




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					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  CycleDiffusionPipeline
     lowerCAmelCase    :	int       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'negative_prompt',
         'height',
         'width',
         'negative_prompt_embeds',
     }
     lowerCAmelCase    :	Tuple       		=  PipelineTesterMixin.required_optional_params - {'latents'}
     lowerCAmelCase    :	Tuple       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}      )
     lowerCAmelCase    :	List[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     lowerCAmelCase    :	Optional[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     def   lowerCamelCase_       (     self       :      int    ):
         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=0.00_085 ,   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    )
         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=10_00 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__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   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Dict ,   __snake_case       :      Union[str, Any]=0    ):
         UpperCAmelCase_										=					floats_tensor((1, 3, 32, 32) ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
         UpperCAmelCase_										=					image / 2 + 0.5
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__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   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					CycleDiffusionPipeline(**__snake_case    )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					pipe(**__snake_case    )
         UpperCAmelCase_										=					output.images

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

         assert images.shape == (1, 32, 32, 3)
         UpperCAmelCase_										=					np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
     @unittest.skipIf(torch_device != '''cuda''' ,   '''This test requires a GPU'''    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.get_dummy_components()
         for name, module in components.items():
             if hasattr(__snake_case ,   '''half'''    ):
                 UpperCAmelCase_										=					module.half()
         UpperCAmelCase_										=					CycleDiffusionPipeline(**__snake_case    )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					pipe(**__snake_case    )
         UpperCAmelCase_										=					output.images

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

         assert images.shape == (1, 32, 32, 3)
         UpperCAmelCase_										=					np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
     @skip_mps
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return super().test_save_load_local()
     @unittest.skip('''non-deterministic pipeline'''    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return super().test_inference_batch_single_identical()
     @skip_mps
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return super().test_dict_tuple_outputs_equivalent()
     @skip_mps
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return super().test_save_load_optional_components()




     @skip_mps
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return super().test_attention_slicing_forward_pass()



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

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     def   lowerCamelCase_       (     self       :      Tuple    ):
         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((5_12, 5_12)    )

         UpperCAmelCase_										=					'''CompVis/stable-diffusion-v1-4'''
         UpperCAmelCase_										=					DDIMScheduler.from_pretrained(__snake_case ,   subfolder='''scheduler'''    )
         UpperCAmelCase_										=					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()

         UpperCAmelCase_										=					'''A black colored car'''
         UpperCAmelCase_										=					'''A blue colored car'''

         UpperCAmelCase_										=					torch.manual_seed(0    )
         UpperCAmelCase_										=					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''' ,   )
         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   lowerCamelCase_       (     self       :      int    ):
         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((5_12, 5_12)    )

         UpperCAmelCase_										=					'''CompVis/stable-diffusion-v1-4'''
         UpperCAmelCase_										=					DDIMScheduler.from_pretrained(__snake_case ,   subfolder='''scheduler'''    )
         UpperCAmelCase_										=					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()

         UpperCAmelCase_										=					'''A black colored car'''
         UpperCAmelCase_										=					'''A blue colored car'''

         UpperCAmelCase_										=					torch.manual_seed(0    )
         UpperCAmelCase_										=					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''' ,   )
         UpperCAmelCase_										=					output.images

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











import numpy as np




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.array				)  ->							np.array:
    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





