


from jiwer import compute_measures

import datasets


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

SCREAMING_SNAKE_CASE       :		Tuple       =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE       :		Optional[int]       =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






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


   '''simple docstring'''







   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(SCREAMING_SNAKE_CASE_   ,							SCREAMING_SNAKE_CASE_		)["wer"]
        else:
             lowercase_       :Optional[int]     =       0
             lowercase_       :Dict     =       0
             for prediction, reference in zip(SCREAMING_SNAKE_CASE_   ,							SCREAMING_SNAKE_CASE_		):
                  lowercase_       :Optional[int]     =       compute_measures(SCREAMING_SNAKE_CASE_   ,							SCREAMING_SNAKE_CASE_		)
                  incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                  total += measures["substitutions"] + measures["deletions"] + measures["hits"]
             return incorrect / total





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer






   class      UpperCamelCase					(		lowercase__     ):


      '''simple docstring'''







      def __init__(     self   ,							UpperCamelCase_ = 101		):
           lowercase_       :Tuple     =       length
      def __len__(     self		):
           return self.length




      def __getitem__(     self   ,							UpperCamelCase_		):
           return i






   class      UpperCamelCase					:


      '''simple docstring'''







      def __call__(     self   ,							UpperCamelCase_		):
           return {"input_ids": torch.tensor(UpperCamelCase_		), "labels": torch.tensor(UpperCamelCase_		)}






   class      UpperCamelCase					(		nn.Module     ):


      '''simple docstring'''







      def __init__(     self		):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           lowercase_       :List[Any]     =       nn.Linear(120   ,							80		)




      def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
           if labels is not None:
                return torch.tensor(0.0   ,							device=input_ids.device		), input_ids
           else:
                return input_ids






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_neuroncore
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :List[str]     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Any     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_multi_gpu
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :str     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Dict     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				HfArgumentParser((TrainingArguments,))
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
       f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
   )

   # Essentially, what we want to verify in the distributed case is that we get all samples back,
   # in the right order. (this is crucial for prediction for instance)
   for dataset_length in [101, 40, 7]:
      SCREAMING_SNAKE_CASE       :		Dict         =				DummyDataset(dataset_length)




      def     UpperCamelCase    (      _a		)					->							Dict:
           '''simple docstring'''





           lowercase_       :Optional[Any]     =       list(range(len(_a		)		)		)
           lowercase_       :Optional[Any]     =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
           if not success and training_args.local_rank == 0:
                logger.warning(
                    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
                    f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"		)
           return {"success": success}

      SCREAMING_SNAKE_CASE       :		Optional[Any]         =				Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Optional[int]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				2

      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		List[str]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)
      SCREAMING_SNAKE_CASE       :		Optional[int]         =				None

