



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							       =		getLogger(__name__)


__A							       =		'''cuda''' if torch.cuda.is_available() else '''cpu'''



def 			__a	(       lowerCAmelCase_  :   int     ,lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   Optional[int] = 8     ,lowerCAmelCase_  :   Union[str, Any] = DEFAULT_DEVICE     ,lowerCAmelCase_  :   str=False     ,lowerCAmelCase_  :   Union[str, Any]="summarization"     ,lowerCAmelCase_  :   Any=None     ,**lowerCAmelCase_  :   int     ,) ->					Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=  Path(A__							).open("""w"""     ,encoding="""utf-8"""							)
						UpperCAmelCase_=  str(A__							)
						UpperCAmelCase_=  AutoModelForSeqaSeqLM.from_pretrained(A__							).to(A__							)
						if fpaa:
												UpperCAmelCase_=  model.half()

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

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



def 			__a	(       ) ->					Tuple:






						'''simple docstring'''



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



def 			__a	(       lowerCAmelCase_  :   Optional[Any]=True							) ->					str:






						'''simple docstring'''



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

						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"""							)

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

						if args.reference_path is None:
												return {}

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

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

						if verbose:
												print(A__							)

						if args.score_path is not None:
												json.dump(A__     ,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 comet  # From: unbabel-comet
import torch

import datasets


lowercase__											=datasets.logging.get_logger(__name__)

lowercase__											='\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

lowercase__											='\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

lowercase__											='\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,  _KWARGS_DESCRIPTION					)
class   a_ (      datasets.Metric					):




			def 					lowerCAmelCase__					(       self	):
				return datasets.MetricInfo(
				    description=_DESCRIPTION					,   citation=_CITATION					,   homepage="""https://unbabel.github.io/COMET/html/index.html"""					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
				        {
				            """sources""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """predictions""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """references""": datasets.Value("""string"""					,   id="""sequence"""	),
				        }	)					,   codebase_urls=["""https://github.com/Unbabel/COMET"""]					,   reference_urls=[
				        """https://github.com/Unbabel/COMET""",
				        """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
				        """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
				    ]					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if self.config_name == "default":
					a_      				=						comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""	)	)
				else:
					a_      				=						comet.load_from_checkpoint(comet.download_model(self.config_name	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase=False	):
				if gpus is None:
					a_      				=						1 if torch.cuda.is_available() else 0
				a_      				=						{"""src""": sources, """mt""": predictions, """ref""": references}
				a_      				=						[dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	) for t in zip(*data.values()	)]
				a_				,  a_      				=						self.scorer.predict(UpperCAmelCase					,   gpus=UpperCAmelCase					,   progress_bar=UpperCAmelCase	)
				return {"mean_score": mean_score, "scores": scores}
