





'''simple docstring'''




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


a__					:			List[Any]          =     getLogger(__name__)


a__					:			Tuple          =     '''cuda''' if torch.cuda.is_available() else '''cpu'''




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = 8					,				UpperCAmelCase_ = DEFAULT_DEVICE					,				UpperCAmelCase_=False					,				UpperCAmelCase_="summarization"					,				UpperCAmelCase_=None					,				**UpperCAmelCase_					,				)   ->Dict:
	snake_case__	       =   Path(UpperCAmelCase_							).open('w'					,				encoding='utf-8'							)
	snake_case__	       =   str(UpperCAmelCase_							)
	snake_case__	       =   AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_							).to(UpperCAmelCase_							)
	if fpaa:
		snake_case__	       =   model.half()

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

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




def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'							)




def 						__lowerCamelCase			(	UpperCAmelCase_=True							)   ->Any:
	snake_case__	       =   argparse.ArgumentParser()
	parser.add_argument('model_name'					,				type=UpperCAmelCase_					,				help='like facebook/bart-large-cnn,t5-base, etc.'							)
	parser.add_argument('input_path'					,				type=UpperCAmelCase_					,				help='like cnn_dm/test.source'							)
	parser.add_argument('save_path'					,				type=UpperCAmelCase_					,				help='where to save summaries'							)
	parser.add_argument('--reference_path'					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				help='like cnn_dm/test.target'							)
	parser.add_argument('--score_path'					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				default='metrics.json'					,				help='where to save metrics'							)
	parser.add_argument('--device'					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help='cuda, cuda:1, cpu etc.'							)
	parser.add_argument(
	    '--prefix'					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help='will be added to the begininng of src examples'							)
	parser.add_argument('--task'					,				type=UpperCAmelCase_					,				default='summarization'					,				help='used for task_specific_params + metrics'							)
	parser.add_argument('--bs'					,				type=UpperCAmelCase_					,				default=8					,				required=UpperCAmelCase_					,				help='batch size'							)
	parser.add_argument(
	    '--n_obs'					,				type=UpperCAmelCase_					,				default=-1					,				required=UpperCAmelCase_					,				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=UpperCAmelCase_					,				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
	snake_case__       ,  snake_case__	       =   parser.parse_known_args()
	snake_case__	       =   parse_numeric_n_bool_cl_kwargs(UpperCAmelCase_							)
	if parsed_args and verbose:
		print(f'''parsed the following generate kwargs: {parsed_args}'''							)
	snake_case__	       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
	if args.n_obs > 0:
		snake_case__	       =   examples[: args.n_obs]
	Path(args.save_path							).parent.mkdir(exist_ok=UpperCAmelCase_							)

	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'							)

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

	if args.reference_path is None:
		return {}

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

	if args.dump_args:
		scores.update(UpperCAmelCase_							)
	if args.info:
		snake_case__	       =   args.info

	if verbose:
		print(UpperCAmelCase_							)

	if args.score_path is not None:
		json.dump(UpperCAmelCase_					,				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)










'''simple docstring'''




from collections import Counter
from timeit import timeit




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""					,				)   ->bool:
	return sum(c % 2 for c in Counter(input_str.replace(' '					,				''							).lower()							).values()							) < 2




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->bool:
	if len(UpperCAmelCase_							) == 0:
		return True
	snake_case__	       =   input_str.replace(' '					,				''							).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	snake_case__	       =   {}

	for character in lower_case_input_str:
		snake_case__	       =   character_freq_dict.get(UpperCAmelCase_					,				0							) + 1
	snake_case__	       =   0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->None:
	print('\nFor string = '					,				UpperCAmelCase_					,				':'							)
	print(
	    '> can_string_be_rearranged_as_palindrome_counter()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)
	print(
	    '> can_string_be_rearranged_as_palindrome()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)


if __name__ == "__main__":
			a__					:			str          =     input(
			    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
			).strip()
			benchmark(check_str)
			a__					:			List[Any]          =     can_string_be_rearranged_as_palindrome_counter(check_str)
			print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")



