



'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase							       =			logging.getLogger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     return (preds == labels).mean()







@dataclass
class 	UpperCAmelCase	:


 '''simple docstring'''




 __magic_name__					: str  =      field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Pretrained config name or path if not the same as model_name"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,	)







@dataclass
class 	UpperCAmelCase	:


 '''simple docstring'''




 __magic_name__					: str  =      field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
 __magic_name__					: str  =      field(metadata={"help": "Should contain the data files for the task."})
 __magic_name__					: int  =      field(
     default=128					,	metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }					,	)
 __magic_name__					: bool  =      field(
     default=__a					,	metadata={"help": "Overwrite the cached training and evaluation sets"})






def  UpperCAmelCase_       (		):


     '''simple docstring'''







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

     # Set seed
     set_seed(training_args.seed       )

     try:
          a_       		=processors[data_args.task_name]()
          a_       		=processor.get_labels()
          a_       		=len(lowercase__       )
     except KeyError:
          raise ValueError("Task not found: %s" % (data_args.task_name)       )

     # 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       ,	num_labels=lowercase__       ,	finetuning_task=data_args.task_name       ,	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       ,	)
     a_       		=AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path       ,	from_tf=bool(".ckpt" in model_args.model_name_or_path       )       ,	config=lowercase__       ,	cache_dir=model_args.cache_dir       ,	)

     # Get datasets
     a_       		=(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir       ,	tokenizer=lowercase__       ,	task=data_args.task_name       ,	max_seq_length=data_args.max_seq_length       ,	overwrite_cache=data_args.overwrite_cache       ,	mode=Split.train       ,	)
         if training_args.do_train
         else None
     )
     a_       		=(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir       ,	tokenizer=lowercase__       ,	task=data_args.task_name       ,	max_seq_length=data_args.max_seq_length       ,	overwrite_cache=data_args.overwrite_cache       ,	mode=Split.dev       ,	)
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase__       ) -> Dict:
          a_       		=np.argmax(p.predictions       ,	axis=1       )
          return {"acc": simple_accuracy(lowercase__       ,	p.label_ids       )}

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

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

     # 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       )

    # Evaluation
     a_       		={}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"       )

          a_       		=trainer.evaluate()

          a_       		=os.path.join(training_args.output_dir       ,	"eval_results.txt"       )
          if trainer.is_world_master():
               with open(lowercase__       ,	"w"       ) as writer:
                    logger.info("***** Eval results *****"       )
                    for key, value in result.items():
                         logger.info("  %s = %s"       ,	lowercase__       ,	lowercase__       )
                         writer.write("%s = %s\n" % (key, value)       )

                    results.update(lowercase__       )

     return results






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     main()


if __name__ == "__main__":
  main()







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


