






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

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


enable_full_determinism()



class 						lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  StableDiffusionDiffEditPipeline
		_snake_case   :   Optional[Any]							=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
		_snake_case   :   Optional[Any]							=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
		_snake_case   :   Tuple							=  frozenset(
		    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		_snake_case   :   Dict							=  frozenset([]      )



		def  __UpperCAmelCase      (							self       )     ->							str:
							torch.manual_seed(0       )
							UpperCAmelCase_   :				List[Any]						=						UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)    ,		layers_per_block=2    ,		sample_size=3_2    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,		cross_attention_dim=3_2    ,		attention_head_dim=(2, 4)    ,		use_linear_projection=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Tuple						=						DDIMScheduler(
							    beta_start=0.0_00_85    ,		beta_end=0.0_12    ,		beta_schedule='scaled_linear'    ,		clip_sample=_UpperCamelCase    ,		set_alpha_to_one=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				int						=						DDIMInverseScheduler(
							    beta_start=0.0_00_85    ,		beta_end=0.0_12    ,		beta_schedule='scaled_linear'    ,		clip_sample=_UpperCamelCase    ,		set_alpha_to_zero=_UpperCamelCase    ,		)
							torch.manual_seed(0       )
							UpperCAmelCase_   :				List[Any]						=						AutoencoderKL(
							    block_out_channels=[3_2, 6_4]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=4    ,		sample_size=1_2_8    ,		)
							torch.manual_seed(0       )
							UpperCAmelCase_   :				List[str]						=						CLIPTextConfig(
							    bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=3_2    ,		intermediate_size=3_7    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1_0_0_0    ,		hidden_act='gelu'    ,		projection_dim=5_1_2    ,		)
							UpperCAmelCase_   :				List[Any]						=						CLIPTextModel(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

							UpperCAmelCase_   :				Dict						=						{
							    'unet': unet,
							    'scheduler': scheduler,
							    'inverse_scheduler': inverse_scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=						floats_tensor((1, 1_6, 1_6)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						floats_tensor((1, 2, 4, 1_6, 1_6)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							if str(_UpperCamelCase       ).startswith('mps'       ):
												UpperCAmelCase_   :				Tuple						=						torch.manual_seed(_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Dict						=						torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						{
							    'prompt': 'a dog and a newt',
							    'mask_image': mask,
							    'image_latents': latents,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'inpaint_strength': 1.0,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}

							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[Any]						=						floats_tensor((1, 3, 3_2, 3_2)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						image.cpu().permute(0    ,		2    ,		3    ,		1       )[0]
							UpperCAmelCase_   :				Dict						=						Image.fromarray(np.uinta(_UpperCamelCase       )       ).convert('RGB'       )
							if str(_UpperCamelCase       ).startswith('mps'       ):
												UpperCAmelCase_   :				str						=						torch.manual_seed(_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Optional[Any]						=						torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						{
							    'image': image,
							    'source_prompt': 'a cat and a frog',
							    'target_prompt': 'a dog and a newt',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'num_maps_per_mask': 2,
							    'mask_encode_strength': 1.0,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}

							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0       )     ->							Optional[Any]:
							UpperCAmelCase_   :				List[str]						=						floats_tensor((1, 3, 3_2, 3_2)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						image.cpu().permute(0    ,		2    ,		3    ,		1       )[0]
							UpperCAmelCase_   :				Union[str, Any]						=						Image.fromarray(np.uinta(_UpperCamelCase       )       ).convert('RGB'       )
							if str(_UpperCamelCase       ).startswith('mps'       ):
												UpperCAmelCase_   :				str						=						torch.manual_seed(_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Optional[int]						=						torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						{
							    'image': image,
							    'prompt': 'a cat and a frog',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'inpaint_strength': 1.0,
							    'guidance_scale': 6.0,
							    'decode_latents': True,
							    'output_type': 'numpy',
							}
							return inputs



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							if not hasattr(self.pipeline_class    ,		'_optional_components'       ):
												return

