






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"""






	UpperCamelCase__								=      StableDiffusionDiffEditPipeline
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
	UpperCamelCase__								=      frozenset(
	    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	UpperCamelCase__								=      frozenset([])

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=4       ,    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=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			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									=			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									=			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    )
							_UpperCAmelCase									=			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       ,    )
							_UpperCAmelCase									=			CLIPTextModel(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

							_UpperCAmelCase									=			{
							    '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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, 16, 16)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			floats_tensor((1, 2, 4, 16, 16)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    '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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
							_UpperCAmelCase									=			Image.fromarray(np.uinta(UpperCAmelCase    )    ).convert('RGB'    )
							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    '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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
							_UpperCAmelCase									=			Image.fromarray(np.uinta(UpperCAmelCase    )    ).convert('RGB'    )
							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    '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    ):
							"""simple docstring"""







							if not hasattr(self.pipeline_class       ,    '_optional_components'    ):
													return

							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			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									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe(**UpperCAmelCase    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase    )
													_UpperCAmelCase									=			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									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe_loaded(**UpperCAmelCase    )[0]

							_UpperCAmelCase									=			np.abs(output - output_loaded    ).max()
							self.assertLess(UpperCAmelCase       ,    1e-4    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_mask_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe.generate_mask(**UpperCAmelCase    )
							_UpperCAmelCase									=			mask[0, -3:, -3:]

							self.assertEqual(mask.shape       ,    (1, 16, 16)    )
							_UpperCAmelCase									=			np.array([0] * 9    )
							_UpperCAmelCase									=			np.abs(mask_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(UpperCAmelCase       ,    1e-3    )
							self.assertEqual(mask[0, -3, -4]       ,    0    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inversion_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe.invert(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -1, -3:, -3:]

							self.assertEqual(image.shape       ,    (2, 32, 32, 3)    )
							_UpperCAmelCase									=			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									=			np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(UpperCAmelCase       ,    1e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_inference_batch_single_identical(expected_max_diff=5e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()

							_UpperCAmelCase									=			{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
							_UpperCAmelCase									=			DPMSolverMultistepScheduler(**UpperCAmelCase    )
							_UpperCAmelCase									=			DPMSolverMultistepInverseScheduler(**UpperCAmelCase    )

							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inversion_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe.invert(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -1, -3:, -3:]

							self.assertEqual(image.shape       ,    (2, 32, 32, 3)    )
							_UpperCAmelCase									=			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									=			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    ):
							"""simple docstring"""







							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

	@classmethod
	def        UpperCamelCase    (							cls    ):
							"""simple docstring"""







							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'    )

							_UpperCAmelCase									=			raw_image.convert('RGB'    ).resize((768, 768)    )

							_UpperCAmelCase									=			raw_image

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(0    )

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

							_UpperCAmelCase									=			'a bowl of fruit'
							_UpperCAmelCase									=			'a bowl of pears'

							_UpperCAmelCase									=			pipe.generate_mask(
							    image=self.raw_image       ,    source_prompt=UpperCAmelCase       ,    target_prompt=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    )

							_UpperCAmelCase									=			pipe.invert(
							    prompt=UpperCAmelCase       ,    image=self.raw_image       ,    inpaint_strength=0.7       ,    generator=UpperCAmelCase    ).latents

							_UpperCAmelCase									=			pipe(
							    prompt=UpperCAmelCase       ,    mask_image=UpperCAmelCase       ,    image_latents=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    negative_prompt=UpperCAmelCase       ,    inpaint_strength=0.7       ,    output_type='numpy'       ,    ).images[0]

							_UpperCAmelCase									=			(
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'    ).resize((768, 768)    )    )
							    / 255
							)
							assert np.abs((expected_image - image).max()    ) < 5e-1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(0    )

							_UpperCAmelCase									=			StableDiffusionDiffEditPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-1'       ,    safety_checker=UpperCAmelCase       ,    torch_dtype=torch.floataa    )
							_UpperCAmelCase									=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							_UpperCAmelCase									=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			'a bowl of fruit'
							_UpperCAmelCase									=			'a bowl of pears'

							_UpperCAmelCase									=			pipe.generate_mask(
							    image=self.raw_image       ,    source_prompt=UpperCAmelCase       ,    target_prompt=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    )

							_UpperCAmelCase									=			pipe.invert(
							    prompt=UpperCAmelCase       ,    image=self.raw_image       ,    inpaint_strength=0.7       ,    generator=UpperCAmelCase       ,    num_inference_steps=25       ,    ).latents

							_UpperCAmelCase									=			pipe(
							    prompt=UpperCAmelCase       ,    mask_image=UpperCAmelCase       ,    image_latents=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    negative_prompt=UpperCAmelCase       ,    inpaint_strength=0.7       ,    num_inference_steps=25       ,    output_type='numpy'       ,    ).images[0]

							_UpperCAmelCase									=			(
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'    ).resize((768, 768)    )    )
							    / 255
							)
							assert np.abs((expected_image - image).max()    ) < 5e-1








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_a      	=  get_logger(__name__)





class __lowerCamelCase (      enum.Enum):





	"""simple docstring"""






	UpperCamelCase__								=      "all_checks"
	UpperCamelCase__								=      "basic_checks"
	UpperCamelCase__								=      "no_checks"





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""









def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None	)->  str:





						"""simple docstring"""



						if expected_checksums is None:
												logger.info('Unable to verify checksums.'	)
												return
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise UnexpectedDownloadedFile(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						_UpperCAmelCase									=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						_UpperCAmelCase									=			' for ' + verification_name if verification_name is not None else ''
						if len(__lowerCAmelCase	) > 0:
												raise NonMatchingChecksumError(
												    F"""Checksums didn't match{for_verification_name}:\n"""
												    F"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'	)
						logger.info('All the checksums matched successfully' + for_verification_name	)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""









def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'	)
												return
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise ExpectedMoreSplits(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise UnexpectedSplits(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						_UpperCAmelCase									=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(__lowerCAmelCase	) > 0:
												raise NonMatchingSplitsSizesError(str(__lowerCAmelCase	)	)
						logger.info('All the splits matched successfully.'	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = True	)->  dict:





						"""simple docstring"""



						if record_checksum:
												_UpperCAmelCase									=			shaaaa()
												with open(__lowerCAmelCase ,		'rb'	) as f:
																		for chunk in iter(lambda: f.read(1 << 20	) ,		b''	):
																								m.update(__lowerCAmelCase	)
																		_UpperCAmelCase									=			m.hexdigest()
						else:
												_UpperCAmelCase									=			None
						return {"num_bytes": os.path.getsize(__lowerCAmelCase	), "checksum": checksum}


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False
