"""simple docstring"""






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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




		"""simple docstring"""

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







		def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
						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=lowerCamelCase__					,)
						UpperCAmelCase__    =							DDIMScheduler(
						    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
						UpperCAmelCase__    =							DDIMInverseScheduler(
						    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_zero=lowerCamelCase__					,)
						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=1_000					,hidden_act='gelu'					,projection_dim=512					,)
						UpperCAmelCase__    =							CLIPTextModel(lowerCamelCase__    )
						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 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			int=0    ):
						UpperCAmelCase__    =							floats_tensor((1, 16, 16)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
						UpperCAmelCase__    =							floats_tensor((1, 2, 4, 16, 16)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
						if str(lowerCamelCase__    ).startswith('mps'    ):
										UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
						else:
										UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
						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 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int=0    ):
						UpperCAmelCase__    =							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
						UpperCAmelCase__    =							image.cpu().permute(0					,2					,3					,1    )[0]
						UpperCAmelCase__    =							Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert('RGB'    )
						if str(lowerCamelCase__    ).startswith('mps'    ):
										UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
						else:
										UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
						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 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			str=0    ):
						UpperCAmelCase__    =							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
						UpperCAmelCase__    =							image.cpu().permute(0					,2					,3					,1    )[0]
						UpperCAmelCase__    =							Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert('RGB'    )
						if str(lowerCamelCase__    ).startswith('mps'    ):
										UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
						else:
										UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
						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 	__lowerCAmelCase		(       self     :			Any    ):
						if not hasattr(self.pipeline_class					,'_optional_components'    ):
										return

						UpperCAmelCase__    =							self.get_dummy_components()
						UpperCAmelCase__    =							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}    )

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

						with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(lowerCamelCase__    )
										UpperCAmelCase__    =							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.'''					,)

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

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







		def 	__lowerCAmelCase		(       self     :			str    ):
						UpperCAmelCase__    =							'cpu'

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

						UpperCAmelCase__    =							self.get_dummy_mask_inputs(lowerCamelCase__    )
						UpperCAmelCase__    =							pipe.generate_mask(**lowerCamelCase__    )
						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(lowerCamelCase__					,1e-3    )
						self.assertEqual(mask[0, -3, -4]					,0    )







		def 	__lowerCAmelCase		(       self     :			List[str]    ):
						UpperCAmelCase__    =							'cpu'

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

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

						self.assertEqual(image.shape					,(2, 32, 32, 3)    )
						UpperCAmelCase__    =							np.array(
						    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
						UpperCAmelCase__    =							np.abs(image_slice.flatten() - expected_slice    ).max()
						self.assertLessEqual(lowerCamelCase__					,1e-3    )







		def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
						super().test_inference_batch_single_identical(expected_max_diff=5e-3    )







		def 	__lowerCAmelCase		(       self     :			Dict    ):
						UpperCAmelCase__    =							'cpu'

						UpperCAmelCase__    =							self.get_dummy_components()

						UpperCAmelCase__    =							{'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
						UpperCAmelCase__    =							DPMSolverMultistepScheduler(**lowerCamelCase__    )
						UpperCAmelCase__    =							DPMSolverMultistepInverseScheduler(**lowerCamelCase__    )

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

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

						self.assertEqual(image.shape					,(2, 32, 32, 3)    )
						UpperCAmelCase__    =							np.array(
						    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
						UpperCAmelCase__    =							np.abs(image_slice.flatten() - expected_slice    ).max()
						self.assertLessEqual(lowerCamelCase__					,1e-3    )






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




		"""simple docstring"""

		def 	__lowerCAmelCase		(       self     :			List[Any]    ):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		@classmethod
		def 	__lowerCAmelCase		(       cls     :			Dict    ):
						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 	__lowerCAmelCase		(       self     :			int    ):
						UpperCAmelCase__    =							torch.manual_seed(0    )

						UpperCAmelCase__    =							StableDiffusionDiffEditPipeline.from_pretrained(
						    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,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=lowerCamelCase__    )

						UpperCAmelCase__    =							'a bowl of fruit'
						UpperCAmelCase__    =							'a bowl of pears'

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

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

						UpperCAmelCase__    =							pipe(
						    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,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 	__lowerCAmelCase		(       self     :			Optional[int]    ):
						UpperCAmelCase__    =							torch.manual_seed(0    )

						UpperCAmelCase__    =							StableDiffusionDiffEditPipeline.from_pretrained(
						    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,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=lowerCamelCase__    )

						UpperCAmelCase__    =							'a bowl of fruit'
						UpperCAmelCase__    =							'a bowl of pears'

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

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

						UpperCAmelCase__    =							pipe(
						    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,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




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase__						:						Dict		  =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'



def 						a_		(      ):
				UpperCAmelCase__    =							_ask_options(
				    'In which compute environment are you running?'      ,    ['This machine', 'AWS (Amazon SageMaker)']      ,    _convert_compute_environment      ,    )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								UpperCAmelCase__    =							get_sagemaker_input()
				else:
								UpperCAmelCase__    =							get_cluster_input()
				return config



def 						a_		(      lowerCamelCase=None ):
				if subparsers is not None:
								UpperCAmelCase__    =							subparsers.add_parser('config'      ,    description=lowerCamelCase )
				else:
								UpperCAmelCase__    =							argparse.ArgumentParser('Accelerate config command'      ,    description=lowerCamelCase )

				parser.add_argument(
				    '--config_file'      ,    default=lowerCamelCase      ,    help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )      ,    )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase )
				return parser



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							get_user_input()
				if args.config_file is not None:
								UpperCAmelCase__    =							args.config_file
				else:
								if not os.path.isdir(lowerCamelCase ):
												os.makedirs(lowerCamelCase )
								UpperCAmelCase__    =							default_yaml_config_file

				if config_file.endswith('.json' ):
								config.to_json_file(lowerCamelCase )
				else:
								config.to_yaml_file(lowerCamelCase )
				print(f'''accelerate configuration saved at {config_file}''' )



def 						a_		(      ):
				UpperCAmelCase__    =							config_command_parser()
				UpperCAmelCase__    =							parser.parse_args()
				config_command(lowerCamelCase )


if __name__ == "__main__":
						main()



