





'''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     __lowerCAmelCase					(   lowercase_						,						lowercase_						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   StableDiffusionDiffEditPipeline
       _snake_case     :					Optional[Any]						    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       _snake_case     :					Union[str, Any]						    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       _snake_case     :					str						    =   frozenset(
           []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       _snake_case     :					Optional[Any]						    =   frozenset([]			)





       def snake_case__						(							self       :		Dict					)   ->					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=a__	,			)
            _UpperCamelCase       =	DDIMScheduler(
                beta_start=0.00085	,			beta_end=0.012	,			beta_schedule='''scaled_linear'''	,			clip_sample=a__	,			set_alpha_to_one=a__	,			)
            _UpperCamelCase       =	DDIMInverseScheduler(
                beta_start=0.00085	,			beta_end=0.012	,			beta_schedule='''scaled_linear'''	,			clip_sample=a__	,			set_alpha_to_zero=a__	,			)
            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-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			hidden_act='''gelu'''	,			projection_dim=512	,			)
            _UpperCamelCase       =	CLIPTextModel(a__					)
            _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 snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[int]=0					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	floats_tensor((1, 16, 16)	,			rng=random.Random(a__					)					).to(a__					)
            _UpperCamelCase       =	floats_tensor((1, 2, 4, 16, 16)	,			rng=random.Random(a__					)					).to(a__					)
            if str(a__					).startswith('''mps'''					):
                 _UpperCamelCase       =	torch.manual_seed(a__					)
            else:
                 _UpperCamelCase       =	torch.Generator(device=a__					).manual_seed(a__					)
            _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 snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		List[str]=0					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	floats_tensor((1, 3, 32, 32)	,			rng=random.Random(a__					)					).to(a__					)
            _UpperCamelCase       =	image.cpu().permute(0	,			2	,			3	,			1					)[0]
            _UpperCamelCase       =	Image.fromarray(np.uinta(a__					)					).convert('''RGB'''					)
            if str(a__					).startswith('''mps'''					):
                 _UpperCamelCase       =	torch.manual_seed(a__					)
            else:
                 _UpperCamelCase       =	torch.Generator(device=a__					).manual_seed(a__					)
            _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 snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[Any]=0					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	floats_tensor((1, 3, 32, 32)	,			rng=random.Random(a__					)					).to(a__					)
            _UpperCamelCase       =	image.cpu().permute(0	,			2	,			3	,			1					)[0]
            _UpperCamelCase       =	Image.fromarray(np.uinta(a__					)					).convert('''RGB'''					)
            if str(a__					).startswith('''mps'''					):
                 _UpperCamelCase       =	torch.manual_seed(a__					)
            else:
                 _UpperCamelCase       =	torch.Generator(device=a__					).manual_seed(a__					)
            _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 snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




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

            _UpperCamelCase       =	self.get_dummy_components()
            _UpperCamelCase       =	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}					)

            _UpperCamelCase       =	self.get_dummy_inputs(a__					)
            _UpperCamelCase       =	pipe(**a__					)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(a__					)
                 _UpperCamelCase       =	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."""	,			)

            _UpperCamelCase       =	self.get_dummy_inputs(a__					)
            _UpperCamelCase       =	pipe_loaded(**a__					)[0]

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





       def snake_case__						(							self       :		str					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	'''cpu'''

            _UpperCamelCase       =	self.get_dummy_components()
            _UpperCamelCase       =	self.pipeline_class(**a__					)
            pipe.to(a__					)
            pipe.set_progress_bar_config(disable=a__					)

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





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

            '''simple docstring'''




            _UpperCamelCase       =	'''cpu'''

            _UpperCamelCase       =	self.get_dummy_components()
            _UpperCamelCase       =	self.pipeline_class(**a__					)
            pipe.to(a__					)
            pipe.set_progress_bar_config(disable=a__					)

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

            self.assertEqual(image.shape	,			(2, 32, 32, 3)					)
            _UpperCamelCase       =	np.array(
                [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]	,			)
            _UpperCamelCase       =	np.abs(image_slice.flatten() - expected_slice					).max()
            self.assertLessEqual(a__	,			1e-3					)





       def snake_case__						(							self       :		int					)   ->					List[str]:

            '''simple docstring'''




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







       def snake_case__						(							self       :		Any					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	'''cpu'''

            _UpperCamelCase       =	self.get_dummy_components()

            _UpperCamelCase       =	{'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
            _UpperCamelCase       =	DPMSolverMultistepScheduler(**a__					)
            _UpperCamelCase       =	DPMSolverMultistepInverseScheduler(**a__					)

            _UpperCamelCase       =	self.pipeline_class(**a__					)
            pipe.to(a__					)
            pipe.set_progress_bar_config(disable=a__					)

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

            self.assertEqual(image.shape	,			(2, 32, 32, 3)					)
            _UpperCamelCase       =	np.array(
                [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]	,			)
            _UpperCamelCase       =	np.abs(image_slice.flatten() - expected_slice					).max()
            self.assertLessEqual(a__	,			1e-3					)

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






       """simple docstring"""





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

            '''simple docstring'''




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





       @classmethod
       def snake_case__						(							cls       :		int					)   ->					List[Any]:

            '''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((768, 768)					)

            _UpperCamelCase       =	raw_image





       def snake_case__						(							self       :		int					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	torch.manual_seed(0					)

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

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

            _UpperCamelCase       =	pipe.generate_mask(
                image=self.raw_image	,			source_prompt=a__	,			target_prompt=a__	,			generator=a__	,			)

            _UpperCamelCase       =	pipe.invert(
                prompt=a__	,			image=self.raw_image	,			inpaint_strength=0.7	,			generator=a__					).latents

            _UpperCamelCase       =	pipe(
                prompt=a__	,			mask_image=a__	,			image_latents=a__	,			generator=a__	,			negative_prompt=a__	,			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 snake_case__						(							self       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	torch.manual_seed(0					)

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

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

            _UpperCamelCase       =	pipe.generate_mask(
                image=self.raw_image	,			source_prompt=a__	,			target_prompt=a__	,			generator=a__	,			)

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

            _UpperCamelCase       =	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]

            _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'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