							UpperCAmelCase_   :				Dict						=						self.get_dummy_components()
							UpperCAmelCase_   :				str						=						self.pipeline_class(**_UpperCamelCase       )
							pipe.to(_UpperCamelCase       )
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
												setattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}       )

							UpperCAmelCase_   :				int						=						self.get_dummy_inputs(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						pipe(**_UpperCamelCase       )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(_UpperCamelCase       )
												UpperCAmelCase_   :				Union[str, Any]						=						self.pipeline_class.from_pretrained(_UpperCamelCase       )
												pipe_loaded.to(_UpperCamelCase       )
												pipe_loaded.set_progress_bar_config(disable=_UpperCamelCase       )

							for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(_UpperCamelCase    ,		_UpperCamelCase       ) is None    ,		f"`{optional_component}` did not stay set to None after loading."    ,		)

							UpperCAmelCase_   :				Union[str, Any]						=						self.get_dummy_inputs(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						pipe_loaded(**_UpperCamelCase       )[0]

							UpperCAmelCase_   :				Any						=						np.abs(output - output_loaded       ).max()
							self.assertLess(_UpperCamelCase    ,		1E-4       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Union[str, Any]						=						'cpu'

							UpperCAmelCase_   :				int						=						self.get_dummy_components()
							UpperCAmelCase_   :				List[Any]						=						self.pipeline_class(**_UpperCamelCase       )
							pipe.to(_UpperCamelCase       )
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						self.get_dummy_mask_inputs(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						pipe.generate_mask(**_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						mask[0, -3:, -3:]

							self.assertEqual(mask.shape    ,		(1, 1_6, 1_6)       )
							UpperCAmelCase_   :				Any						=						np.array([0] * 9       )
							UpperCAmelCase_   :				int						=						np.abs(mask_slice.flatten() - expected_slice       ).max()
							self.assertLessEqual(_UpperCamelCase    ,		1E-3       )
							self.assertEqual(mask[0, -3, -4]    ,		0       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Any						=						'cpu'

							UpperCAmelCase_   :				Tuple						=						self.get_dummy_components()
							UpperCAmelCase_   :				Dict						=						self.pipeline_class(**_UpperCamelCase       )
							pipe.to(_UpperCamelCase       )
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						self.get_dummy_inversion_inputs(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						pipe.invert(**_UpperCamelCase       ).images
							UpperCAmelCase_   :				str						=						image[0, -1, -3:, -3:]

							self.assertEqual(image.shape    ,		(2, 3_2, 3_2, 3)       )
							UpperCAmelCase_   :				Optional[Any]						=						np.array(
							    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]    ,		)
							UpperCAmelCase_   :				Dict						=						np.abs(image_slice.flatten() - expected_slice       ).max()
							self.assertLessEqual(_UpperCamelCase    ,		1E-3       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							super().test_inference_batch_single_identical(expected_max_diff=5E-3       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Tuple						=						'cpu'

							UpperCAmelCase_   :				Optional[Any]						=						self.get_dummy_components()

							UpperCAmelCase_   :				Tuple						=						{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
							UpperCAmelCase_   :				Optional[Any]						=						DPMSolverMultistepScheduler(**_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						DPMSolverMultistepInverseScheduler(**_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						self.pipeline_class(**_UpperCamelCase       )
							pipe.to(_UpperCamelCase       )
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						self.get_dummy_inversion_inputs(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						pipe.invert(**_UpperCamelCase       ).images
							UpperCAmelCase_   :				List[str]						=						image[0, -1, -3:, -3:]

							self.assertEqual(image.shape    ,		(2, 3_2, 3_2, 3)       )
							UpperCAmelCase_   :				str						=						np.array(
							    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]    ,		)
							UpperCAmelCase_   :				Any						=						np.abs(image_slice.flatten() - expected_slice       ).max()
							self.assertLessEqual(_UpperCamelCase    ,		1E-3       )





