



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  (						lowerCAmelCase__	,						lowerCAmelCase__	,						unittest.TestCase ):
 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=(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						,					)
    __lowerCamelCase    				=     DDIMScheduler(
        beta_start=0.00_085						,					beta_end=0.012						,					beta_schedule='''scaled_linear'''						,					clip_sample=__UpperCAmelCase						,					set_alpha_to_one=__UpperCAmelCase						,					)
    __lowerCamelCase    				=     DDIMInverseScheduler(
        beta_start=0.00_085						,					beta_end=0.012						,					beta_schedule='''scaled_linear'''						,					clip_sample=__UpperCAmelCase						,					set_alpha_to_zero=__UpperCAmelCase						,					)
    torch.manual_seed(0					)
    __lowerCamelCase    				=     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					)
    __lowerCamelCase    				=     CLIPTextConfig(
        bos_token_id=0						,					eos_token_id=2						,					hidden_size=32						,					intermediate_size=37						,					layer_norm_eps=1E-0_5						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					hidden_act='''gelu'''						,					projection_dim=512						,					)
    __lowerCamelCase    				=     CLIPTextModel(__UpperCAmelCase					)
    __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						,					__UpperCAmelCase						,					__UpperCAmelCase=0					):



    '''simple docstring'''
    __lowerCamelCase    				=     floats_tensor((1, 16, 16)						,					rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
    __lowerCamelCase    				=     floats_tensor((1, 2, 4, 16, 16)						,					rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
    if str(__UpperCAmelCase					).startswith('''mps'''					):
       __lowerCamelCase    				=     torch.manual_seed(__UpperCAmelCase					)
    else:
       __lowerCamelCase    				=     torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
    __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						,					__UpperCAmelCase						,					__UpperCAmelCase=0					):



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



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

    __lowerCamelCase    				=     self.get_dummy_inputs(__UpperCAmelCase					)
    __lowerCamelCase    				=     pipe(**__UpperCAmelCase					)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(__UpperCAmelCase					)
       __lowerCamelCase    				=     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."""						,					)

    __lowerCamelCase    				=     self.get_dummy_inputs(__UpperCAmelCase					)
    __lowerCamelCase    				=     pipe_loaded(**__UpperCAmelCase					)[0]

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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''cpu'''

    __lowerCamelCase    				=     self.get_dummy_components()
    __lowerCamelCase    				=     self.pipeline_class(**__UpperCAmelCase					)
    pipe.to(__UpperCAmelCase					)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

    self.assertEqual(mask.shape						,					(1, 16, 16)					)
    __lowerCamelCase    				=     np.array([0] * 9					)
    __lowerCamelCase    				=     np.abs(mask_slice.flatten() - expected_slice					).max()
    self.assertLessEqual(__UpperCAmelCase						,					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(**__UpperCAmelCase					)
    pipe.to(__UpperCAmelCase					)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

    self.assertEqual(image.shape						,					(2, 32, 32, 3)					)
    __lowerCamelCase    				=     np.array(
        [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]						,					)
    __lowerCamelCase    				=     np.abs(image_slice.flatten() - expected_slice					).max()
    self.assertLessEqual(__UpperCAmelCase						,					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''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
    __lowerCamelCase    				=     DPMSolverMultistepScheduler(**__UpperCAmelCase					)
    __lowerCamelCase    				=     DPMSolverMultistepInverseScheduler(**__UpperCAmelCase					)

    __lowerCamelCase    				=     self.pipeline_class(**__UpperCAmelCase					)
    pipe.to(__UpperCAmelCase					)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

    self.assertEqual(image.shape						,					(2, 32, 32, 3)					)
    __lowerCamelCase    				=     np.array(
        [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]						,					)
    __lowerCamelCase    				=     np.abs(image_slice.flatten() - expected_slice					).max()
    self.assertLessEqual(__UpperCAmelCase						,					1E-3					)



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




 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((768, 768)					)

    __lowerCamelCase    				=     raw_image




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     torch.manual_seed(0					)

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

    __lowerCamelCase    				=     '''a bowl of fruit'''
    __lowerCamelCase    				=     '''a bowl of pears'''

    __lowerCamelCase    				=     pipe.generate_mask(
        image=self.raw_image						,					source_prompt=__UpperCAmelCase						,					target_prompt=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					)

    __lowerCamelCase    				=     pipe.invert(
        prompt=__UpperCAmelCase						,					image=self.raw_image						,					inpaint_strength=0.7						,					generator=__UpperCAmelCase					).latents

    __lowerCamelCase    				=     pipe(
        prompt=__UpperCAmelCase						,					mask_image=__UpperCAmelCase						,					image_latents=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					negative_prompt=__UpperCAmelCase						,					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((768, 768)					)					)
        / 255
    )
    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=__UpperCAmelCase						,					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=__UpperCAmelCase					)

    __lowerCamelCase    				=     '''a bowl of fruit'''
    __lowerCamelCase    				=     '''a bowl of pears'''

    __lowerCamelCase    				=     pipe.generate_mask(
        image=self.raw_image						,					source_prompt=__UpperCAmelCase						,					target_prompt=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					)

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

    __lowerCamelCase    				=     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]

    __lowerCamelCase    				=     (
        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











from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(
        features=__UpperCAmelCase						,					cache_dir=__UpperCAmelCase						,					keep_in_memory=__UpperCAmelCase						,					streaming=__UpperCAmelCase						,					num_proc=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     Generator(
        cache_dir=__UpperCAmelCase						,					features=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					gen_kwargs=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Build iterable dataset
    if self.streaming:
       __lowerCamelCase    				=     self.builder.as_streaming_dataset(split='''train'''					)
    # Build regular (map-style) dataset
    else:
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase						,					download_mode=__UpperCAmelCase						,					verification_mode=__UpperCAmelCase						,					base_path=__UpperCAmelCase						,					num_proc=self.num_proc						,					)
       __lowerCamelCase    				=     self.builder.as_dataset(
           split='''train'''						,					verification_mode=__UpperCAmelCase						,					in_memory=self.keep_in_memory					)
    return dataset






