


'''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


__snake_case    						=logging.getLogger(__name__)





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict				):
   return (preds == labels).mean()

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




@dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	str		=     field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()		)}		)
     lowerCamelCase				:	str		=     field(metadata={'''help''': '''Should contain the data files for the task.'''}		)
     lowerCamelCase				:	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.'''
             )
         }       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def 	a_					( ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowerCAmelCase     =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			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'	, lowerCamelCase				)

   # Set seed
   set_seed(training_args.seed				)

   try:
      lowerCAmelCase     =			processors[data_args.task_name]()
      lowerCAmelCase     =			processor.get_labels()
      lowerCAmelCase     =			len(lowerCamelCase				)
   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.

   lowerCAmelCase     =			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	, num_labels=lowerCamelCase	, finetuning_task=data_args.task_name	, cache_dir=model_args.cache_dir	, )
   lowerCAmelCase     =			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	, )
   lowerCAmelCase     =			AutoModelForMultipleChoice.from_pretrained(
       model_args.model_name_or_path	, from_tf=bool('.ckpt' in model_args.model_name_or_path				)	, config=lowerCamelCase	, cache_dir=model_args.cache_dir	, )

   # Get datasets
   lowerCAmelCase     =			(
       MultipleChoiceDataset(
           data_dir=data_args.data_dir	, tokenizer=lowerCamelCase	, 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
   )
   lowerCAmelCase     =			(
       MultipleChoiceDataset(
           data_dir=data_args.data_dir	, tokenizer=lowerCamelCase	, 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(lowerCamelCase	:     EvalPrediction				) -> Dict:
      lowerCAmelCase     =			np.argmax(p.predictions	, axis=1				)
      return {"acc": simple_accuracy(lowerCamelCase	, p.label_ids				)}

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

   # Initialize our Trainer
   lowerCAmelCase     =			Trainer(
       model=lowerCamelCase	, args=lowerCamelCase	, train_dataset=lowerCamelCase	, eval_dataset=lowerCamelCase	, compute_metrics=lowerCamelCase	, data_collator=lowerCamelCase	, )

   # 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
   lowerCAmelCase     =			{}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'				)

      lowerCAmelCase     =			trainer.evaluate()

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

            results.update(lowerCamelCase				)

   return results





def 	a_					( lowerCamelCase	:     Dict				):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()











import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_ : List[Any]    		=       data_utils.TransfoXLTokenizer
snake_case_ : int    		=       data_utils.TransfoXLCorpus
snake_case_ : List[Any]    		=       data_utils
snake_case_ : int    		=       data_utils


def    A						(__A   :    Dict   ,				__A   :    List[Any]   ,				__A   :    Union[str, Any]   ,				__A   :    Tuple       )   ->      Union[str, Any]:







     """simple docstring"""


     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__A   ,				'''rb'''       ) as fp:
               UpperCAmelCase_          =							pickle.load(__A   ,				encoding='''latin1'''       )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""       )
          UpperCAmelCase_          =							corpus.vocab.__dict__
          torch.save(__A   ,				__A       )

          UpperCAmelCase_          =							corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''   ,				__A       )
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F"""Save dataset to {pytorch_dataset_dump_path}"""       )
          torch.save(__A   ,				__A       )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          UpperCAmelCase_          =							os.path.abspath(__A       )
          UpperCAmelCase_          =							os.path.abspath(__A       )

          print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""       )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               UpperCAmelCase_          =							TransfoXLConfig()
          else:
               UpperCAmelCase_          =							TransfoXLConfig.from_json_file(__A       )
          print(F"""Building PyTorch model from configuration: {config}"""       )
          UpperCAmelCase_          =							TransfoXLLMHeadModel(__A       )

          UpperCAmelCase_          =							load_tf_weights_in_transfo_xl(__A   ,				__A   ,				__A       )
          # Save pytorch-model
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          print(F"""Save PyTorch model to {os.path.abspath(__A       )}"""       )
          torch.save(model.state_dict()   ,				__A       )
          print(F"""Save configuration file to {os.path.abspath(__A       )}"""       )
          with open(__A   ,				'''w'''   ,				encoding='''utf-8'''       ) as f:
               f.write(config.to_json_string()       )


if __name__ == "__main__":
    snake_case_ : List[str]    		=       argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    snake_case_ : int    		=       parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )






