






'''simple docstring'''

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_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   CycleDiffusionPipeline
  lowercase  	=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      """negative_prompt""",
      """height""",
      """width""",
      """negative_prompt_embeds""",
  }
  lowercase  	=   PipelineTesterMixin.required_optional_params - {"""latents"""}
  lowercase  	=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}       )
  lowercase  	=   IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowercase  	=   IMAGE_TO_IMAGE_IMAGE_PARAMS

  def 						A__		(   self   )						->   int:



        """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    ,	)
        UpperCamelCase       						=      DDIMScheduler(
            beta_start=0.0_0_0_8_5    ,	beta_end=0.0_1_2    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=_SCREAMING_SNAKE_CASE    ,	set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,	)
        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    ,	)
        torch.manual_seed(0   )
        UpperCamelCase       						=      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    ,	)
        UpperCamelCase       						=      CLIPTextModel(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

        UpperCamelCase       						=      {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(_SCREAMING_SNAKE_CASE   )   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      image / 2 + 0.5
        if str(_SCREAMING_SNAKE_CASE   ).startswith("""mps"""   ):
              UpperCamelCase       						=      torch.manual_seed(_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      torch.Generator(device=_SCREAMING_SNAKE_CASE   ).manual_seed(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      {
            """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 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator

        UpperCamelCase       						=      self.get_dummy_components()
        UpperCamelCase       						=      CycleDiffusionPipeline(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      pipe.to(_SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      pipe(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      output.images

        UpperCamelCase       						=      images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]   )

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

  @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""   )
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_dummy_components()
        for name, module in components.items():
              if hasattr(_SCREAMING_SNAKE_CASE    ,	"""half"""   ):
                    UpperCamelCase       						=      module.half()
        UpperCamelCase       						=      CycleDiffusionPipeline(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      pipe.to(_SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      pipe(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      output.images

        UpperCamelCase       						=      images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]   )

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

  @skip_mps
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return super().test_save_load_local()

  @unittest.skip("""non-deterministic pipeline"""   )
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return super().test_inference_batch_single_identical()

  @skip_mps
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return super().test_dict_tuple_outputs_equivalent()

  @skip_mps
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        return super().test_save_load_optional_components()


  @skip_mps
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return super().test_attention_slicing_forward_pass()






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

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



        """simple docstring"""






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

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""   )
        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""   )
        UpperCamelCase       						=      init_image.resize((512, 512)   )

        UpperCamelCase       						=      """CompVis/stable-diffusion-v1-4"""
        UpperCamelCase       						=      DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE    ,	subfolder="""scheduler"""   )
        UpperCamelCase       						=      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()

        UpperCamelCase       						=      """A black colored car"""
        UpperCamelCase       						=      """A blue colored car"""

        UpperCamelCase       						=      torch.manual_seed(0   )
        UpperCamelCase       						=      pipe(
            prompt=_SCREAMING_SNAKE_CASE    ,	source_prompt=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.8_5    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=_SCREAMING_SNAKE_CASE    ,	output_type="""np"""    ,	)
        UpperCamelCase       						=      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 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""   )
        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""   )
        UpperCamelCase       						=      init_image.resize((512, 512)   )

        UpperCamelCase       						=      """CompVis/stable-diffusion-v1-4"""
        UpperCamelCase       						=      DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE    ,	subfolder="""scheduler"""   )
        UpperCamelCase       						=      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()

        UpperCamelCase       						=      """A black colored car"""
        UpperCamelCase       						=      """A blue colored car"""

        UpperCamelCase       						=      torch.manual_seed(0   )
        UpperCamelCase       						=      pipe(
            prompt=_SCREAMING_SNAKE_CASE    ,	source_prompt=_SCREAMING_SNAKE_CASE    ,	image=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.8_5    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=_SCREAMING_SNAKE_CASE    ,	output_type="""np"""    ,	)
        UpperCamelCase       						=      output.images

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








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


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

     testmod()
