


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_						:			Optional[int]													=							logging.getLogger(__name__)







def 						lowerCamelCase(   a__	,a__):
				return (preds == labels).mean()

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

@dataclass
class 						A__							:
						UpperCAmelCase             =						field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()  )}  )
						UpperCAmelCase             =						field(metadata={"help": "Should contain the data files for the task."}  )
						UpperCAmelCase             =						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."
						        )
						    }   ,     )
						UpperCAmelCase             =						field(
						    default=UpperCamelCase__   ,     metadata={"help": "Overwrite the cached training and evaluation sets"}  )







def 						lowerCamelCase(   ):
				# 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.

				_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'''	,a__)

				# 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(a__)
				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=a__	,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=a__	,cache_dir=model_args.cache_dir	,)

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

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

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE			  =Trainer(
				    model=a__	,args=a__	,train_dataset=a__	,eval_dataset=a__	,compute_metrics=a__	,data_collator=a__	,)

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

																results.update(a__)

				return results







def 						lowerCamelCase(   a__):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()



snake_case_						:			dict[tuple[int, int, int], int]													=							{}







def 						lowerCamelCase(   a__	,a__	,a__):

				# if we are absent twice, or late 3 consecutive days,
				# no further prize strings are possible
				if late == 3 or absent == 2:
								return 0

				# if we have no days left, and have not failed any other rules,
				# we have a prize string
				if days == 0:
								return 1

				# No easy solution, so now we need to do the recursive calculation

				# First, check if the combination is already in the cache, and
				# if yes, return the stored value from there since we already
				# know the number of possible prize strings from this point on
				_SCREAMING_SNAKE_CASE			  =(days, absent, late)
				if key in cache:
								return cache[key]

				# now we calculate the three possible ways that can unfold from
				# this point on, depending on our attendance today

				# 1) if we are late (but not absent), the "absent" counter stays as
				# it is, but the "late" counter increases by one
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,late + 1)

				# 2) if we are absent, the "absent" counter increases by 1, and the
				# "late" counter resets to 0
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,absent + 1	,0)

				# 3) if we are on time, this resets the "late" counter and keeps the
				# absent counter
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,0)

				_SCREAMING_SNAKE_CASE			  =state_late + state_absent + state_ontime

				_SCREAMING_SNAKE_CASE			  =prizestrings
				return prizestrings







def 						lowerCamelCase(   a__ = 30):
				return _calculate(a__	,absent=0	,late=0)


if __name__ == "__main__":
							print(solution())