



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


snake_case__					:     Tuple    		=   getLogger(__name__)


snake_case__					:     Union[str, Any]    		=   """cuda""" if torch.cuda.is_available() else """cpu"""



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 8  ,	_SCREAMING_SNAKE_CASE = DEFAULT_DEVICE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE="summarization"  ,	_SCREAMING_SNAKE_CASE=None  ,	**_SCREAMING_SNAKE_CASE  ,	):
	__lowercase		       =			Path(_snake_case   ).open("w"  ,	encoding="utf-8"   )
	__lowercase		       =			str(_snake_case   )
	__lowercase		       =			AutoModelForSeqaSeqLM.from_pretrained(_snake_case   ).to(_snake_case   )
	if fpaa:
		__lowercase		       =			model.half()

	__lowercase		       =			AutoTokenizer.from_pretrained(_snake_case   )
	logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""   )  # if this is wrong, check config.model_type.

	__lowercase		       =			time.time()
	# update config with task specific params
	use_task_specific_params(_snake_case  ,	_snake_case   )
	if prefix is None:
		__lowercase		       =			prefix or getattr(model.config  ,	"prefix"  ,	""   ) or ""
	for examples_chunk in tqdm(list(chunks(_snake_case  ,	_snake_case   )   )   ):
		__lowercase		       =			[prefix + text for text in examples_chunk]
		__lowercase		       =			tokenizer(_snake_case  ,	return_tensors="pt"  ,	truncation=_snake_case  ,	padding="longest"   ).to(_snake_case   )
		__lowercase		       =			model.generate(
		    input_ids=batch.input_ids  ,	attention_mask=batch.attention_mask  ,	**_snake_case  ,	)
		__lowercase		       =			tokenizer.batch_decode(_snake_case  ,	skip_special_tokens=_snake_case  ,	clean_up_tokenization_spaces=_snake_case   )
		for hypothesis in dec:
			fout.write(hypothesis + "\n"   )
			fout.flush()
	fout.close()
	__lowercase		       =			int(time.time() - start_time   )  # seconds
	__lowercase		       =			len(_snake_case   )
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs  ,	4   )}



def    snake_case_				(    ):
	return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE=True   ):
	__lowercase		       =			argparse.ArgumentParser()
	parser.add_argument("model_name"  ,	type=_snake_case  ,	help="like facebook/bart-large-cnn,t5-base, etc."   )
	parser.add_argument("input_path"  ,	type=_snake_case  ,	help="like cnn_dm/test.source"   )
	parser.add_argument("save_path"  ,	type=_snake_case  ,	help="where to save summaries"   )
	parser.add_argument("--reference_path"  ,	type=_snake_case  ,	required=_snake_case  ,	help="like cnn_dm/test.target"   )
	parser.add_argument("--score_path"  ,	type=_snake_case  ,	required=_snake_case  ,	default="metrics.json"  ,	help="where to save metrics"   )
	parser.add_argument("--device"  ,	type=_snake_case  ,	required=_snake_case  ,	default=_snake_case  ,	help="cuda, cuda:1, cpu etc."   )
	parser.add_argument(
	    "--prefix"  ,	type=_snake_case  ,	required=_snake_case  ,	default=_snake_case  ,	help="will be added to the begininng of src examples"   )
	parser.add_argument("--task"  ,	type=_snake_case  ,	default="summarization"  ,	help="used for task_specific_params + metrics"   )
	parser.add_argument("--bs"  ,	type=_snake_case  ,	default=8  ,	required=_snake_case  ,	help="batch size"   )
	parser.add_argument(
	    "--n_obs"  ,	type=_snake_case  ,	default=-1  ,	required=_snake_case  ,	help="How many observations. Defaults to all."   )
	parser.add_argument("--fp16"  ,	action="store_true"   )
	parser.add_argument("--dump-args"  ,	action="store_true"  ,	help="print the custom hparams with the results"   )
	parser.add_argument(
	    "--info"  ,	nargs="?"  ,	type=_snake_case  ,	const=datetime_now()  ,	help=(
	        "use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."
	        " lang=en-ru. If no value is passed, the current datetime string will be used."
	    )  ,	)
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowercase       ,    __lowercase		       =			parser.parse_known_args()
	__lowercase		       =			parse_numeric_n_bool_cl_kwargs(_snake_case   )
	if parsed_args and verbose:
		print(f"""parsed the following generate kwargs: {parsed_args}"""   )
	__lowercase		       =			[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
	if args.n_obs > 0:
		__lowercase		       =			examples[: args.n_obs]
	Path(args.save_path   ).parent.mkdir(exist_ok=_snake_case   )

	if args.reference_path is None and Path(args.score_path   ).exists():
		warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""   )

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError("Can\'t mix --fp16 and --device cpu"   )

	__lowercase		       =			generate_summaries_or_translations(
	    _snake_case  ,	args.save_path  ,	args.model_name  ,	batch_size=args.bs  ,	device=args.device  ,	fpaa=args.fpaa  ,	task=args.task  ,	prefix=args.prefix  ,	**_snake_case  ,	)

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowercase		       =			calculate_bleu if "translation" in args.task else calculate_rouge
	__lowercase		       =			[x.rstrip() for x in open(args.save_path   ).readlines()]
	__lowercase		       =			[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_snake_case   )]
	__lowercase		       =			score_fn(_snake_case  ,	_snake_case   )
	scores.update(_snake_case   )

	if args.dump_args:
		scores.update(_snake_case   )
	if args.info:
		__lowercase		       =			args.info

	if verbose:
		print(_snake_case   )

	if args.score_path is not None:
		json.dump(_snake_case  ,	open(args.score_path  ,	"w"   )   )

	return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)








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)



