

'''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 						_A	(       A__	,							A__	,							A__	,							A__ = 8	,							A__ = DEFAULT_DEVICE	,							A__=False	,							A__="summarization"	,							A__=None	,							**A__	,							):






 """simple docstring"""



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

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

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


def 						_A	(       ):






 """simple docstring"""



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


def 						_A	(       A__=True ):






 """simple docstring"""



 __lowercase				    =					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
 __lowercase    ,      __lowercase				    =					parser.parse_known_args()
 __lowercase				    =					parse_numeric_n_bool_cl_kwargs(A__ )
 if parsed_args and verbose:
  print(F"parsed the following generate kwargs: {parsed_args}" )
 __lowercase				    =					[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
 if args.n_obs > 0:
  __lowercase				    =					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''' )

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

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


from jiwer import compute_measures

import datasets


A_    							=					"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

A_    							=					"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

A_    							=					"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""




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




				def        UpperCAmelCase__						(				self   ):
							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/jitsi/jiwer/''']							,						reference_urls=[
							        '''https://en.wikipedia.org/wiki/Word_error_rate''',
							    ]							,						)





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=False   ):
							if concatenate_texts:
										return compute_measures(UpperCAmelCase							,						UpperCAmelCase   )["wer"]
							else:
										lowerCamelCase_					=  0
										lowerCamelCase_					=  0
										for prediction, reference in zip(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  compute_measures(UpperCAmelCase							,						UpperCAmelCase   )
													incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
													total += measures["substitutions"] + measures["deletions"] + measures["hits"]
										return incorrect / total



