






'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowerCAmelCase							=logging.getLogger(__name__)


@dataclass
class    _snake_case   :

					"""simple docstring"""





					_UpperCamelCase		 =      field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
					_UpperCamelCase		 =      field(
					    default=snake_case     ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
					_UpperCamelCase		 =      field(
					    default=snake_case     ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
					_UpperCamelCase		 =      field(default=snake_case     ,					metadata={"help": "Set this flag to use fast tokenization."}					)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					_UpperCamelCase		 =      field(
					    default=snake_case     ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,					)



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






					"""simple docstring"""







					a_									=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										a_							,					a_							,					a_									=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
					else:
										a_							,					a_							,					a_									=   parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir )
					    and os.listdir(training_args.output_dir )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ' --overwrite_output_dir to overcome.' )

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,  datefmt='%m/%d/%Y %H:%M:%S'					,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,  )
					logger.warning(
					    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,  training_args.local_rank					,  training_args.device					,  training_args.n_gpu					,  bool(training_args.local_rank != -1 )					,  training_args.fpaa					,  )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank ):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('Training/evaluation parameters %s'					,  _UpperCAmelCase )

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					a_									=   AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,  cache_dir=model_args.cache_dir					,  )
					a_									=   AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,  cache_dir=model_args.cache_dir					,  use_fast=_UpperCAmelCase					,  )
					a_									=   AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path )					,  config=_UpperCAmelCase					,  cache_dir=model_args.cache_dir					,  )

					# Get datasets
					a_									=   hasattr(model.config					,  'lang2id' )
					a_									=   (
					    SquadDataset(
					        _UpperCAmelCase					,  tokenizer=_UpperCAmelCase					,  is_language_sensitive=_UpperCAmelCase					,  cache_dir=model_args.cache_dir )
					    if training_args.do_train
					    else None
					)
					a_									=   (
					    SquadDataset(
					        _UpperCAmelCase					,  tokenizer=_UpperCAmelCase					,  mode='dev'					,  is_language_sensitive=_UpperCAmelCase					,  cache_dir=model_args.cache_dir					,  )
					    if training_args.do_eval
					    else None
					)

					# Data collator
					a_									=   DataCollatorWithPadding(_UpperCAmelCase					,  pad_to_multiple_of=8 ) if training_args.fpaa else None

					# Initialize our Trainer
					a_									=   Trainer(
					    model=_UpperCAmelCase					,  args=_UpperCAmelCase					,  train_dataset=_UpperCAmelCase					,  eval_dataset=_UpperCAmelCase					,  data_collator=_UpperCAmelCase					,  )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir )

def 							a	(   _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					main()


if __name__ == "__main__":
						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__     )

