

def 					_lowercase     (				a_				:					str					,a_				:					int				)				->	list:




     '''simple docstring'''







     __magic_name__							=    word.split()

     def justify(a_				:					list					,a_				:					int					,a_				:					int				) -> str:
          __magic_name__							=    max_width - width
          __magic_name__							=    len(a_				)
          if len(a_				) == 1:
               # if there is only word in line
               # just insert overall_spaces_count for the remainder of line
               return line[0] + " " * overall_spaces_count
          else:
               __magic_name__							=    words_count - 1
               # num_spaces_between_words_list[i] : tells you to insert
               # num_spaces_between_words_list[i] spaces
               # after word on line[i]
               __magic_name__							=    spaces_to_insert_between_words * [
                   overall_spaces_count // spaces_to_insert_between_words
               ]
               __magic_name__							=    (
                   overall_spaces_count % spaces_to_insert_between_words
               )
               # distribute spaces via round robin to the left words
               for i in range(a_				):
                    num_spaces_between_words_list[i] += 1
               __magic_name__							=    []
               for i in range(a_				):
                    # add the word
                    aligned_words_list.append(line[i]				)
                    # add the spaces to insert
                    aligned_words_list.append(num_spaces_between_words_list[i] * ' '				)
               # just add the last word to the sentence
               aligned_words_list.append(line[-1]				)
               # join the aligned words list to form a justified line
               return "".join(a_				)

     __magic_name__							=    []
     __magic_name__							=    []
     __magic_name__							=    0
     for word in words:
          if width + len(a_				) + len(a_				) <= max_width:
               # keep adding words until we can fill out max_width
               # width = sum of length of all words (without overall_spaces_count)
               # len(word) = length of current word
               # len(line) = number of overall_spaces_count to insert between words
               line.append(a_				)
               width += len(a_				)
          else:
               # justify the line and add it to result
               answer.append(justify(a_					,a_					,a_				)				)
               # reset new line and new width
               __magic_name__,	__magic_name__							=    [word], len(a_				)
     __magic_name__							=    max_width - width - len(a_				)
     answer.append(' '.join(a_				) + (remaining_spaces + 1) * ' '				)
     return answer


if __name__ == "__main__":
      from doctest import testmod

      testmod()







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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Dict       				=  StableDiffusionDiffEditPipeline
	_lowercase  :   Dict       				=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
	_lowercase  :   Dict       				=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
	_lowercase  :   str       				=  frozenset(
	    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowercase  :   int       				=  frozenset([]			)







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    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  , )
						__magic_name__							=    DDIMScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_one=__UpperCamelCase  , )
						__magic_name__							=    DDIMInverseScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_zero=__UpperCamelCase  , )
						torch.manual_seed(0      )
						__magic_name__							=    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      )
						__magic_name__							=    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  , )
						__magic_name__							=    CLIPTextModel(__UpperCamelCase      )
						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						__magic_name__							=    {
						    '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: Optional[int]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, 2, 4, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: str  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: Union[str, Any]      ):

						'''simple docstring'''





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

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    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}      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe(**__UpperCamelCase      )[0]

						with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(__UpperCamelCase      )
											__magic_name__							=    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.'  , )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe_loaded(**__UpperCamelCase      )[0]

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()

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

						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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




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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'      )

						__magic_name__							=    raw_image.convert('RGB'      ).resize((7_68, 7_68)      )

						__magic_name__							=    raw_image







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

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

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

						__magic_name__							=    pipe.invert(
						    prompt=__UpperCamelCase  , image=self.raw_image  , inpaint_strength=0.7  , generator=__UpperCamelCase      ).latents

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

						__magic_name__							=    (
						    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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

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

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

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

						__magic_name__							=    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]

						__magic_name__							=    (
						    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




