



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    (			a__				,						a__				,						unittest.TestCase						):
			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     _lowerCamelCase       (						self   :  int)   ->    Tuple:



						"""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=__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=1_28 ,		)
						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=10_00 ,		hidden_act='gelu' ,		projection_dim=5_12 ,		)
						_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   :  Dict ,		A   :  str ,		A   :  Dict=0)   ->    int:



						"""simple docstring"""






						_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 ,		A   :  Optional[int] ,		A   :  Union[str, Any]=0)   ->    Optional[Any]:



						"""simple docstring"""






						_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   :  List[Any] ,		A   :  List[str] ,		A   :  Dict=0)   ->    Optional[int]:



						"""simple docstring"""






						_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   :  int)   ->    str:



						"""simple docstring"""






						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   :  Any)   ->    Union[str, Any]:



						"""simple docstring"""






						_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   :  Optional[Any])   ->    Union[str, Any]:



						"""simple docstring"""






						_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   :  Tuple)   ->    List[str]:



						"""simple docstring"""






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



			def     _lowerCamelCase       (						self   :  List[Any])   ->    Optional[int]:



						"""simple docstring"""






						_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 			__lowerCAmelCase    (			unittest.TestCase						):



			def     _lowerCamelCase       (						self   :  Tuple)   ->    Dict:



						"""simple docstring"""






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



			@classmethod
			def     _lowerCamelCase       (						cls   :  Optional[int])   ->    int:



						"""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((7_68, 7_68))

						_UpperCAmelCase		       =   raw_image



			def     _lowerCamelCase       (						self   :  Union[str, Any])   ->    List[Any]:



						"""simple docstring"""






						_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((7_68, 7_68)))
						    / 2_55
						)
						assert np.abs((expected_image - image).max()) < 5E-1



			def     _lowerCamelCase       (						self   :  int)   ->    Union[str, Any]:



						"""simple docstring"""






						_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((7_68, 7_68)))
						    / 2_55
						)
						assert np.abs((expected_image - image).max()) < 5E-1

'''simple docstring'''

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__a: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

 lowercase__				:    Optional[int]  =					{'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase     )]}
 lowercase__				:    Tuple  =					IterableDataset.from_generator(UpperCAmelCase		,     gen_kwargs=UpperCAmelCase     )
 if not streaming:
  lowercase__				:    int  =					Dataset.from_list(list(UpperCAmelCase     )     )

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

 lowercase__				:    Dict  =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 lowercase__				:    Optional[Any]  =					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 lowercase__				:    Union[str, Any]  =					sum(1 for _ in dataloader     )
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
  main()

