






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


lowerCamelCase	:							Tuple				=logging.getLogger(__name__)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	List[Any]:
     return (preds == labels).mean()





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







@dataclass
class 			__a  :
 _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=1_2_8       ,   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=A__       ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )







def 		SCREAMING_SNAKE_CASE							(					) ->	Optional[Any]:
     # 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.

     UpperCamelCase__			:   Optional[Any]       				=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   int       				=  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:
          UpperCamelCase__			:   Any       				=  processors[data_args.task_name]()
          UpperCamelCase__			:   Any       				=  processor.get_labels()
          UpperCamelCase__			:   Dict       				=  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.

     UpperCamelCase__			:   Union[str, Any]       				=  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					,    )
     UpperCamelCase__			:   Dict       				=  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					,    )
     UpperCamelCase__			:   str       				=  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
     UpperCamelCase__			:   str       				=  (
         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
     )
     UpperCamelCase__			:   List[Any]       				=  (
         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			) -> Dict:
          UpperCamelCase__			:   Any       				=  np.argmax(p.predictions					,    axis=1			)
          return {"acc": simple_accuracy(__lowerCAmelCase					,    p.label_ids			)}

     # Data collator
     UpperCamelCase__			:   Optional[Any]       				=  DataCollatorWithPadding(__lowerCAmelCase					,    pad_to_multiple_of=8			) if training_args.fpaa else None

     # Initialize our Trainer
     UpperCamelCase__			:   Tuple       				=  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
     UpperCamelCase__			:   str       				=  {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"			)

          UpperCamelCase__			:   int       				=  trainer.evaluate()

          UpperCamelCase__			:   Union[str, Any]       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase	:							Optional[Any]				=subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowerCamelCase	:							str				=subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

lowerCamelCase	:							List[Any]				='''|'''.join(sys.argv[1:])
lowerCamelCase	:							str				=re.compile(RF"""^({joined_dirs}).*?\.py$""")

lowerCamelCase	:							Optional[int]				=[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')