



'''simple docstring'''






from __future__ import annotations
def 				lowerCamelCase__	(						_A    ):
  a						: int					       =					0.00
  a						: Union[str, Any]					       =					0
  for resistor in resistors:
    if resistor <= 0:
      a						: Any					       =					f"""Resistor at index {index} has a negative or zero value!"""
      raise ValueError(_A    )
    first_sum += 1 / float(_A    )
    index += 1
  return 1 / first_sum
def 				lowerCamelCase__	(						_A    ):
  a						: List[Any]					       =					0.00
  a						: str					       =					0
  for resistor in resistors:
    sum_r += resistor
    if resistor < 0:
      a						: Dict					       =					f"""Resistor at index {index} has a negative value!"""
      raise ValueError(_A    )
    index += 1
  return sum_r


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''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       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Union[str, Any]					       =					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						,      )
      a						: str					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      )
      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						,      )
      torch.manual_seed(0     )
      a						: int					       =					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=10_00						,      )
      a						: List[str]					       =					CLIPTextModel(__snake_case     )
      a						: int					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Tuple					       =					{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Any=0     ):
      a						: str					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__snake_case     ).startswith('mps'     ):
        a						: List[str]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Union[str, Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: List[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       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[Any]					       =					'cpu'  # ensure determinism for the device-dependent torch.Generator

      a						: int					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: List[str]					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

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

      a						: Optional[Any]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Tuple					       =					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       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__snake_case						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Tuple					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: Any					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					self.get_dummy_inputs(__snake_case     )
      a						: int					       =					pipe(**__snake_case     )
      a						: Optional[int]					       =					output.images

      a						: Tuple					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: int					       =					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       lowercase_	(				self			:  List[Any]     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Dict     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_attention_slicing_forward_pass()

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







    def       lowercase_	(				self			:  Tuple     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: Optional[int]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[str]					       =					init_image.resize((5_12, 5_12)     )

      a						: Dict					       =					'CompVis/stable-diffusion-v1-4'
      a						: List[str]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: Any					       =					CycleDiffusionPipeline.from_pretrained(
          __snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case						,      torch_dtype=torch.floataa						,      revision='fp16'     )

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

      a						: Union[str, Any]					       =					'A black colored car'
      a						: Optional[Any]					       =					'A blue colored car'

      a						: int					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: 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       lowercase_	(				self			:  int     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: str					       =					init_image.resize((5_12, 5_12)     )

      a						: Optional[int]					       =					'CompVis/stable-diffusion-v1-4'
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: str					       =					CycleDiffusionPipeline.from_pretrained(__snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case     )

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

      a						: Tuple					       =					'A black colored car'
      a						: Tuple					       =					'A blue colored car'

      a						: List[str]					       =					torch.manual_seed(0     )
      a						: str					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Tuple					       =					output.images

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