



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__					:     Dict    		=   logging.getLogger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	return (preds == labels).mean()

@dataclass
class   _A   :


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

@dataclass
class   _A   :


						'''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=_lowercase  ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)



def    snake_case_				(    ):
	# 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.

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

	__lowercase		       =			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  ,	)
	__lowercase		       =			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  ,	)
	__lowercase		       =			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
	__lowercase		       =			(
	    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
	)
	__lowercase		       =			(
	    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   ) -> Dict:
		__lowercase		       =			np.argmax(p.predictions  ,	axis=1   )
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE  ,	p.label_ids   )}

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

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

		__lowercase		       =			trainer.evaluate()

		__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The length of profit and weight must be same."   )
	if max_weight <= 0:
		raise ValueError("max_weight must greater than zero."   )
	if any(p < 0 for p in profit   ):
		raise ValueError("Profit can not be negative."   )
	if any(w < 0 for w in weight   ):
		raise ValueError("Weight can not be negative."   )

	# List created to store profit gained for the 1kg in case of each weight
	# respectively.  Calculate and append profit/weight for each element.
	__lowercase		       =			[p / w for p, w in zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )]

	# Creating a copy of the list and sorting profit/weight in ascending order
	__lowercase		       =			sorted(_SCREAMING_SNAKE_CASE   )

	# declaring useful variables
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			0
	__lowercase		       =			0
	__lowercase		       =			0

	# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
	while limit <= max_weight and i < length:
		# flag value for encountered greatest element in sorted_profit_by_weight
		__lowercase		       =			sorted_profit_by_weight[length - i - 1]
		__lowercase		       =			profit_by_weight.index(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			-1

		# check if the weight encountered is less than the total weight
		# encountered before.
		if max_weight - limit >= weight[index]:
			limit += weight[index]
			# Adding profit gained for the given weight 1 ===
			# weight[index]/weight[index]
			gain += 1 * profit[index]
		else:
			# Since the weight encountered is greater than limit, therefore take the
			# required number of remaining kgs and calculate profit for it.
			# weight remaining / weight[index]
			gain += (max_weight - limit) / weight[index] * profit[index]
			break
		i += 1
	return gain


if __name__ == "__main__":
	print(
	    """Input profits, weights, and then max_weight (all positive ints) separated by """
	    """spaces."""
	)

	snake_case__					:     str    		=   [int(x) for x in input("""Input profits separated by spaces: """).split()]
	snake_case__					:     str    		=   [int(x) for x in input("""Input weights separated by spaces: """).split()]
	snake_case__					:     Optional[Any]    		=   int(input("""Max weight allowed: """))

	# Function Call
	calc_profit(profit, weight, max_weight)



