


"""simple docstring"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


__SCREAMING_SNAKE_CASE					=						logging.getLogger(__name__)



def 						__a      (  ):





       """simple docstring"""







       _a          =				argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."					)
       parser.add_argument(
           "--dataset_name",		type=a,		default="wikitext",		help="Name of the training. Explore datasets at: hf.co/datasets.",		)
       parser.add_argument(
           "--dataset_config",		type=a,		default="wikitext-103-raw-v1",		help="Configuration name of the dataset."					)
       parser.add_argument(
           "--batch_size",		type=a,		default=1_0_0_0,		help="Batch size during training.",		)
       parser.add_argument(
           "--vocab_size",		type=a,		default=1_0_0_4_8,		help="Size of the desired vocabulary.",		)
       parser.add_argument(
           "--limit",		default=a,		type=a,		help="Limit the number of shards (used for debugging).",		)
       parser.add_argument(
           "--export_to_hub",		action="store_true",		)

       _a          =				parser.parse_args()
       return args



def 						__a      (  a					):





       """simple docstring"""







       _a          =				datasets.load_dataset(args.dataset_name,		args.dataset_config,		split="train"					)

       if args.limit is not None:
              _a          =				min(len(a					),		args.limit					)
              _a          =				dataset.select(range(a					)					)
              logger.info(F'Limiting the dataset to {args.limit} entries.'					)

       def batch_iterator():
              for i in range(0,		len(a					),		args.batch_size					):
                     yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
       _a          =				Tokenizer(Unigram()					)
       _a          =				normalizers.Sequence([normalizers.Replace("``",		"\""					), normalizers.Replace("''",		"\""					)]					)
       _a          =				pre_tokenizers.Metaspace()

       # Prepare the trainer.
       _a          =				UnigramTrainer(
           unk_token="<unk>",		special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"],		vocab_size=args.vocab_size,		)

       logger.info("Training the tokenizer."					)
       tokenizer.train_from_iterator(batch_iterator(),		trainer=a					)
       logger.info("Tokenizer training complete!"					)

       _a          =				tokenizer.token_to_id("[CLS]"					)
       _a          =				tokenizer.token_to_id("[SEP]"					)
       _a          =				processors.TemplateProcessing(
           single="[CLS]:0 $A:0 [SEP]:0",		pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",		special_tokens=[
               ("[CLS]", cls_token_id),
               ("[SEP]", sep_token_id),
           ],		)
       _a          =				decoders.Metaspace()

       if args.export_to_hub:
              logger.info("Exporting the trained tokenzier to Hub."					)
              _a          =				AlbertTokenizerFast(tokenizer_object=a					)
              new_tokenizer.push_to_hub("unigram-tokenizer-dataset"					)


if __name__ == "__main__":
   __SCREAMING_SNAKE_CASE					=						parse_args()
   main(args)




"""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		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

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




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * 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							:List[str]				,  UpperCamelCase__							:List[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							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _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__       )
