


"""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       __A				(       _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    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       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     torch.manual_seed(0						)
     a 		=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=__A ,    )
     a 		=DDIMScheduler(
         beta_start=0.00_085 ,    beta_end=0.012 ,    beta_schedule='''scaled_linear''' ,    clip_sample=__A ,    set_alpha_to_one=__A ,    )
     a 		=DDIMInverseScheduler(
         beta_start=0.00_085 ,    beta_end=0.012 ,    beta_schedule='''scaled_linear''' ,    clip_sample=__A ,    set_alpha_to_zero=__A ,    )
     torch.manual_seed(0						)
     a 		=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						)
     a 		=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 ,    )
     a 		=CLIPTextModel(__A						)
     a 		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

     a 		={
         '''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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=0						)					->     str:
     a 		=floats_tensor((1, 16, 16) ,    rng=random.Random(__A						)						).to(__A						)
     a 		=floats_tensor((1, 2, 4, 16, 16) ,    rng=random.Random(__A						)						).to(__A						)
     if str(__A						).startswith('''mps'''						):
         a 		=torch.manual_seed(__A						)
     else:
         a 		=torch.Generator(device=__A						).manual_seed(__A						)
     a 		={
         '''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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=0						)					->     Optional[Any]:
     a 		=floats_tensor((1, 3, 32, 32) ,    rng=random.Random(__A						)						).to(__A						)
     a 		=image.cpu().permute(0 ,    2 ,    3 ,    1						)[0]
     a 		=Image.fromarray(np.uinta(__A						)						).convert('''RGB'''						)
     if str(__A						).startswith('''mps'''						):
         a 		=torch.manual_seed(__A						)
     else:
         a 		=torch.Generator(device=__A						).manual_seed(__A						)
     a 		={
         '''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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=0						)					->     str:
     a 		=floats_tensor((1, 3, 32, 32) ,    rng=random.Random(__A						)						).to(__A						)
     a 		=image.cpu().permute(0 ,    2 ,    3 ,    1						)[0]
     a 		=Image.fromarray(np.uinta(__A						)						).convert('''RGB'''						)
     if str(__A						).startswith('''mps'''						):
         a 		=torch.manual_seed(__A						)
     else:
         a 		=torch.Generator(device=__A						).manual_seed(__A						)
     a 		={
         '''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       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     if not hasattr(self.pipeline_class ,    '''_optional_components'''						):
         return

     a 		=self.get_dummy_components()
     a 		=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

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

     a 		=self.get_dummy_inputs(__A						)
     a 		=pipe(**__A						)[0]

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

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

     a 		=self.get_dummy_inputs(__A						)
     a 		=pipe_loaded(**__A						)[0]

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		='''cpu'''

     a 		=self.get_dummy_components()
     a 		=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

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

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		='''cpu'''

     a 		=self.get_dummy_components()
     a 		=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

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

     self.assertEqual(image.shape ,    (2, 32, 32, 3)						)
     a 		=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] ,    )
     a 		=np.abs(image_slice.flatten() - expected_slice						).max()
     self.assertLessEqual(__A ,    1E-3						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     super().test_inference_batch_single_identical(expected_max_diff=5E-3						)





 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		='''cpu'''

     a 		=self.get_dummy_components()

     a 		={'''beta_start''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
     a 		=DPMSolverMultistepScheduler(**__A						)
     a 		=DPMSolverMultistepInverseScheduler(**__A						)

     a 		=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

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

     self.assertEqual(image.shape ,    (2, 32, 32, 3)						)
     a 		=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] ,    )
     a 		=np.abs(image_slice.flatten() - expected_slice						).max()
     self.assertLessEqual(__A ,    1E-3						)






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






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 @classmethod
 def       SCREAMING_SNAKE_CASE							(		cls						)					->     List[Any]:
     a 		=load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''						)

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

     a 		=raw_image


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=torch.manual_seed(0						)

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

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

     a 		=pipe.generate_mask(
         image=self.raw_image ,    source_prompt=__A ,    target_prompt=__A ,    generator=__A ,    )

     a 		=pipe.invert(
         prompt=__A ,    image=self.raw_image ,    inpaint_strength=0.7 ,    generator=__A						).latents

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

     a 		=(
         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       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=torch.manual_seed(0						)

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

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

     a 		=pipe.generate_mask(
         image=self.raw_image ,    source_prompt=__A ,    target_prompt=__A ,    generator=__A ,    )

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

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

     a 		=(
         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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def        _UpperCamelCase							(      *lowercase__    ,       lowercase__ = None    ,       lowercase__=True    ,       lowercase__=2      ):
						from .. import __version__

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		take_from
						__SCREAMING_SNAKE_CASE						:     List[str]					=		()
						if not isinstance(args[0]    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(args,)

						for attribute, version_name, message in args:
												if version.parse(version.parse(lowercase__      ).base_version      ) >= version.parse(lowercase__      ):
																		raise ValueError(
																		    F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
																		    F''' version {__version__} is >= {version_name}'''      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
												if isinstance(lowercase__    ,       lowercase__      ) and attribute in deprecated_kwargs:
																		values += (deprecated_kwargs.pop(lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
												elif hasattr(lowercase__    ,       lowercase__      ):
																		values += (getattr(lowercase__    ,       lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
												elif deprecated_kwargs is None:
																		__SCREAMING_SNAKE_CASE						:     str					=		F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

												if warning is not None:
																		__SCREAMING_SNAKE_CASE						:     Any					=		warning + ''' ''' if standard_warn else ''''''
																		warnings.warn(warning + message    ,       lowercase__    ,       stacklevel=lowercase__      )

						if isinstance(lowercase__    ,       lowercase__      ) and len(lowercase__      ) > 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inspect.getouterframes(inspect.currentframe()      )[1]
												__SCREAMING_SNAKE_CASE						:     Dict					=		call_frame.filename
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		call_frame.lineno
												__SCREAMING_SNAKE_CASE						:     int					=		call_frame.function
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		next(iter(deprecated_kwargs.items()      )      )
												raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''      )

						if len(lowercase__      ) == 0:
												return
						elif len(lowercase__      ) == 1:
												return values[0]
						return values
