





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       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__              =	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=__lowerCAmelCase ,						)
				lowerCamelCase__              =	DDIMScheduler(
				    beta_start=0.0_0085 ,						beta_end=0.012 ,						beta_schedule='''scaled_linear''' ,						clip_sample=__lowerCAmelCase ,						set_alpha_to_one=__lowerCAmelCase ,						)
				lowerCamelCase__              =	DDIMInverseScheduler(
				    beta_start=0.0_0085 ,						beta_end=0.012 ,						beta_schedule='''scaled_linear''' ,						clip_sample=__lowerCAmelCase ,						set_alpha_to_zero=__lowerCAmelCase ,						)
				torch.manual_seed(0							)
				lowerCamelCase__              =	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							)
				lowerCamelCase__              =	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 ,						)
				lowerCamelCase__              =	CLIPTextModel(__lowerCAmelCase							)
				lowerCamelCase__              =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

				lowerCamelCase__              =	{
				    '''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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__              =	floats_tensor((1, 1_6, 1_6) ,						rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	floats_tensor((1, 2, 4, 1_6, 1_6) ,						rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__              =	torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__              =	{
				    '''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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__              =	floats_tensor((1, 3, 3_2, 3_2) ,						rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	image.cpu().permute(0 ,						2 ,						3 ,						1							)[0]
				lowerCamelCase__              =	Image.fromarray(np.uinta(__lowerCAmelCase							)							).convert('''RGB'''							)
				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__              =	torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__              =	{
				    '''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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__              =	floats_tensor((1, 3, 3_2, 3_2) ,						rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	image.cpu().permute(0 ,						2 ,						3 ,						1							)[0]
				lowerCamelCase__              =	Image.fromarray(np.uinta(__lowerCAmelCase							)							).convert('''RGB'''							)
				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__              =	torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__              =	{
				    '''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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	self.get_dummy_components()
				lowerCamelCase__              =	self.pipeline_class(**__lowerCAmelCase							)
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

				lowerCamelCase__              =	self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__              =	pipe(**__lowerCAmelCase							)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(__lowerCAmelCase							)
					lowerCamelCase__              =	self.pipeline_class.from_pretrained(__lowerCAmelCase							)
					pipe_loaded.to(__lowerCAmelCase							)
					pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase							)

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

				lowerCamelCase__              =	self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__              =	pipe_loaded(**__lowerCAmelCase							)[0]

				lowerCamelCase__              =	np.abs(output - output_loaded							).max()
				self.assertLess(__lowerCAmelCase ,						1E-4							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''cpu'''

				lowerCamelCase__              =	self.get_dummy_components()
				lowerCamelCase__              =	self.pipeline_class(**__lowerCAmelCase							)
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''cpu'''

				lowerCamelCase__              =	self.get_dummy_components()
				lowerCamelCase__              =	self.pipeline_class(**__lowerCAmelCase							)
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

				self.assertEqual(image.shape ,						(2, 3_2, 3_2, 3)							)
				lowerCamelCase__              =	np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,						)
				lowerCamelCase__              =	np.abs(image_slice.flatten() - expected_slice							).max()
				self.assertLessEqual(__lowerCAmelCase ,						1E-3							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''cpu'''

				lowerCamelCase__              =	self.get_dummy_components()

				lowerCamelCase__              =	{'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
				lowerCamelCase__              =	DPMSolverMultistepScheduler(**__lowerCAmelCase							)
				lowerCamelCase__              =	DPMSolverMultistepInverseScheduler(**__lowerCAmelCase							)

				lowerCamelCase__              =	self.pipeline_class(**__lowerCAmelCase							)
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

				self.assertEqual(image.shape ,						(2, 3_2, 3_2, 3)							)
				lowerCamelCase__              =	np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,						)
				lowerCamelCase__              =	np.abs(image_slice.flatten() - expected_slice							).max()
				self.assertLessEqual(__lowerCAmelCase ,						1E-3							)






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

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@classmethod
			def 	__lowerCamelCase       (					cls							):



				'''simple docstring'''







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

				lowerCamelCase__              =	raw_image.convert('''RGB'''							).resize((7_6_8, 7_6_8)							)

				lowerCamelCase__              =	raw_image





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	torch.manual_seed(0							)

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

				lowerCamelCase__              =	'''a bowl of fruit'''
				lowerCamelCase__              =	'''a bowl of pears'''

				lowerCamelCase__              =	pipe.generate_mask(
				    image=self.raw_image ,						source_prompt=__lowerCAmelCase ,						target_prompt=__lowerCAmelCase ,						generator=__lowerCAmelCase ,						)

				lowerCamelCase__              =	pipe.invert(
				    prompt=__lowerCAmelCase ,						image=self.raw_image ,						inpaint_strength=0.7 ,						generator=__lowerCAmelCase							).latents

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

				lowerCamelCase__              =	(
				    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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	torch.manual_seed(0							)

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

				lowerCamelCase__              =	'''a bowl of fruit'''
				lowerCamelCase__              =	'''a bowl of pears'''

				lowerCamelCase__              =	pipe.generate_mask(
				    image=self.raw_image ,						source_prompt=__lowerCAmelCase ,						target_prompt=__lowerCAmelCase ,						generator=__lowerCAmelCase ,						)

				lowerCamelCase__              =	pipe.invert(
				    prompt=__lowerCAmelCase ,						image=self.raw_image ,						inpaint_strength=0.7 ,						generator=__lowerCAmelCase ,						num_inference_steps=2_5 ,						).latents

				lowerCamelCase__              =	pipe(
				    prompt=__lowerCAmelCase ,						mask_image=__lowerCAmelCase ,						image_latents=__lowerCAmelCase ,						generator=__lowerCAmelCase ,						negative_prompt=__lowerCAmelCase ,						inpaint_strength=0.7 ,						num_inference_steps=2_5 ,						output_type='''numpy''' ,						).images[0]

				lowerCamelCase__              =	(
				    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










import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a						=   logging.get_logger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				warnings.warn(
				    '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use SegformerImageProcessor instead.''' ,						__lowerCAmelCase ,						)
				super().__init__(*__lowerCAmelCase ,						**__lowerCAmelCase							)



