



'''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


_UpperCAmelCase				: Tuple							    =				getLogger(__name__)


_UpperCAmelCase				: str							    =				"""cuda""" if torch.cuda.is_available() else """cpu"""

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase = 8,				lowerCamelCase = DEFAULT_DEVICE,				lowerCamelCase=False,				lowerCamelCase="summarization",				lowerCamelCase=None,				**lowerCamelCase,				):
						__lowerCAmelCase							       =				Path(lowerCamelCase).open('''w''',				encoding='''utf-8''')
						__lowerCAmelCase							       =				str(lowerCamelCase)
						__lowerCAmelCase							       =				AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase).to(lowerCamelCase)
						if fpaa:
												__lowerCAmelCase							       =				model.half()

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

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

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

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

						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''')

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

						if args.reference_path is None:
												return {}

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

						if args.dump_args:
												scores.update(lowerCamelCase)
						if args.info:
												__lowerCAmelCase							       =				args.info

						if verbose:
												print(lowerCamelCase)

						if args.score_path is not None:
												json.dump(lowerCamelCase,				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'''

import requests
from bsa import BeautifulSoup

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				BeautifulSoup(requests.get(lowerCamelCase,				params=lowerCamelCase).content,				'''html.parser''')
						__lowerCAmelCase							       =				soup.find('''div''',				attrs={'''class''': '''gs_ri'''})
						__lowerCAmelCase							       =				div.find('''div''',				attrs={'''class''': '''gs_fl'''}).find_all('''a''')
						return anchors[2].get_text()


if __name__ == "__main__":
	_UpperCAmelCase				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

