
"""simple docstring"""







import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowerCamelCase__        =		"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowerCamelCase__        =		"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

lowerCamelCase__        =		"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"




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

		def 							__lowerCamelCase							(				self			):
			if version.parse(scb.__version__			) < version.parse('1.4.12'			):
				raise ImportWarning(
				    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
				    'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
			return datasets.MetricInfo(
			    description=_DESCRIPTION	,		citation=_CITATION	,		homepage='http://www.cs.umd.edu/~snover/tercom/'	,		inputs_description=_KWARGS_DESCRIPTION	,		features=datasets.Features(
			        {
			            'predictions': datasets.Value('string'	,		id='sequence'			),
			            'references': datasets.Sequence(datasets.Value('string'	,		id='sequence'			)	,		id='references'			),
			        }			)	,		codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']	,		reference_urls=[
			        'https://github.com/jhclark/tercom',
			    ]	,		)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		):
			__lowerCAmelCase      :						List[str]						      =				len(references[0]			)
			if any(len(_SCREAMING_SNAKE_CASE			) != references_per_prediction for refs in references			):
				raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
			__lowerCAmelCase      :						int						      =				[[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE			)]

			__lowerCAmelCase      :						str						      =				TER(
			    normalized=_SCREAMING_SNAKE_CASE	,		no_punct=_SCREAMING_SNAKE_CASE	,		asian_support=_SCREAMING_SNAKE_CASE	,		case_sensitive=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				sb_ter.corpus_score(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}

"""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


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""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	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

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

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				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()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,  4		)}
def    __lowerCAmelCase    ():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)
def    __lowerCAmelCase    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				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
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + 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      :						int						      =				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'		)

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

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				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)