



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        __SCREAMING_SNAKE_CASE							(	_SCREAMING_SNAKE_CASE						):

					def __init__(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, ):







						'''simple docstring'''




						super().__init__()

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

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						with torch.no_grad():
							lowerCamelCase_							:	int							     =							self.vae.encode(tfms.ToTensor()(__A      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
							lowerCamelCase_							:	int							     =							0.1_82_15 * latent.latent_dist.sample()
						return latent

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							(1 / 0.1_82_15) * latent
						with torch.no_grad():
							lowerCamelCase_							:	Union[str, Any]							     =							self.vae.decode(__A      ).sample
						lowerCamelCase_							:	str							     =							(img / 2 + 0.5).clamp(0				, 1      )
						lowerCamelCase_							:	Union[str, Any]							     =							img.detach().cpu().permute(0				, 2				, 3				, 1      ).numpy()
						lowerCamelCase_							:	List[str]							     =							(img * 255).round().astype('uint8'      )
						return Image.fromarray(img[0]      )

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.tokenizer(
						    __A				, padding='max_length'				, max_length=self.tokenizer.model_max_length				, truncation=__A				, return_tensors='pt'				, )

						lowerCamelCase_							:	Any							     =							self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

						lowerCamelCase_							:	Dict							     =							self.tokenizer(
						    ''				, padding='max_length'				, max_length=self.tokenizer.model_max_length				, truncation=__A				, return_tensors='pt'				, )

						lowerCamelCase_							:	Optional[Any]							     =							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

						return torch.cat([uncond_embedding, text_embedding]      )





					def __call__(       self				, __lowercase				, __lowercase				, __lowercase = 0.3				, __lowercase = 0.6				, __lowercase = 0.5				, __lowercase = 42				, __lowercase = 50				, __lowercase = 7.5				, ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							steps - int(kmin * steps      )
						lowerCamelCase_							:	Any							     =							steps - int(kmax * steps      )

						lowerCamelCase_							:	Dict							     =							self.prep_text(__A      )

						self.scheduler.set_timesteps(__A      )

						lowerCamelCase_     ,    lowerCamelCase_							:	List[Any]							     =							img.size
						lowerCamelCase_							:	Union[str, Any]							     =							self.encode(__A      )

						torch.manual_seed(__A      )
						lowerCamelCase_							:	Optional[int]							     =							torch.randn(
						    (1, self.unet.config.in_channels, height // 8, width // 8)				, ).to(self.device      )

						lowerCamelCase_							:	Union[str, Any]							     =							self.scheduler.add_noise(
						    __A				, __A				, timesteps=self.scheduler.timesteps[tmax]				, )

						lowerCamelCase_							:	List[Any]							     =							torch.cat([latents] * 2      )

						lowerCamelCase_							:	Dict							     =							self.scheduler.scale_model_input(__A				, self.scheduler.timesteps[tmax]      )

						with torch.no_grad():
							lowerCamelCase_							:	Any							     =							self.unet(
							    __A				, self.scheduler.timesteps[tmax]				, encoder_hidden_states=__A				, ).sample

						lowerCamelCase_     ,    lowerCamelCase_							:	Optional[Any]							     =							pred.chunk(2      )
						lowerCamelCase_							:	List[Any]							     =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

						lowerCamelCase_							:	Dict							     =							self.scheduler.step(__A				, self.scheduler.timesteps[tmax]				, __A      ).prev_sample

						for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
							if i > tmax:
								if i < tmin:  # layout generation phase
									lowerCamelCase_							:	List[str]							     =							self.scheduler.add_noise(
									    __A				, __A				, timesteps=__A				, )

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

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

								lowerCamelCase_							:	Optional[Any]							     =							self.scheduler.scale_model_input(__A				, __A      )

								with torch.no_grad():
									lowerCamelCase_							:	str							     =							self.unet(
									    __A				, __A				, encoder_hidden_states=__A				, ).sample

								lowerCamelCase_     ,    lowerCamelCase_							:	Tuple							     =							pred.chunk(2      )
								lowerCamelCase_							:	int							     =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

								lowerCamelCase_							:	Union[str, Any]							     =							self.scheduler.step(__A				, __A				, __A      ).prev_sample

						return self.decode(__A      )






# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/
