




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


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					return (preds == labels).mean()
@dataclass
class 			a__    :
						_a							:    str					=    field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
						_a							:    Optional[str]					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
						_a							:    Optional[str]					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
						_a							:    Optional[str]					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,				)






@dataclass
class 			a__    :
						_a							:    str					=    field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
						_a							:    str					=    field(metadata={"""help""": """Should contain the data files for the task."""}      )
						_a							:    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."""
						        )
						    } ,				)
						_a							:    bool					=    field(
						    default=snake_case__ ,				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"      ,      SCREAMING_SNAKE_CASE_     )

					# Set seed
					set_seed(training_args.seed     )

					try:
										__lowerCAmelCase				   =       processors[data_args.task_name]()
										__lowerCAmelCase				   =       processor.get_labels()
										__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_     )
					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=SCREAMING_SNAKE_CASE_      ,      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=SCREAMING_SNAKE_CASE_      ,      cache_dir=model_args.cache_dir      ,      )

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

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

					# Initialize our Trainer
					__lowerCAmelCase				   =       Trainer(
					    model=SCREAMING_SNAKE_CASE_      ,      args=SCREAMING_SNAKE_CASE_      ,      train_dataset=SCREAMING_SNAKE_CASE_      ,      eval_dataset=SCREAMING_SNAKE_CASE_      ,      compute_metrics=SCREAMING_SNAKE_CASE_      ,      data_collator=SCREAMING_SNAKE_CASE_      ,      )

					# 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(SCREAMING_SNAKE_CASE_      ,      "w"     ) as writer:
																				logger.info("***** Eval results *****"     )
																				for key, value in result.items():
																									logger.info("  %s = %s"      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																									writer.write("%s = %s\n" % (key, value)     )

																				results.update(SCREAMING_SNAKE_CASE_     )

					return results







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()





'''simple docstring'''




__SCREAMING_SNAKE_CASE       :     Dict     =  8.3_1_4_4_6_2  # Unit - J mol-1 K-1

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or volume < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or pressure < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()