@require_torch_gpu
@slow
class 						lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							str:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		@classmethod
		def  __UpperCAmelCase      (							cls       )     ->							List[str]:
							UpperCAmelCase_   :				List[str]						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'       )

							UpperCAmelCase_   :				Any						=						raw_image.convert('RGB'       ).resize((7_6_8, 7_6_8)       )

							UpperCAmelCase_   :				Any						=						raw_image



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Dict						=						torch.manual_seed(0       )

							UpperCAmelCase_   :				Any						=						StableDiffusionDiffEditPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-1'    ,		safety_checker=_UpperCamelCase    ,		torch_dtype=torch.floataa       )
							UpperCAmelCase_   :				Any						=						DDIMScheduler.from_config(pipe.scheduler.config       )
							UpperCAmelCase_   :				Dict						=						DDIMInverseScheduler.from_config(pipe.scheduler.config       )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						'a bowl of fruit'
							UpperCAmelCase_   :				Union[str, Any]						=						'a bowl of pears'

							UpperCAmelCase_   :				Optional[int]						=						pipe.generate_mask(
							    image=self.raw_image    ,		source_prompt=_UpperCamelCase    ,		target_prompt=_UpperCamelCase    ,		generator=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Union[str, Any]						=						pipe.invert(
							    prompt=_UpperCamelCase    ,		image=self.raw_image    ,		inpaint_strength=0.7    ,		generator=_UpperCamelCase       ).latents

							UpperCAmelCase_   :				Any						=						pipe(
							    prompt=_UpperCamelCase    ,		mask_image=_UpperCamelCase    ,		image_latents=_UpperCamelCase    ,		generator=_UpperCamelCase    ,		negative_prompt=_UpperCamelCase    ,		inpaint_strength=0.7    ,		output_type='numpy'    ,		).images[0]

							UpperCAmelCase_   :				Union[str, Any]						=						(
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'       ).resize((7_6_8, 7_6_8)       )       )
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()       ) < 5E-1



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[Any]						=						torch.manual_seed(0       )

							UpperCAmelCase_   :				List[str]						=						StableDiffusionDiffEditPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-1'    ,		safety_checker=_UpperCamelCase    ,		torch_dtype=torch.floataa       )
							UpperCAmelCase_   :				Optional[int]						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
							UpperCAmelCase_   :				Tuple						=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config       )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						'a bowl of fruit'
							UpperCAmelCase_   :				List[str]						=						'a bowl of pears'

							UpperCAmelCase_   :				str						=						pipe.generate_mask(
							    image=self.raw_image    ,		source_prompt=_UpperCamelCase    ,		target_prompt=_UpperCamelCase    ,		generator=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Optional[int]						=						pipe.invert(
							    prompt=_UpperCamelCase    ,		image=self.raw_image    ,		inpaint_strength=0.7    ,		generator=_UpperCamelCase    ,		num_inference_steps=2_5    ,		).latents

							UpperCAmelCase_   :				Union[str, Any]						=						pipe(
							    prompt=_UpperCamelCase    ,		mask_image=_UpperCamelCase    ,		image_latents=_UpperCamelCase    ,		generator=_UpperCamelCase    ,		negative_prompt=_UpperCamelCase    ,		inpaint_strength=0.7    ,		num_inference_steps=2_5    ,		output_type='numpy'    ,		).images[0]

							UpperCAmelCase_   :				Any						=						(
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'       ).resize((7_6_8, 7_6_8)       )       )
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()       ) < 5E-1














def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							int					,	__snake_case   :							List[str]  ):


					'''simple docstring'''


					if n == 0:
										return 1

					elif n % 2 == 1:
										return (binary_exponentiation(__snake_case					,	n - 1					,	__snake_case  ) * a) % mod

					else:
										UpperCAmelCase_   :				Optional[int]						=						binary_exponentiation(__snake_case					,	n / 2					,	__snake_case  )
										return (b * b) % mod


# a prime number
__UpperCAmelCase     							=						701

__UpperCAmelCase     							=						1000000000
__UpperCAmelCase     							=						10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)






