
'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  int								=		RePaintPipeline
	_UpperCamelCase							:  Optional[Any]								=		IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
	_UpperCamelCase							:  Optional[int]								=		PipelineTesterMixin.required_optional_params - {
	    '''latents''',
	    '''num_images_per_prompt''',
	    '''callback''',
	    '''callback_steps''',
	}
	_UpperCamelCase							:  List[str]								=		IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCamelCase							:  Optional[int]								=		False




	def 	__A      (						self				: List[str]      ):
			torch.manual_seed(0      )
			torch.manual_seed(0      )
			lowerCAmelCase_   :       str    							=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''')					,						)
			lowerCAmelCase_   :       Union[str, Any]    							=RePaintScheduler()
			lowerCAmelCase_   :       Optional[Any]    							={'''unet''': unet, '''scheduler''': scheduler}
			return components




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: int=0      ):
			if str(UpperCamelCase_      ).startswith('''mps'''      ):
					lowerCAmelCase_   :       Tuple    							=torch.manual_seed(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       List[str]    							=torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=np.random.RandomState(UpperCamelCase_      ).standard_normal((1, 3, 32, 32)      )
			lowerCAmelCase_   :       int    							=torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_					,						dtype=torch.floataa      )
			lowerCAmelCase_   :       Tuple    							=(image > 0).to(device=UpperCamelCase_					,						dtype=torch.floataa      )
			lowerCAmelCase_   :       Tuple    							={
			    '''image''': image,
			    '''mask_image''': mask,
			    '''generator''': generator,
			    '''num_inference_steps''': 5,
			    '''eta''': 0.0,
			    '''jump_length''': 2,
			    '''jump_n_sample''': 2,
			    '''output_type''': '''numpy''',
			}
			return inputs




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							='''cpu'''  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase_   :       str    							=self.get_dummy_components()
			lowerCAmelCase_   :       str    							=RePaintPipeline(**UpperCamelCase_      )
			lowerCAmelCase_   :       str    							=sd_pipe.to(UpperCamelCase_      )
			sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							=self.get_dummy_inputs(UpperCamelCase_      )
			lowerCAmelCase_   :       int    							=sd_pipe(**UpperCamelCase_      ).images
			lowerCAmelCase_   :       List[Any]    							=image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase_   :       Optional[int]    							=np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




	@skip_mps
	def 	__A      (						self				: Optional[int]      ):
			return super().test_save_load_local()




	@unittest.skip('''non-deterministic pipeline'''      )
	def 	__A      (						self				: int      ):
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 	__A      (						self				: Optional[int]      ):
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 	__A      (						self				: Dict      ):
			return super().test_save_load_optional_components()





	@skip_mps
	def 	__A      (						self				: int      ):
			return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def 	__A      (						self				: str      ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       Any    							=load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
			    '''repaint/celeba_hq_256.png'''      )
			lowerCAmelCase_   :       Optional[Any]    							=load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''      )
			lowerCAmelCase_   :       Optional[Any]    							=load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
			    '''repaint/celeba_hq_256_result.npy'''      )

			lowerCAmelCase_   :       int    							='''google/ddpm-ema-celebahq-256'''
			lowerCAmelCase_   :       List[Any]    							=UNetaDModel.from_pretrained(UpperCamelCase_      )
			lowerCAmelCase_   :       str    							=RePaintScheduler.from_pretrained(UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=RePaintPipeline(unet=UpperCamelCase_					,						scheduler=UpperCamelCase_      ).to(UpperCamelCase_      )
			repaint.set_progress_bar_config(disable=UpperCamelCase_      )
			repaint.enable_attention_slicing()

			lowerCAmelCase_   :       str    							=torch.manual_seed(0      )
			lowerCAmelCase_   :       Tuple    							=repaint(
			    UpperCamelCase_					,						UpperCamelCase_					,						num_inference_steps=250					,						eta=0.0					,						jump_length=10					,						jump_n_sample=10					,						generator=UpperCamelCase_					,						output_type='''np'''					,						)
			lowerCAmelCase_   :       Dict    							=output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image      ).mean() < 1E-2








'''simple docstring'''






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if len(_SCREAMING_SNAKE_CASE  ) == 0:
				raise ValueError('''find_min() arg is an empty sequence'''  )
		if (
		    left >= len(_SCREAMING_SNAKE_CASE  )
		    or left < -len(_SCREAMING_SNAKE_CASE  )
		    or right >= len(_SCREAMING_SNAKE_CASE  )
		    or right < -len(_SCREAMING_SNAKE_CASE  )
		):
				raise IndexError('''list index out of range'''  )
		if left == right:
				return nums[left]
		lowerCAmelCase_   :       Dict    							=(left + right) >> 1  # the middle
		lowerCAmelCase_   :       Optional[int]    							=find_min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[left, mid]
		lowerCAmelCase_   :       Any    							=find_min(_SCREAMING_SNAKE_CASE  ,					mid + 1  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[mid + 1, right]

		return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






