





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




  def 				UpperCAmelCase__   (self      :	Dict				)					-> Dict:
      torch.manual_seed(0				)
      lowercase         = UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)							,			layers_per_block=2							,			sample_size=3_2							,			in_channels=4							,			out_channels=4							,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,			cross_attention_dim=3_2							,			)
      lowercase         = DDIMScheduler(
          beta_start=0.0_0_0_8_5							,			beta_end=0.0_1_2							,			beta_schedule="scaled_linear"							,			num_train_timesteps=1_0_0_0							,			clip_sample=lowerCamelCase_							,			set_alpha_to_one=lowerCamelCase_							,			)
      torch.manual_seed(0				)
      lowercase         = AutoencoderKL(
          block_out_channels=[3_2, 6_4]							,			in_channels=3							,			out_channels=3							,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,			latent_channels=4							,			)
      torch.manual_seed(0				)
      lowercase         = CLIPTextConfig(
          bos_token_id=0							,			eos_token_id=2							,			hidden_size=3_2							,			intermediate_size=3_7							,			layer_norm_eps=1e-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1_0_0_0							,			)
      lowercase         = CLIPTextModel(lowerCamelCase_				)
      lowercase         = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

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




  def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	str							,			A__      :	Dict=0				)					-> List[str]:
      lowercase         = floats_tensor((1, 3, 3_2, 3_2)							,			rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
      lowercase         = image / 2 + 0.5
      if str(lowerCamelCase_				).startswith("mps"				):
          lowercase         = torch.manual_seed(lowerCamelCase_				)
      else:
          lowercase         = torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
      lowercase         = {
          """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 				UpperCAmelCase__   (self      :	str				)					-> Optional[Any]:
      lowercase         = """cpu"""  # ensure determinism for the device-dependent torch.Generator

      lowercase         = self.get_dummy_components()
      lowercase         = CycleDiffusionPipeline(**lowerCamelCase_				)
      lowercase         = pipe.to(lowerCamelCase_				)
      pipe.set_progress_bar_config(disable=lowerCamelCase_				)

      lowercase         = self.get_dummy_inputs(lowerCamelCase_				)
      lowercase         = pipe(**lowerCamelCase_				)
      lowercase         = output.images

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

      assert images.shape == (1, 3_2, 3_2, 3)
      lowercase         = 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 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Dict:
      lowercase         = self.get_dummy_components()
      for name, module in components.items():
          if hasattr(lowerCamelCase_							,			"half"				):
              lowercase         = module.half()
      lowercase         = CycleDiffusionPipeline(**lowerCamelCase_				)
      lowercase         = pipe.to(lowerCamelCase_				)
      pipe.set_progress_bar_config(disable=lowerCamelCase_				)

      lowercase         = self.get_dummy_inputs(lowerCamelCase_				)
      lowercase         = pipe(**lowerCamelCase_				)
      lowercase         = output.images

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

      assert images.shape == (1, 3_2, 3_2, 3)
      lowercase         = 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 				UpperCAmelCase__   (self      :	int				)					-> Dict:
      return super().test_save_load_local()




  @unittest.skip("non-deterministic pipeline"				)
  def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
      return super().test_inference_batch_single_identical()




  @skip_mps
  def 				UpperCAmelCase__   (self      :	List[str]				)					-> Dict:
      return super().test_dict_tuple_outputs_equivalent()




  @skip_mps
  def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[Any]:
      return super().test_save_load_optional_components()





  @skip_mps
  def 				UpperCAmelCase__   (self      :	Any				)					-> Tuple:
      return super().test_attention_slicing_forward_pass()




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




  def 				UpperCAmelCase__   (self      :	str				)					-> str:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




  def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[str]:
      lowercase         = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/cycle-diffusion/black_colored_car.png"				)
      lowercase         = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"				)
      lowercase         = init_image.resize((5_1_2, 5_1_2)				)

      lowercase         = """CompVis/stable-diffusion-v1-4"""
      lowercase         = DDIMScheduler.from_pretrained(lowerCamelCase_							,			subfolder="scheduler"				)
      lowercase         = CycleDiffusionPipeline.from_pretrained(
          lowerCamelCase_							,			scheduler=lowerCamelCase_							,			safety_checker=lowerCamelCase_							,			torch_dtype=torch.floataa							,			revision="fp16"				)

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

      lowercase         = """A black colored car"""
      lowercase         = """A blue colored car"""

      lowercase         = torch.manual_seed(0				)
      lowercase         = pipe(
          prompt=lowerCamelCase_							,			source_prompt=lowerCamelCase_							,			image=lowerCamelCase_							,			num_inference_steps=1_0_0							,			eta=0.1							,			strength=0.8_5							,			guidance_scale=3							,			source_guidance_scale=1							,			generator=lowerCamelCase_							,			output_type="np"							,			)
      lowercase         = 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 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
      lowercase         = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/cycle-diffusion/black_colored_car.png"				)
      lowercase         = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"				)
      lowercase         = init_image.resize((5_1_2, 5_1_2)				)

      lowercase         = """CompVis/stable-diffusion-v1-4"""
      lowercase         = DDIMScheduler.from_pretrained(lowerCamelCase_							,			subfolder="scheduler"				)
      lowercase         = CycleDiffusionPipeline.from_pretrained(lowerCamelCase_							,			scheduler=lowerCamelCase_							,			safety_checker=lowerCamelCase_				)

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

      lowercase         = """A black colored car"""
      lowercase         = """A blue colored car"""

      lowercase         = torch.manual_seed(0				)
      lowercase         = pipe(
          prompt=lowerCamelCase_							,			source_prompt=lowerCamelCase_							,			image=lowerCamelCase_							,			num_inference_steps=1_0_0							,			eta=0.1							,			strength=0.8_5							,			guidance_scale=3							,			source_guidance_scale=1							,			generator=lowerCamelCase_							,			output_type="np"							,			)
      lowercase         = output.images

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











'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase : Optional[Any]											=			logging.get_logger(__name__)

__lowerCamelCase : Optional[Any]											=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''vit_mae'''




		def __init__(self      :	Dict							,			A__      :	Tuple=7_6_8							,			A__      :	str=1_2							,			A__      :	Dict=1_2							,			A__      :	Optional[Any]=3_0_7_2							,			A__      :	Union[str, Any]="gelu"							,			A__      :	Optional[int]=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Optional[Any]=1e-12							,			A__      :	Tuple=2_2_4							,			A__      :	str=1_6							,			A__      :	Optional[int]=3							,			A__      :	List[Any]=True							,			A__      :	Dict=1_6							,			A__      :	Dict=5_1_2							,			A__      :	Dict=8							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	str=0.7_5							,			A__      :	str=False							,			**A__      :	Any							,			)					-> Union[str, Any]:
						super().__init__(**A__				)

						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = qkv_bias
						lowercase         = decoder_num_attention_heads
						lowercase         = decoder_hidden_size
						lowercase         = decoder_num_hidden_layers
						lowercase         = decoder_intermediate_size
						lowercase         = mask_ratio
						lowercase         = norm_pix_loss




