


"""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


a__    :							Any            =					logging.getLogger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return (preds == labels).mean()




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





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




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  str    =      field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
	snake_case__    :  str    =      field(metadata={"help": "Should contain the data files for the task."})
	snake_case__    :  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."
	        )
	    }      ,  )
	snake_case__    :  bool    =      field(
	    default=UpperCamelCase      ,  metadata={"help": "Overwrite the cached training and evaluation sets"})




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    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:
										__SCREAMING_SNAKE_CASE						       =    processors[data_args.task_name]()
										__SCREAMING_SNAKE_CASE						       =    processor.get_labels()
										__SCREAMING_SNAKE_CASE						       =    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.

					__SCREAMING_SNAKE_CASE						       =    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  ,						)
					__SCREAMING_SNAKE_CASE						       =    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  ,						)
					__SCREAMING_SNAKE_CASE						       =    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
					__SCREAMING_SNAKE_CASE						       =    (
					    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
					)
					__SCREAMING_SNAKE_CASE						       =    (
					    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:
										__SCREAMING_SNAKE_CASE						       =    np.argmax(p.predictions  ,						axis=1       )
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						p.label_ids       )}

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

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

										__SCREAMING_SNAKE_CASE						       =    trainer.evaluate()

										__SCREAMING_SNAKE_CASE						       =    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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [0 for i in range(r + 1       )]
					# nc0 = 1
					__SCREAMING_SNAKE_CASE						       =    1
					for i in range(1  ,						n + 1       ):
										# to compute current row from previous row.
										__SCREAMING_SNAKE_CASE						       =    min(lowerCAmelCase_  ,						lowerCAmelCase_       )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]



print(binomial_coefficient(n=1_0, r=5))
