




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(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		CycleDiffusionPipeline
 UpperCamelCase		     =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
 }
 UpperCamelCase		     =		PipelineTesterMixin.required_optional_params - {'''latents'''}
 UpperCamelCase		     =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}     )
 UpperCamelCase		     =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase		     =		IMAGE_TO_IMAGE_IMAGE_PARAMS

 def        a__				(       self      :						int						)     ->			Any:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					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		,							)
       lowerCamelCase_	  =					DDIMScheduler(
           beta_start=0.00085		,							beta_end=0.012		,							beta_schedule='scaled_linear'		,							num_train_timesteps=1000		,							clip_sample=A_		,							set_alpha_to_one=A_		,							)
       torch.manual_seed(0						)
       lowerCamelCase_	  =					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						)
       lowerCamelCase_	  =					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		,							)
       lowerCamelCase_	  =					CLIPTextModel(A_						)
       lowerCamelCase_	  =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

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

 def        a__				(       self      :						str		,							A_      :						List[str]		,							A_      :						Optional[Any]=0						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					floats_tensor((1, 3, 32, 32)		,							rng=random.Random(A_						)						).to(A_						)
       lowerCamelCase_	  =					image / 2 + 0.5
       if str(A_						).startswith('mps'						):
             lowerCamelCase_	  =					torch.manual_seed(A_						)
       else:
             lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(A_						)
       lowerCamelCase_	  =					{
           '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      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






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

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

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

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

       assert images.shape == (1, 32, 32, 3)
       lowerCamelCase_	  =					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        a__				(       self      :						int						)     ->			int:

       """simple docstring"""






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

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

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

       assert images.shape == (1, 32, 32, 3)
       lowerCamelCase_	  =					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        a__				(       self      :						Tuple						)     ->			int:

       """simple docstring"""






       return super().test_save_load_local()

 @unittest.skip('non-deterministic pipeline'						)
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       return super().test_inference_batch_single_identical()

 @skip_mps
 def        a__				(       self      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def        a__				(       self      :						Optional[int]						)     ->			List[Any]:

       """simple docstring"""






       return super().test_save_load_optional_components()





 @skip_mps
 def        a__				(       self      :						Any						)     ->			Tuple:

       """simple docstring"""






       return super().test_attention_slicing_forward_pass()







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



 '''simple docstring'''


 def        a__				(       self      :						int						)     ->			List[Any]:

       """simple docstring"""






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

 def        a__				(       self      :						List[str]						)     ->			int:

       """simple docstring"""






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

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

       lowerCamelCase_	  =					'A black colored car'
       lowerCamelCase_	  =					'A blue colored car'

       lowerCamelCase_	  =					torch.manual_seed(0						)
       lowerCamelCase_	  =					pipe(
           prompt=A_		,							source_prompt=A_		,							image=A_		,							num_inference_steps=100		,							eta=0.1		,							strength=0.85		,							guidance_scale=3		,							source_guidance_scale=1		,							generator=A_		,							output_type='np'		,							)
       lowerCamelCase_	  =					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						)     ->			Optional[Any]:

       """simple docstring"""






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

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

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

       lowerCamelCase_	  =					'A black colored car'
       lowerCamelCase_	  =					'A blue colored car'

       lowerCamelCase_	  =					torch.manual_seed(0						)
       lowerCamelCase_	  =					pipe(
           prompt=A_		,							source_prompt=A_		,							image=A_		,							num_inference_steps=100		,							eta=0.1		,							strength=0.85		,							guidance_scale=3		,							source_guidance_scale=1		,							generator=A_		,							output_type='np'		,							)
       lowerCamelCase_	  =					output.images

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









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



