



from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
UpperCamelCase_									=   '\\n'

UpperCamelCase_									=   '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

UpperCamelCase_									=   '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

UpperCamelCase_									=   ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
UpperCamelCase_									=   ''

try:
      from jiwer import transforms as tr

      UpperCamelCase_									=   True
except ImportError:
      UpperCamelCase_									=   False

if _jiwer_available and version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):







      class       _snake_case				(     tr.AbstractTransform ):






         '''simple docstring'''






         def __init__(    self:     int  ,lowerCamelCase_:     str = " "							)	->      Optional[int]:
             UpperCAmelCase_     :       Union[str, Any]  		=					sentence_delimiter


         def       A__    (    self:     Tuple  ,lowerCamelCase_:     str							)	->      Any:
             return list(snake_case__							)






         def       A__    (    self:     int  ,lowerCamelCase_:     List[str]							)	->      Union[str, Any]:
             UpperCAmelCase_     :       List[Any]  		=					[]
             for sent_idx, sentence in enumerate(snake_case__							):
                 chars.extend(self.process_string(snake_case__							)							)
                 if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case__							) - 1:
                     chars.append(self.sentence_delimiter							)
             return chars


      UpperCamelCase_									=   tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      UpperCamelCase_									=   tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      UpperCamelCase_									=   None







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						Dict    ):



    '''simple docstring'''




    return float((preds == labels).mean()    )







def       lowerCamelCase_  (    _a       :						Any   ,		_a       :						Any    ):



    '''simple docstring'''




    return {
        "f1": float(fa_score(y_true=snake_case_   ,		y_pred=snake_case_   ,		average="""macro"""    )    ),
        "accuracy": simple_accuracy(snake_case_   ,		snake_case_    ),
    }







def       lowerCamelCase_  (    _a       :						Dict   ,		_a       :						Union[str, Any]   ,		_a       :						Optional[int]="exp"   ,		_a       :						Tuple=None   ,		_a       :						List[str]=False   ,		_a       :						str=False   ,		_a       :						Union[str, Any]=None   ,		_a       :						int=False   ,		):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					[[label] for label in labels]
    UpperCAmelCase_     :       List[Any]  		=					list(snake_case_    )
    try:
        import sacrebleu as scb
    except ImportError:
        raise ValueError(
            """sacrebleu has to be installed in order to apply the bleu metric for covost2."""
            """You can install it via `pip install sacrebleu`."""    )

    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\"`."""    )

    UpperCAmelCase_     :       Optional[Any]  		=					len(labels[0]    )
    if any(len(snake_case_    ) != references_per_prediction for refs in labels    ):
        raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
    UpperCAmelCase_     :       Optional[Any]  		=					[[refs[i] for refs in labels] for i in range(snake_case_    )]
    UpperCAmelCase_     :       Union[str, Any]  		=					scb.corpus_bleu(
        snake_case_   ,		snake_case_   ,		smooth_method=snake_case_   ,		smooth_value=snake_case_   ,		force=snake_case_   ,		lowercase=snake_case_   ,		use_effective_order=snake_case_   ,		**({"""tokenize""": tokenize} if tokenize else {})   ,		)
    return {"bleu": output.score}







def       lowerCamelCase_  (    _a       :						Dict   ,		_a       :						List[str]   ,		_a       :						List[Any]   ,		_a       :						str    ):



    '''simple docstring'''




    try:
        from jiwer import compute_measures
    except ImportError:
        raise ValueError(
            F'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
            """You can install it via `pip install jiwer`."""    )

    if concatenate_texts:
        UpperCAmelCase_     :       List[str]  		=					compute_measures(snake_case_   ,		snake_case_    )["""wer"""]

        UpperCAmelCase_     :       Any  		=					compute_measures(snake_case_   ,		snake_case_   ,		truth_transform=snake_case_   ,		hypothesis_transform=snake_case_    )["""wer"""]
        return {"wer": wer, "cer": cer}
    else:

        def compute_score(_a       :						Tuple   ,		_a       :						List[str]   ,		_a       :						Optional[int]="wer"    ):
            UpperCAmelCase_     :       List[str]  		=					0
            UpperCAmelCase_     :       Optional[int]  		=					0
            for prediction, reference in zip(snake_case_   ,		snake_case_    ):
                if score_type == "wer":
                    UpperCAmelCase_     :       str  		=					compute_measures(snake_case_   ,		snake_case_    )
                elif score_type == "cer":
                    UpperCAmelCase_     :       Tuple  		=					compute_measures(
                        snake_case_   ,		snake_case_   ,		truth_transform=snake_case_   ,		hypothesis_transform=snake_case_    )
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total

        return {"wer": compute_score(snake_case_   ,		snake_case_   ,		"""wer"""    ), "cer": compute_score(snake_case_   ,		snake_case_   ,		"""cer"""    )}







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






   '''simple docstring'''






   def       A__    (    self:     List[Any]							)	->      int:


       if self.config_name not in _CONFIG_NAMES:
           raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''							)

       UpperCAmelCase_     :       List[Any]  		=					"""int64""" if self.config_name in ["""fleurs-lang_id""", """minds14"""] else """string"""

       return datasets.MetricInfo(
           description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
               {"""predictions""": datasets.Value(snake_case__							), """references""": datasets.Value(snake_case__							)}							)  ,codebase_urls=[]  ,reference_urls=[]  ,format="""numpy"""  ,)






   def       A__    (    self:     Dict  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     str=None  ,lowerCamelCase_:     List[Any]=None							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Tuple  		=					bleu_kwargs if bleu_kwargs is not None else {}
       UpperCAmelCase_     :       Tuple  		=					wer_kwargs if wer_kwargs is not None else {}

       if self.config_name == "fleurs-lang_id":
           return {"accuracy": simple_accuracy(snake_case__  ,snake_case__							)}
       elif self.config_name == "minds14":
           return fa_and_simple_accuracy(snake_case__  ,snake_case__							)
       elif self.config_name == "covost2":
           UpperCAmelCase_     :       List[Any]  		=					bleu_kwargs.pop("""smooth_method"""  ,"""exp"""							)
           UpperCAmelCase_     :       Any  		=					bleu_kwargs.pop("""smooth_value"""  ,snake_case__							)
           UpperCAmelCase_     :       str  		=					bleu_kwargs.pop("""force"""  ,snake_case__							)
           UpperCAmelCase_     :       Dict  		=					bleu_kwargs.pop("""lowercase"""  ,snake_case__							)
           UpperCAmelCase_     :       Any  		=					bleu_kwargs.pop("""tokenize"""  ,snake_case__							)
           UpperCAmelCase_     :       Tuple  		=					bleu_kwargs.pop("""use_effective_order"""  ,snake_case__							)
           return bleu(
               preds=snake_case__  ,labels=snake_case__  ,smooth_method=snake_case__  ,smooth_value=snake_case__  ,force=snake_case__  ,lowercase=snake_case__  ,tokenize=snake_case__  ,use_effective_order=snake_case__  ,)
       elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
           UpperCAmelCase_     :       Optional[Any]  		=					wer_kwargs.pop("""concatenate_texts"""  ,snake_case__							)
           return wer_and_cer(snake_case__  ,snake_case__  ,snake_case__  ,self.config_name							)
       else:
           raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''							)






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_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


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

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


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


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

						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(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	args.info

						if verbose:
												print(snake_case_		)

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




