCREAMING_SNAKE_CASE :   Dict					  =							"stabilityai/stable-diffusion-2-inpainting"
				SCREAMING_SNAKE_CASE :   Any					  =							PNDMScheduler.from_pretrained(a   ,   subfolder="scheduler"						)
				SCREAMING_SNAKE_CASE :   str					  =							StableDiffusionInpaintPipeline.from_pretrained(
				    a   ,   safety_checker=a   ,   scheduler=a   ,   torch_dtype=torch.floataa   ,   )
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing(1						)
				pipe.enable_sequential_cpu_offload()

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"Face of a yellow cat, high resolution, sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   generator=a   ,   num_inference_steps=2   ,   output_type="np"   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							torch.cuda.max_memory_allocated()
				# make sure that less than 2.65 GB is allocated
				assert mem_bytes < 2.65 * 10**9







'''simple docstring'''
import copy

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


UpperCamelCase__			=      logging.get_logger(__name__)

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


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Union[str, Any]									=     'deta'
    __lowerCamelCase: str									=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }




    def __init__(      self	:							List[str]	,  a	:							Union[str, Any]=None	,  a	:							Dict=9_0_0	,  a	:							str=2_0_4_8	,  a	:							Union[str, Any]=6	,  a	:							int=2_0_4_8	,  a	:							Optional[int]=8	,  a	:							Optional[Any]=6	,  a	:							Optional[Any]=1_0_2_4	,  a	:							Any=8	,  a	:							Dict=0.0	,  a	:							Optional[int]=True	,  a	:							Union[str, Any]="relu"	,  a	:							str=2_5_6	,  a	:							str=0.1	,  a	:							Any=0.0	,  a	:							Optional[Any]=0.0	,  a	:							str=0.02	,  a	:							int=1.0	,  a	:							Tuple=True	,  a	:							Optional[int]=False	,  a	:							str="sine"	,  a	:							List[Any]=5	,  a	:							str=4	,  a	:							Any=4	,  a	:							Optional[Any]=True	,  a	:							Union[str, Any]=3_0_0	,  a	:							List[Any]=True	,  a	:							Tuple=True	,  a	:							Any=1	,  a	:							int=5	,  a	:							Optional[int]=2	,  a	:							Tuple=1	,  a	:							List[str]=1	,  a	:							Tuple=5	,  a	:							Tuple=2	,  a	:							List[str]=0.1	,  a	:							Tuple=0.25	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       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_						:     Any							     =  backbone_config.pop("model_type"      )
             lowercase_						:     str							     =  CONFIG_MAPPING[backbone_model_type]
             lowercase_						:     Any							     =  config_class.from_dict(a      )

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




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.encoder_attention_heads




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.d_model





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  copy.deepcopy(self.__dict__      )
       lowercase_						:     Union[str, Any]							     =  self.backbone_config.to_dict()
       lowercase_						:     str							     =  self.__class__.model_type
       return output




