


'''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 _lowerCamelCase			(		lowercase_						,	lowercase_						,	unittest.TestCase				):



		'''simple docstring'''







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







		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					Tuple:
			torch.manual_seed(0     )
			__magic_name__			:       Tuple	      =	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    ,	)
			__magic_name__			:       int	      =	DDIMScheduler(
			    beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule='scaled_linear'    ,	num_train_timesteps=1000    ,	clip_sample=_A    ,	set_alpha_to_one=_A    ,	)
			torch.manual_seed(0     )
			__magic_name__			:       List[str]	      =	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     )
			__magic_name__			:       Optional[Any]	      =	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    ,	)
			__magic_name__			:       str	      =	CLIPTextModel(_A     )
			__magic_name__			:       Tuple	      =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

			__magic_name__			:       List[Any]	      =	{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components







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

			__magic_name__			:       List[str]	      =	self.get_dummy_components()
			__magic_name__			:       Optional[int]	      =	CycleDiffusionPipeline(**_A     )
			__magic_name__			:       str	      =	pipe.to(_A     )
			pipe.set_progress_bar_config(disable=_A     )

			__magic_name__			:       Any	      =	self.get_dummy_inputs(_A     )
			__magic_name__			:       Optional[int]	      =	pipe(**_A     )
			__magic_name__			:       List[str]	      =	output.images

			__magic_name__			:       Tuple	      =	images[0, -3:, -3:, -1]

			assert images.shape == (1, 32, 32, 3)
			__magic_name__			:       int	      =	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   __lowerCAmelCase						(   self						:		str     )    ->					str:
			__magic_name__			:       str	      =	self.get_dummy_components()
			for name, module in components.items():
				if hasattr(_A    ,	'half'     ):
					__magic_name__			:       Dict	      =	module.half()
			__magic_name__			:       str	      =	CycleDiffusionPipeline(**_A     )
			__magic_name__			:       Optional[int]	      =	pipe.to(_A     )
			pipe.set_progress_bar_config(disable=_A     )

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

			__magic_name__			:       Any	      =	images[0, -3:, -3:, -1]

			assert images.shape == (1, 32, 32, 3)
			__magic_name__			:       str	      =	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   __lowerCAmelCase						(   self						:		List[str]     )    ->					List[Any]:
			return super().test_save_load_local()







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







		@skip_mps
		def   __lowerCAmelCase						(   self						:		str     )    ->					str:
			return super().test_dict_tuple_outputs_equivalent()







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







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







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



		'''simple docstring'''







		def   __lowerCAmelCase						(   self						:		List[str]     )    ->					str:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def   __lowerCAmelCase						(   self						:		Any     )    ->					List[str]:
			__magic_name__			:       Tuple	      =	load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/cycle-diffusion/black_colored_car.png'     )
			__magic_name__			:       Any	      =	load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
			__magic_name__			:       List[Any]	      =	init_image.resize((512, 512)     )

			__magic_name__			:       Optional[int]	      =	'CompVis/stable-diffusion-v1-4'
			__magic_name__			:       Tuple	      =	DDIMScheduler.from_pretrained(_A    ,	subfolder='scheduler'     )
			__magic_name__			:       List[str]	      =	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()

			__magic_name__			:       List[Any]	      =	'A black colored car'
			__magic_name__			:       Tuple	      =	'A blue colored car'

			__magic_name__			:       int	      =	torch.manual_seed(0     )
			__magic_name__			:       Tuple	      =	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'    ,	)
			__magic_name__			:       Tuple	      =	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						:		List[Any]     )    ->					Any:
			__magic_name__			:       Optional[Any]	      =	load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/cycle-diffusion/black_colored_car.png'     )
			__magic_name__			:       Union[str, Any]	      =	load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
			__magic_name__			:       Optional[Any]	      =	init_image.resize((512, 512)     )

			__magic_name__			:       Union[str, Any]	      =	'CompVis/stable-diffusion-v1-4'
			__magic_name__			:       Union[str, Any]	      =	DDIMScheduler.from_pretrained(_A    ,	subfolder='scheduler'     )
			__magic_name__			:       Tuple	      =	CycleDiffusionPipeline.from_pretrained(_A    ,	scheduler=_A    ,	safety_checker=_A     )

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

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

			__magic_name__			:       Optional[int]	      =	torch.manual_seed(0     )
			__magic_name__			:       Dict	      =	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'    ,	)
			__magic_name__			:       Optional[int]	      =	output.images

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


import string





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       ''
							for i in sequence:
														snake_case_     							=       ord(UpperCamelCase__ )
														if 65 <= extract <= 90:
																					output += chr(155 - extract )
														elif 97 <= extract <= 122:
																					output += chr(219 - extract )
														else:
																					output += i
							return output





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       string.ascii_letters
							snake_case_     							=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(UpperCamelCase__ )] if c in letters else c for c in sequence )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							from timeit import timeit

							print('Running performance benchmarks...' )
							snake_case_     							=       'from string import printable ; from __main__ import atbash, atbash_slow'
							print(F'''> atbash_slow(): {timeit("atbash_slow(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )
							print(F'''>      atbash(): {timeit("atbash(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )


if __name__ == "__main__":
					for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
										print(F'''{example} encrypted in atbash: {atbash(example)}''')
					benchmark()




