import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__UpperCamelCase		:       Dict									=     '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

__UpperCamelCase		:       int									=     '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

__UpperCamelCase		:       Any									=     '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'





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


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												return datasets.MetricInfo(
												    description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
												        {
												            '''predictions''': datasets.Value('''string'''    ,		id='''sequence'''						),
												            '''references''': datasets.Value('''string'''    ,		id='''sequence'''						),
												        }						)    ,		codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']    ,		reference_urls=[
												        '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
												        '''https://github.com/google-research/google-research/tree/master/rouge''',
												    ]    ,		)



						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				int=None    ,		UpperCamelCase__	:				Tuple=True    ,		UpperCamelCase__	:				int=False						):





												'''simple docstring'''





												if rouge_types is None:
																		SCREAMING_SNAKE_CASE							:    Dict							=							['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

												SCREAMING_SNAKE_CASE							:    Tuple							=							rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__    ,		use_stemmer=UpperCamelCase__						)
												if use_aggregator:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scoring.BootstrapAggregator()
												else:
																		SCREAMING_SNAKE_CASE							:    int							=							[]

												for ref, pred in zip(UpperCamelCase__    ,		UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    List[str]							=							scorer.score(UpperCamelCase__    ,		UpperCamelCase__						)
																		if use_aggregator:
																								aggregator.add_scores(UpperCamelCase__						)
																		else:
																								scores.append(UpperCamelCase__						)

												if use_aggregator:
																		SCREAMING_SNAKE_CASE							:    int							=							aggregator.aggregate()
												else:
																		SCREAMING_SNAKE_CASE							:    str							=							{}
																		for key in scores[0]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							[score[key] for score in scores]

												return result






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		:       Any									=     getLogger(__name__)


__UpperCamelCase		:       int									=     'cuda' if torch.cuda.is_available() else 'cpu'
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = DEFAULT_DEVICE		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Path(_lowercase						).open('''w'''		,  encoding='''utf-8'''						)
						SCREAMING_SNAKE_CASE							:    int							=							str(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).to(_lowercase						)
						if fpaa:
												SCREAMING_SNAKE_CASE							:    Dict							=							model.half()

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoTokenizer.from_pretrained(_lowercase						)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						# update config with task specific params
						use_task_specific_params(_lowercase		,  _lowercase						)
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						for examples_chunk in tqdm(list(chunks(_lowercase		,  _lowercase						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''						).to(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							model.generate(
												    input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.batch_decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase						)
												for hypothesis in dec:
																		fout.write(hypothesis + '''\n'''						)
																		fout.flush()
						fout.close()
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(time.time() - start_time						)  # seconds
						SCREAMING_SNAKE_CASE							:    str							=							len(_lowercase						)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4						)}
def       A	(    ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)
def       A	(    _lowercase=True						):
						SCREAMING_SNAKE_CASE							:    int							=							argparse.ArgumentParser()
						parser.add_argument('''model_name'''		,  type=_lowercase		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
						parser.add_argument('''input_path'''		,  type=_lowercase		,  help='''like cnn_dm/test.source'''						)
						parser.add_argument('''save_path'''		,  type=_lowercase		,  help='''where to save summaries'''						)
						parser.add_argument('''--reference_path'''		,  type=_lowercase		,  required=_lowercase		,  help='''like cnn_dm/test.target'''						)
						parser.add_argument('''--score_path'''		,  type=_lowercase		,  required=_lowercase		,  default='''metrics.json'''		,  help='''where to save metrics'''						)
						parser.add_argument('''--device'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''cuda, cuda:1, cpu etc.'''						)
						parser.add_argument(
						    '''--prefix'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''will be added to the begininng of src examples'''						)
						parser.add_argument('''--task'''		,  type=_lowercase		,  default='''summarization'''		,  help='''used for task_specific_params + metrics'''						)
						parser.add_argument('''--bs'''		,  type=_lowercase		,  default=8		,  required=_lowercase		,  help='''batch size'''						)
						parser.add_argument(
						    '''--n_obs'''		,  type=_lowercase		,  default=-1		,  required=_lowercase		,  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=_lowercase		,  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
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
						if args.n_obs > 0:
												SCREAMING_SNAKE_CASE							:    Any							=							examples[: args.n_obs]
						Path(args.save_path						).parent.mkdir(exist_ok=_lowercase						)

						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'''						)

						SCREAMING_SNAKE_CASE							:    List[str]							=							generate_summaries_or_translations(
						    _lowercase		,  args.save_path		,  args.model_name		,  batch_size=args.bs		,  device=args.device		,  fpaa=args.fpaa		,  task=args.task		,  prefix=args.prefix		,  **_lowercase		,  )

						if args.reference_path is None:
												return {}

						# Compute scores
						SCREAMING_SNAKE_CASE							:    Dict							=							calculate_bleu if '''translation''' in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[x.rstrip() for x in open(args.save_path						).readlines()]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowercase						)]
						SCREAMING_SNAKE_CASE							:    dict							=							score_fn(_lowercase		,  _lowercase						)
						scores.update(_lowercase						)

						if args.dump_args:
												scores.update(_lowercase						)
						if args.info:
												SCREAMING_SNAKE_CASE							:    Tuple							=							args.info

						if verbose:
												print(_lowercase						)

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





