

"""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       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						CycleDiffusionPipeline
			SCREAMING_SNAKE_CASE__     				=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    '''negative_prompt''',
			    '''height''',
			    '''width''',
			    '''negative_prompt_embeds''',
			}
			SCREAMING_SNAKE_CASE__     				=						PipelineTesterMixin.required_optional_params - {'''latents'''}
			SCREAMING_SNAKE_CASE__     				=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}     )
			SCREAMING_SNAKE_CASE__     				=						IMAGE_TO_IMAGE_IMAGE_PARAMS
			SCREAMING_SNAKE_CASE__     				=						IMAGE_TO_IMAGE_IMAGE_PARAMS







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								torch.manual_seed(0  )
								UpperCAmelCase			:   Union[str, 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			,  )
								UpperCAmelCase			:   Optional[Any]      =				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=lowercase			,  set_alpha_to_one=lowercase			,  )
								torch.manual_seed(0  )
								UpperCAmelCase			:   Optional[Any]      =				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  )
								UpperCAmelCase			:   str      =				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			,  )
								UpperCAmelCase			:   List[Any]      =				CLIPTextModel(lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

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







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[str]			,  lowercase			: Optional[int]=0  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				floats_tensor((1, 3, 32, 32)			,  rng=random.Random(lowercase  )  ).to(lowercase  )
								UpperCAmelCase			:   Tuple      =				image / 2 + 0.5
								if str(lowercase  ).startswith("mps"  ):
													UpperCAmelCase			:   str      =				torch.manual_seed(lowercase  )
								else:
													UpperCAmelCase			:   List[str]      =				torch.Generator(device=lowercase  ).manual_seed(lowercase  )
								UpperCAmelCase			:   str      =				{
								    "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			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				"cpu"  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase			:   Dict      =				self.get_dummy_components()
								UpperCAmelCase			:   List[str]      =				CycleDiffusionPipeline(**lowercase  )
								UpperCAmelCase			:   Dict      =				pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   List[str]      =				self.get_dummy_inputs(lowercase  )
								UpperCAmelCase			:   Optional[int]      =				pipe(**lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				output.images

								UpperCAmelCase			:   Optional[Any]      =				images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								UpperCAmelCase			:   Optional[int]      =				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			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.get_dummy_components()
								for name, module in components.items():
													if hasattr(lowercase			,  "half"  ):
																		UpperCAmelCase			:   Any      =				module.half()
								UpperCAmelCase			:   Any      =				CycleDiffusionPipeline(**lowercase  )
								UpperCAmelCase			:   int      =				pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

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

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

								assert images.shape == (1, 32, 32, 3)
								UpperCAmelCase			:   Union[str, Any]      =				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			: int  ):





								'''simple docstring'''



								return super().test_save_load_local()







			@unittest.skip("non-deterministic pipeline"  )
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return super().test_inference_batch_single_identical()







			@skip_mps
			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								return super().test_dict_tuple_outputs_equivalent()







			@skip_mps
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return super().test_save_load_optional_components()







			@skip_mps
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return super().test_attention_slicing_forward_pass()







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







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/cycle-diffusion/black_colored_car.png"  )
								UpperCAmelCase			:   Tuple      =				load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"  )
								UpperCAmelCase			:   Optional[Any]      =				init_image.resize((5_12, 5_12)  )

								UpperCAmelCase			:   Union[str, Any]      =				"CompVis/stable-diffusion-v1-4"
								UpperCAmelCase			:   int      =				DDIMScheduler.from_pretrained(lowercase			,  subfolder="scheduler"  )
								UpperCAmelCase			:   int      =				CycleDiffusionPipeline.from_pretrained(
								    lowercase			,  scheduler=lowercase			,  safety_checker=lowercase			,  torch_dtype=torch.floataa			,  revision="fp16"  )

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

								UpperCAmelCase			:   Optional[int]      =				"A black colored car"
								UpperCAmelCase			:   Optional[int]      =				"A blue colored car"

								UpperCAmelCase			:   Any      =				torch.manual_seed(0  )
								UpperCAmelCase			:   Any      =				pipe(
								    prompt=lowercase			,  source_prompt=lowercase			,  image=lowercase			,  num_inference_steps=1_00			,  eta=0.1			,  strength=0.8_5			,  guidance_scale=3			,  source_guidance_scale=1			,  generator=lowercase			,  output_type="np"			,  )
								UpperCAmelCase			:   Union[str, Any]      =				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			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/cycle-diffusion/black_colored_car.png"  )
								UpperCAmelCase			:   List[Any]      =				load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"  )
								UpperCAmelCase			:   Optional[int]      =				init_image.resize((5_12, 5_12)  )

								UpperCAmelCase			:   List[Any]      =				"CompVis/stable-diffusion-v1-4"
								UpperCAmelCase			:   Union[str, Any]      =				DDIMScheduler.from_pretrained(lowercase			,  subfolder="scheduler"  )
								UpperCAmelCase			:   Optional[int]      =				CycleDiffusionPipeline.from_pretrained(lowercase			,  scheduler=lowercase			,  safety_checker=lowercase  )

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

								UpperCAmelCase			:   Dict      =				"A black colored car"
								UpperCAmelCase			:   Tuple      =				"A blue colored car"

								UpperCAmelCase			:   Union[str, Any]      =				torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				pipe(
								    prompt=lowercase			,  source_prompt=lowercase			,  image=lowercase			,  num_inference_steps=1_00			,  eta=0.1			,  strength=0.8_5			,  guidance_scale=3			,  source_guidance_scale=1			,  generator=lowercase			,  output_type="np"			,  )
								UpperCAmelCase			:   Any      =				output.images

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





"""simple docstring"""






def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					if not all(x.isalpha() for x in string	):
										raise ValueError("String must only contain alphabetic characters."	)

					UpperCAmelCase			:   Optional[int]      =				sorted(string.lower()	)
					return len(_lowercase	) == len(set(_lowercase	)	)


if __name__ == "__main__":
	snake_case_   :   Tuple        =       input("""Enter a string """).strip()

	snake_case_   :   str        =       is_isogram(input_str)
	print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')


