





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL








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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   CycleDiffusionPipeline
      A_						:    List[Any]							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'negative_prompt',
          'height',
          'width',
          'negative_prompt_embeds',
      }
      A_						:    List[str]							=   PipelineTesterMixin.required_optional_params - {'latents'}
      A_						:    List[str]							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
      A_						:    List[str]							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
      A_						:    Any							=   IMAGE_TO_IMAGE_IMAGE_PARAMS


      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       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    ,      )
         __snake_case								=       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=a__    ,      set_alpha_to_one=a__    ,      )
         torch.manual_seed(0				)
         __snake_case								=       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				)
         __snake_case								=       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    ,      )
         __snake_case								=       CLIPTextModel(a__				)
         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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


      def        a			(self	:				List[str]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image / 2 + 0.5
         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''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	:				str				):





         """simple docstring"""



         __snake_case								=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

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

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

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

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       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"""



         __snake_case								=       self.get_dummy_components()
         for name, module in components.items():
            if hasattr(a__    ,      '''half'''				):
               __snake_case								=       module.half()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

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

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

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       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	:				Any				):





         """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	:				Any				):





         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def        a			(self	:				str				):





         """simple docstring"""



         return super().test_save_load_optional_components()


      @skip_mps
      def        a			(self	:				Dict				):





         """simple docstring"""



         return super().test_attention_slicing_forward_pass()






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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



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

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(
             a__    ,      scheduler=a__    ,      safety_checker=a__    ,      torch_dtype=torch.floataa    ,      revision='''fp16'''				)

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

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       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	:				Tuple				):





         """simple docstring"""



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

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

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

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

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

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

