


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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:List[str]										=	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 ,  )
       a						:Any										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       a						:Any										=	DDIMInverseScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  clip_sample=_lowerCamelCase ,  set_alpha_to_zero=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:List[str]										=	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						:List[str]										=	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=1000 ,  hidden_act='''gelu''' ,  projection_dim=512 ,  )
       a						:List[Any]										=	CLIPTextModel(_lowerCamelCase   )
       a						:Any										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

       a						:int										=	self.get_dummy_components()
       a						:int										=	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}   )

       a						:Union[str, Any]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase   )
             a						:List[Any]										=	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.''' ,  )

       a						:Any										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:List[str]										=	pipe_loaded(**_lowerCamelCase   )[0]

       a						:List[Any]										=	np.abs(output - output_loaded   ).max()
       self.assertLess(_lowerCamelCase ,  1e-4   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	'''cpu'''

       a						:Tuple										=	self.get_dummy_components()
       a						:Optional[int]										=	self.pipeline_class(**_lowerCamelCase   )
       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Dict										=	self.get_dummy_mask_inputs(_lowerCamelCase   )
       a						:Union[str, Any]										=	pipe.generate_mask(**_lowerCamelCase   )
       a						:Tuple										=	mask[0, -3:, -3:]

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''

       a						:Tuple										=	self.get_dummy_components()
       a						:Tuple										=	self.pipeline_class(**_lowerCamelCase   )
       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

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

       self.assertEqual(image.shape ,  (2, 32, 32, 3)   )
       a						:Dict										=	np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,  )
       a						:int										=	np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_lowerCamelCase ,  1e-3   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().test_inference_batch_single_identical(expected_max_diff=5e-3   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	'''cpu'''

       a						:Any										=	self.get_dummy_components()

       a						:str										=	{'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
       a						:Union[str, Any]										=	DPMSolverMultistepScheduler(**_lowerCamelCase   )
       a						:int										=	DPMSolverMultistepInverseScheduler(**_lowerCamelCase   )

       a						:Optional[Any]										=	self.pipeline_class(**_lowerCamelCase   )
       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:str										=	self.get_dummy_inversion_inputs(_lowerCamelCase   )
       a						:int										=	pipe.invert(**_lowerCamelCase   ).images
       a						:Union[str, Any]										=	image[0, -1, -3:, -3:]

       self.assertEqual(image.shape ,  (2, 32, 32, 3)   )
       a						:Tuple										=	np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,  )
       a						:Union[str, Any]										=	np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_lowerCamelCase ,  1e-3   )







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 @classmethod
 def 	SCREAMING_SNAKE_CASE__    (		cls   ):
       a						:int										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''   )

       a						:Optional[int]										=	raw_image.convert('''RGB'''   ).resize((768, 768)   )

       a						:List[Any]										=	raw_image




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	torch.manual_seed(0   )

       a						:Optional[Any]										=	StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1''' ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa   )
       a						:Dict										=	DDIMScheduler.from_config(pipe.scheduler.config   )
       a						:Dict										=	DDIMInverseScheduler.from_config(pipe.scheduler.config   )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Union[str, Any]										=	'''a bowl of fruit'''
       a						:str										=	'''a bowl of pears'''

       a						:Any										=	pipe.generate_mask(
           image=self.raw_image ,  source_prompt=_lowerCamelCase ,  target_prompt=_lowerCamelCase ,  generator=_lowerCamelCase ,  )

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

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

       a						:int										=	(
           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   ):
       a						:Optional[int]										=	torch.manual_seed(0   )

       a						:List[Any]										=	StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1''' ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa   )
       a						:Dict										=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
       a						:Dict										=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	'''a bowl of fruit'''
       a						:List[Any]										=	'''a bowl of pears'''

       a						:Optional[int]										=	pipe.generate_mask(
           image=self.raw_image ,  source_prompt=_lowerCamelCase ,  target_prompt=_lowerCamelCase ,  generator=_lowerCamelCase ,  )

       a						:Optional[Any]										=	pipe.invert(
           prompt=_lowerCamelCase ,  image=self.raw_image ,  inpaint_strength=0.7 ,  generator=_lowerCamelCase ,  num_inference_steps=25 ,  ).latents

       a						:Any										=	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]

       a						:Optional[int]										=	(
           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						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





