


'''simple docstring'''





def     __lowerCAmelCase						(							snake_case	:    List[str]						,      snake_case	:    int )       ->							Dict:
   __lowerCamelCase:				int     =	word.split()

   def justify(snake_case	:    List[Any]						,      snake_case	:    Optional[Any]						,      snake_case	:    Optional[int] ) -> str:
      __lowerCamelCase:				List[str]     =	max_width - width
      __lowerCamelCase:				Any     =	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:
         __lowerCamelCase:				Optional[Any]     =	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]
         __lowerCamelCase:				str     =	spaces_to_insert_between_words * [
             overall_spaces_count // spaces_to_insert_between_words
         ]
         __lowerCamelCase:				Union[str, Any]     =	(
             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
         __lowerCamelCase:				Any     =	[]
         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 )

   __lowerCamelCase:				Optional[Any]     =	[]
   __lowerCamelCase:				list[str]     =	[]
   __lowerCamelCase:				Any     =	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
         __lowerCamelCase:				List[str]     =	[word], len(__A )
   __lowerCamelCase:				List[Any]     =	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 unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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    ,	)
        __lowerCamelCase:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				str     =	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    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Optional[int]     =	{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """prompt""": """An astronaut riding an elephant""",
            """source_prompt""": """An astronaut riding a horse""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """eta""": 0.1,
            """strength""": 0.8,
            """guidance_scale""": 3,
            """source_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

        __lowerCamelCase:				int     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





     @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[str]     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_save_load_local()





     @unittest.skip("""non-deterministic pipeline"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return super().test_inference_batch_single_identical()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        return super().test_save_load_optional_components()







     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_attention_slicing_forward_pass()



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Any     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
        __lowerCamelCase:				str     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	CycleDiffusionPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa    ,	revision="""fp16"""							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

        __lowerCamelCase:				List[Any]     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image							).max() < 5E-1







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
        __lowerCamelCase:				List[str]     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

        __lowerCamelCase:				str     =	torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Any     =	output.images

        assert np.abs(image - expected_image							).max() < 2E-2


