


'''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       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		CycleDiffusionPipeline
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
 }
 _lowerCAmelCase	   =		PipelineTesterMixin.required_optional_params - {"""latents"""}
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}    )
 _lowerCAmelCase	   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 _lowerCAmelCase	   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 							__UpperCAmelCase						(	self  )   ->							int:
      torch.manual_seed(0  )
      _a      =							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      =							DDIMScheduler(
          beta_start=0.0_0_0_8_5 ,			beta_end=0.0_1_2 ,			beta_schedule='scaled_linear' ,			num_train_timesteps=10_00 ,			clip_sample=__magic_name__ ,			set_alpha_to_one=__magic_name__ ,			)
      torch.manual_seed(0  )
      _a      =							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      =							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 ,			)
      _a      =							CLIPTextModel(__magic_name__  )
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

      _a      =							{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=0  )   ->							Dict:
      _a      =							floats_tensor((1, 3, 32, 32) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )
      _a      =							image / 2 + 0.5
      if str(__magic_name__  ).startswith('mps'  ):
           _a      =							torch.manual_seed(__magic_name__  )
      else:
           _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a      =							{
          '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  )   ->							int:
      _a      =							'cpu'  # ensure determinism for the device-dependent torch.Generator

      _a      =							self.get_dummy_components()
      _a      =							CycleDiffusionPipeline(**__magic_name__  )
      _a      =							pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							self.get_dummy_inputs(__magic_name__  )
      _a      =							pipe(**__magic_name__  )
      _a      =							output.images

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

      assert images.shape == (1, 32, 32, 3)
      _a      =							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  )   ->							List[Any]:
      _a      =							self.get_dummy_components()
      for name, module in components.items():
           if hasattr(__magic_name__ ,			'half'  ):
                _a      =							module.half()
      _a      =							CycleDiffusionPipeline(**__magic_name__  )
      _a      =							pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							self.get_dummy_inputs(__magic_name__  )
      _a      =							pipe(**__magic_name__  )
      _a      =							output.images

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

      assert images.shape == (1, 32, 32, 3)
      _a      =							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  )   ->							List[str]:
      return super().test_save_load_local()
 @unittest.skip('non-deterministic pipeline'  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      return super().test_inference_batch_single_identical()
 @skip_mps
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return super().test_save_load_optional_components()




 @skip_mps
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'  )
      _a      =							init_image.resize((5_12, 5_12)  )

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

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

      _a      =							'A black colored car'
      _a      =							'A blue colored car'

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			source_prompt=__magic_name__ ,			image=__magic_name__ ,			num_inference_steps=1_00 ,			eta=0.1 ,			strength=0.8_5 ,			guidance_scale=3 ,			source_guidance_scale=1 ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							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  )   ->							Optional[Any]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'  )
      _a      =							init_image.resize((5_12, 5_12)  )

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

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

      _a      =							'A black colored car'
      _a      =							'A blue colored car'

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			source_prompt=__magic_name__ ,			image=__magic_name__ ,			num_inference_steps=1_00 ,			eta=0.1 ,			strength=0.8_5 ,			guidance_scale=3 ,			source_guidance_scale=1 ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images

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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

