






'''simple docstring'''
import unittest

from cer import CER


__lowerCAmelCase							=CER()


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:

										'''simple docstring'''





										a_									=   ["""White House"""]
										a_									=   ["""white house"""]
										# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8     ) < 1e-6     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:

										'''simple docstring'''





										a_									=   ["""were wolf"""]
										a_									=   ["""werewolf"""]
										# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1     ) < 1e-6     )

										a_									=   ["""werewolf"""]
										a_									=   ["""weae     wolf"""]
										# S = 1, D = 1, I = 0, N = 8, CER = 0.25
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.2_5     ) < 1e-6     )

										# consecutive whitespaces case 1
										a_									=   ["""were wolf"""]
										a_									=   ["""were               wolf"""]
										# S = 0, D = 0, I = 0, N = 9, CER = 0
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.0     ) < 1e-6     )

										# consecutive whitespaces case 2
										a_									=   ["""were   wolf"""]
										a_									=   ["""were               wolf"""]
										# S = 0, D = 0, I = 0, N = 9, CER = 0
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.0     ) < 1e-6     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:

										'''simple docstring'''





										a_									=   ["""werewolf"""]
										a_									=   ["""weaewolf"""]
										# S = 1, D = 0, I = 0, N = 8, CER = 0.125
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.1_2_5     ) < 1e-6     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:

										'''simple docstring'''





										a_									=   ["""werewolf"""]
										a_									=   ["""wereawolf"""]
										# S = 0, D = 1, I = 0, N = 8, CER = 0.125
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.1_2_5     ) < 1e-6     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:

										'''simple docstring'''





										a_									=   ["""werewolf"""]
										a_									=   ["""wereolf"""]
										# S = 0, D = 0, I = 1, N = 8, CER = 0.125
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.1_2_5     ) < 1e-6     )




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

										'''simple docstring'''





										a_									=   ["""werewolf"""]
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertEqual(_lowerCAmelCase   , 0.0     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:

										'''simple docstring'''





										a_									=   ["""werewolf""", """I am your father"""]
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertEqual(_lowerCAmelCase   , 0.0     )

										a_									=   ["""werewolf""", """I am your father""", """doge"""]
										a_									=   ["""werxwolf""", """I       am your father""", """doge"""]
										# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8     ) < 1e-6     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:

										'''simple docstring'''





										a_									=   ["""My hovercraft""", """is full of eels"""]
										a_									=   ["""My hovercraft is full""", """ of eels"""]
										# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
										# whitespace at the front of " of eels" will be strip during preporcessing
										# so need to insert 2 whitespaces
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase   , concatenate_texts=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8     ) < 1e-6     )




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

										'''simple docstring'''





										a_									=   ["""我能吞下玻璃而不伤身体"""]
										a_									=   [""" 能吞虾玻璃而 不霜身体啦"""]
										# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5     ) < 1e-6     )

										a_									=   ["""我能吞下玻璃""", """而不伤身体"""]
										a_									=   ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
										# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5     ) < 1e-6     )

										a_									=   ["""我能吞下玻璃而不伤身体"""]
										a_									=   cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )
										self.assertFalse(_lowerCAmelCase   , 0.0     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:

										'''simple docstring'''





										a_									=   [""""""]
										a_									=   ["""Hypothesis"""]
										with self.assertRaises(_lowerCAmelCase     ):
															cer.compute(predictions=_lowerCAmelCase   , references=_lowerCAmelCase     )


if __name__ == "__main__":
						unittest.main()









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

