





'''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


lowercase__       : int         =       getLogger(__name__)


lowercase__       : int         =       """cuda""" if torch.cuda.is_available() else """cpu"""






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					int = 8					,				__snake_case    :					str = DEFAULT_DEVICE					,				__snake_case    :					Any=False					,				__snake_case    :					Tuple="summarization"					,				__snake_case    :					List[Any]=None					,				**__snake_case    :					int					,				)	->	Dict:
     __A		:					Any						=			Path(SCREAMING_SNAKE_CASE__     ).open('w'					,				encoding='utf-8'     )
     __A		:					Dict						=			str(SCREAMING_SNAKE_CASE__     )
     __A		:					str						=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
     if fpaa:
          __A		:					List[Any]						=			model.half()

     __A		:					Union[str, Any]						=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
     logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'     )  # if this is wrong, check config.model_type.

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






def 			_lowerCAmelCase     (    )	->	List[str]:
     return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )






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

     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'     )

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

     if args.reference_path is None:
          return {}

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

     if args.dump_args:
          scores.update(SCREAMING_SNAKE_CASE__     )
     if args.info:
          __A		:					Dict						=			args.info

     if verbose:
          print(SCREAMING_SNAKE_CASE__     )

     if args.score_path is not None:
          json.dump(SCREAMING_SNAKE_CASE__					,				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 sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__UpperCamelCase   :							List[str]         =   """\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__UpperCamelCase   :							Union[str, Any]         =   """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

__UpperCamelCase   :							str         =   """
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION  )
class 				__SCREAMING_SNAKE_CASE							(				datasets.Metric  ):




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         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='https://github.com/mjpost/sacreBLEU#chrf--chrf'			, 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#chrf--chrf']			, reference_urls=[
                 'https://github.com/m-popovic/chrF',
             ]			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = CHRF.CHAR_ORDER			, lowerCamelCase = CHRF.WORD_ORDER			, lowerCamelCase = CHRF.BETA			, lowerCamelCase = False			, lowerCamelCase = False			, lowerCamelCase = False			, )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     len(references[0]   )
         if any(len(lowerCamelCase   ) != references_per_prediction for refs in references   ):
            raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
         __a					  =     [[refs[i] for refs in references] for i in range(lowerCamelCase   )]

         __a					  =     CHRF(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )
         __a					  =     sb_chrf.corpus_score(lowerCamelCase			, lowerCamelCase   )

         return {
             "score": output.score,
             "char_order": output.char_order,
             "word_order": output.word_order,
             "beta": output.beta,
         }