





"""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 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__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  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						Optional[Any]			       =  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 :						Any			       =  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=_UpperCAmelCase	,			set_alpha_to_one=_UpperCAmelCase	,			)
									torch.manual_seed(0     )
									__a :						Dict			       =  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 :						Optional[Any]			       =  CLIPTextConfig(
									    bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			)
									__a :						List[str]			       =  CLIPTextModel(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=0     ):
									__a :						Optional[int]			       =  floats_tensor((1, 3, 32, 32)	,			rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
									__a :						List[str]			       =  image / 2 + 0.5
									if str(_UpperCAmelCase     ).startswith('''mps'''     ):
													__a :						Tuple			       =  torch.manual_seed(_UpperCAmelCase     )
									else:
													__a :						str			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  {
									    '''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     ):
									__a :						int			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

									__a :						Optional[Any]			       =  self.get_dummy_components()
									__a :						Dict			       =  CycleDiffusionPipeline(**_UpperCAmelCase     )
									__a :						Tuple			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Any			       =  pipe(**_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  output.images

									__a :						List[str]			       =  images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									__a :						Optional[Any]			       =  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  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  self.get_dummy_components()
									for name, module in components.items():
													if hasattr(_UpperCAmelCase	,			'''half'''     ):
																	__a :						List[Any]			       =  module.half()
									__a :						Tuple			       =  CycleDiffusionPipeline(**_UpperCAmelCase     )
									__a :						Optional[Any]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Optional[Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  pipe(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  output.images

									__a :						List[Any]			       =  images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									__a :						Tuple			       =  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  _lowerCamelCase		(						self     ):
									return super().test_save_load_local()







					@unittest.skip('''non-deterministic pipeline'''     )
					def  _lowerCamelCase		(						self     ):
									return super().test_inference_batch_single_identical()







					@skip_mps
					def  _lowerCamelCase		(						self     ):
									return super().test_dict_tuple_outputs_equivalent()







					@skip_mps
					def  _lowerCamelCase		(						self     ):
									return super().test_save_load_optional_components()







					@skip_mps
					def  _lowerCamelCase		(						self     ):
									return super().test_attention_slicing_forward_pass()






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







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''     )
									__a :						Union[str, Any]			       =  load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''     )
									__a :						int			       =  init_image.resize((512, 512)     )

									__a :						List[str]			       =  '''CompVis/stable-diffusion-v1-4'''
									__a :						Optional[Any]			       =  DDIMScheduler.from_pretrained(_UpperCAmelCase	,			subfolder='''scheduler'''     )
									__a :						str			       =  CycleDiffusionPipeline.from_pretrained(
									    _UpperCAmelCase	,			scheduler=_UpperCAmelCase	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa	,			revision='''fp16'''     )

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

									__a :						List[str]			       =  '''A black colored car'''
									__a :						Union[str, Any]			       =  '''A blue colored car'''

									__a :						Tuple			       =  torch.manual_seed(0     )
									__a :						Optional[Any]			       =  pipe(
									    prompt=_UpperCAmelCase	,			source_prompt=_UpperCAmelCase	,			image=_UpperCAmelCase	,			num_inference_steps=100	,			eta=0.1	,			strength=0.8_5	,			guidance_scale=3	,			source_guidance_scale=1	,			generator=_UpperCAmelCase	,			output_type='''np'''	,			)
									__a :						List[str]			       =  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     ):
									__a :						Dict			       =  load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''     )
									__a :						Optional[Any]			       =  load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''     )
									__a :						Any			       =  init_image.resize((512, 512)     )

									__a :						Tuple			       =  '''CompVis/stable-diffusion-v1-4'''
									__a :						Optional[Any]			       =  DDIMScheduler.from_pretrained(_UpperCAmelCase	,			subfolder='''scheduler'''     )
									__a :						Tuple			       =  CycleDiffusionPipeline.from_pretrained(_UpperCAmelCase	,			scheduler=_UpperCAmelCase	,			safety_checker=_UpperCAmelCase     )

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

									__a :						List[str]			       =  '''A black colored car'''
									__a :						List[str]			       =  '''A blue colored car'''

									__a :						int			       =  torch.manual_seed(0     )
									__a :						Union[str, Any]			       =  pipe(
									    prompt=_UpperCAmelCase	,			source_prompt=_UpperCAmelCase	,			image=_UpperCAmelCase	,			num_inference_steps=100	,			eta=0.1	,			strength=0.8_5	,			guidance_scale=3	,			source_guidance_scale=1	,			generator=_UpperCAmelCase	,			output_type='''np'''	,			)
									__a :						Dict			       =  output.images

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






"""simple docstring"""






def     __A      (			a_						:float   ,       a_						:float)		->		float:
				return price * (1 + tax_rate)


if __name__ == "__main__":
		print(F'{price_plus_tax(100, 0.25) = }')
		print(F'{price_plus_tax(125.50, 0.05) = }')