"""simple docstring"""






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A			   = 1_6
__A			   = 3_2



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = 1_6       ,    __UpperCAmelCase = "bert-base-cased" )  ->  Any:
   lowercase__:   Optional[Any]					=      AutoTokenizer.from_pretrained(__UpperCAmelCase )
   lowercase__:   int					=      load_dataset('''glue'''       ,    '''mrpc''' )

   def tokenize_function(__UpperCAmelCase ):
      # max_length=None => use the model max length (it's actually the default)
      lowercase__:   Tuple					=      tokenizer(examples['''sentence1''']       ,    examples['''sentence2''']       ,    truncation=__UpperCAmelCase       ,    max_length=__UpperCAmelCase )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   lowercase__:   Tuple					=      datasets.map(
       __UpperCAmelCase       ,    batched=__UpperCAmelCase       ,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,    load_from_cache_file=__UpperCAmelCase )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowercase__:   Optional[Any]					=      tokenized_datasets.rename_column('''label'''       ,    '''labels''' )

   def collate_fn(__UpperCAmelCase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(__UpperCAmelCase       ,    padding='''max_length'''       ,    max_length=1_2_8       ,    return_tensors='''pt''' )
      return tokenizer.pad(__UpperCAmelCase       ,    padding='''longest'''       ,    return_tensors='''pt''' )

   # Instantiate dataloaders.
   lowercase__:   Any					=      DataLoader(
       tokenized_datasets['''train''']       ,    shuffle=__UpperCAmelCase       ,    collate_fn=__UpperCAmelCase       ,    batch_size=__UpperCAmelCase )
   lowercase__:   Optional[int]					=      DataLoader(
       tokenized_datasets['''validation''']       ,    shuffle=__UpperCAmelCase       ,    collate_fn=__UpperCAmelCase       ,    batch_size=__UpperCAmelCase )

   return train_dataloader, eval_dataloader



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  str:
   # Initialize accelerator
   lowercase__:   List[str]					=      Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase__:   List[str]					=      config['''lr''']
   lowercase__:   List[str]					=      int(config['''num_epochs'''] )
   lowercase__:   Optional[Any]					=      int(config['''seed'''] )
   lowercase__:   Dict					=      int(config['''batch_size'''] )
   lowercase__:   str					=      args.model_name_or_path

   set_seed(__UpperCAmelCase )
   lowercase__, lowercase__:   Union[str, Any]					=      get_dataloaders(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase__:   Tuple					=      AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase       ,    return_dict=__UpperCAmelCase )

   # Instantiate optimizer
   lowercase__:   int					=      (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowercase__:   int					=      optimizer_cls(params=model.parameters()       ,    lr=__UpperCAmelCase )

   if accelerator.state.deepspeed_plugin is not None:
      lowercase__:   Dict					=      accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      lowercase__:   Dict					=      1
   lowercase__:   Optional[int]					=      (len(__UpperCAmelCase ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowercase__:   Optional[int]					=      get_linear_schedule_with_warmup(
          optimizer=__UpperCAmelCase       ,    num_warmup_steps=0       ,    num_training_steps=__UpperCAmelCase       ,    )
   else:
      lowercase__:   Union[str, Any]					=      DummyScheduler(__UpperCAmelCase       ,    total_num_steps=__UpperCAmelCase       ,    warmup_num_steps=0 )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   List[str]					=      accelerator.prepare(
       __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # We need to keep track of how many total steps we have iterated over
   lowercase__:   Union[str, Any]					=      0
   # We also need to keep track of the stating epoch so files are named properly
   lowercase__:   List[Any]					=      0

   # Now we train the model
   lowercase__:   List[str]					=      evaluate.load('''glue'''       ,    '''mrpc''' )
   lowercase__:   Tuple					=      0
   lowercase__:   int					=      {}
   for epoch in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      model.train()
      for step, batch in enumerate(__UpperCAmelCase ):
         lowercase__:   str					=      model(**__UpperCAmelCase )
         lowercase__:   Optional[int]					=      outputs.loss
         lowercase__:   List[Any]					=      loss / gradient_accumulation_steps
         accelerator.backward(__UpperCAmelCase )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

      model.eval()
      lowercase__:   str					=      0
      for step, batch in enumerate(__UpperCAmelCase ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device )
         with torch.no_grad():
            lowercase__:   Union[str, Any]					=      model(**__UpperCAmelCase )
         lowercase__:   Tuple					=      outputs.logits.argmax(dim=-1 )
         # It is slightly faster to call this once, than multiple times
         lowercase__, lowercase__:   Any					=      accelerator.gather(
             (predictions, batch['''labels''']) )  # If we are in a multiprocess environment, the last batch has duplicates
         if accelerator.use_distributed:
            if step == len(__UpperCAmelCase ) - 1:
               lowercase__:   Dict					=      predictions[: len(eval_dataloader.dataset ) - samples_seen]
               lowercase__:   Any					=      references[: len(eval_dataloader.dataset ) - samples_seen]
            else:
               samples_seen += references.shape[0]
         metric.add_batch(
             predictions=__UpperCAmelCase       ,    references=__UpperCAmelCase       ,    )

      lowercase__:   List[str]					=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""       ,    __UpperCAmelCase )
      lowercase__:   List[Any]					=      eval_metric['''accuracy''']

      if best_performance < eval_metric["accuracy"]:
         lowercase__:   Optional[Any]					=      eval_metric['''accuracy''']

   if args.performance_lower_bound is not None:
      assert (
          args.performance_lower_bound <= best_performance
      ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir       ,    '''all_results.json''' )       ,    '''w''' ) as f:
         json.dump(__UpperCAmelCase       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   )  ->  Union[str, Any]:
   lowercase__:   int					=      argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
   parser.add_argument(
       '''--model_name_or_path'''       ,    type=__UpperCAmelCase       ,    default='''bert-base-cased'''       ,    help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,    required=__UpperCAmelCase       ,    )
   parser.add_argument(
       '''--output_dir'''       ,    type=__UpperCAmelCase       ,    default='''.'''       ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,    )
   parser.add_argument(
       '''--performance_lower_bound'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''       ,    )
   parser.add_argument(
       '''--num_epochs'''       ,    type=__UpperCAmelCase       ,    default=3       ,    help='''Number of train epochs.'''       ,    )
   lowercase__:   int					=      parser.parse_args()
   lowercase__:   Dict					=      {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
   training_function(__UpperCAmelCase       ,    __UpperCAmelCase )


if __name__ == "__main__":
       main()

"""simple docstring"""






from jiwer import compute_measures

import datasets


__A			   = "\\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"

__A			   = "\\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"

__A			   = "\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 _snake_case			(   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 _snake_case			(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=False							):
       if concatenate_texts:
          return compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)["wer"]
       else:
          lowercase__:   Dict					=      0
          lowercase__:   Union[str, Any]					=      0
          for prediction, reference in zip(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]
          return incorrect / total
