





"""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


a__   :       Dict   						=			logging.getLogger(__name__)





def 							a__     (					)      ->					str:

	'''simple docstring'''






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

	snake_case__      							=			parser.parse_args()
	return args





def 							a__     (					_UpperCAmelCase		)      ->					Union[str, Any]:

	'''simple docstring'''






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

	if args.limit is not None:
		snake_case__      							=			min(len(_UpperCAmelCase		)		,       args.limit		)
		snake_case__      							=			dataset.select(range(_UpperCAmelCase		)		)
		logger.info(f"Limiting the dataset to {args.limit} entries."		)

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

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

	# Prepare the trainer.
	snake_case__      							=			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=_UpperCAmelCase		)
	logger.info("Tokenizer training complete!"		)

	snake_case__      							=			tokenizer.token_to_id("[CLS]"		)
	snake_case__      							=			tokenizer.token_to_id("[SEP]"		)
	snake_case__      							=			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),
	    ]		,       )
	snake_case__      							=			decoders.Metaspace()

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


if __name__ == "__main__":
			a__   :       Tuple   						=			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        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

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

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

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

        return torch.cat([uncond_embedding, text_embedding]       )

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






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

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

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

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

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

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

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

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

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

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

          with torch.no_grad():
           snake_case__      							=			self.unet(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

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

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

