'''simple docstring'''





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def     __lowerCAmelCase						(       a_       ,      a_=()       ,      a_=None       ,      a_="no"       ,      a_="29500"		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       False
     SCREAMING_SNAKE_CASE					:			int    	=       False
     if any(key.startswith('KAGGLE'		) for key in os.environ.keys()		):
          SCREAMING_SNAKE_CASE					:			int    	=       True
     elif "IPython" in sys.modules:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'google.colab' in str(sys.modules['IPython'].get_ipython()		)

     try:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       PrecisionType(mixed_precision.lower()		)
     except ValueError:
          raise ValueError(
              f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""		)

     if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'       ,      a_		) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state		) > 0:
               raise ValueError(
                   'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
                   'your training function. Restart your notebook and make sure no cells initializes an '
                   '`Accelerator`.'		)
          if num_processes is None:
               SCREAMING_SNAKE_CASE					:			Any    	=       8

          SCREAMING_SNAKE_CASE					:			List[Any]    	=       PrepareForLaunch(a_       ,      distributed_type='TPU'		)
          print(f"""Launching a training on {num_processes} TPU cores."""		)
          xmp.spawn(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print('Launching training on one GPU.'		)
          else:
               print('Launching training on one CPU.'		)
          function(*a_		)
     else:
          if num_processes is None:
               raise ValueError(
                   'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'		)

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state		) > 0:
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
                        'inside your training function. Restart your notebook and make sure no cells initializes an '
                        '`Accelerator`.'		)

               if torch.cuda.is_initialized():
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
                        'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
                        'function.'		)

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=a_       ,      master_addr='127.0.01'       ,      master_port=a_       ,      mixed_precision=a_		):
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       PrepareForLaunch(a_       ,      distributed_type='MULTI_GPU'		)
                    print(f"""Launching training on {num_processes} GPUs."""		)
                    try:
                         start_processes(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                                  'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                                  'Please review your imports and test them when running the `notebook_launcher()` to identify '
                                  'which one is problematic.'		) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    SCREAMING_SNAKE_CASE					:			Tuple    	=       '1'
                    print('Launching training on MPS.'		)
               elif torch.cuda.is_available():
                    print('Launching training on one GPU.'		)
               else:
                    print('Launching training on CPU.'		)
               function(*a_		)


def     __lowerCAmelCase						(       a_       ,      a_=()       ,      a_=2		)      ->	List[Any]:





     '''simple docstring'''





     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=a_       ,      master_addr='127.0.01'       ,      master_port='29500'       ,      accelerate_mixed_precision='no'       ,      accelerate_debug_rdv_file=tmp_file.name       ,      accelerate_use_cpu='yes'       ,      ):
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       PrepareForLaunch(a_       ,      debug=a_		)
               start_processes(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)




'''simple docstring'''





import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       f"""WER: {wer_result}\nCER: {cer_result}"""
     print(a_		)

     with open(f"""{dataset_id}_eval_results.txt"""       ,      'w'		) as f:
          f.write(a_		)

     # log all results in text file. Possibly interesting for analysis
     if log_outputs is not None:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       f"""log_{dataset_id}_targets.txt"""

          with open(a_       ,      'w'		) as p, open(a_       ,      'w'		) as t:
               # mapping function to write output
               def write_to_file(a_       ,      a_		):
                    p.write(f"""{i}""" + '\n'		)
                    p.write(batch['prediction'] + '\n'		)
                    t.write(f"""{i}""" + '\n'		)
                    t.write(batch['target'] + '\n'		)

               result.map(a_       ,      with_indices=a_		)


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       re.sub(a_       ,      ''       ,      text.lower()		)

     # In addition, we can normalize the target text, e.g. removing new lines characters etc...
     # note that order is important here!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       load_dataset(args.dataset       ,      args.config       ,      split=args.split       ,      use_auth_token=a_		)

     # for testing: only process the first two examples as a test
     # dataset = dataset.select(range(10))

     # load processor
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

     # load eval pipeline
     if args.device is None:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       0 if torch.cuda.is_available() else -1
     SCREAMING_SNAKE_CASE					:			int    	=       pipeline('automatic-speech-recognition'       ,      model=args.model_id       ,      device=args.device		)

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

     # compute and log_results
     # do not change function below
     log_results(a_       ,      a_		)


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[Any]  				=    argparse.ArgumentParser()

 parser.add_argument(
     """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
 )
 parser.add_argument(
     """--dataset""",
     type=str,
     required=True,
     help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
 )
 parser.add_argument(
     """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
 )
 parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
 parser.add_argument(
     """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
 )
 parser.add_argument(
     """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
 )
 parser.add_argument(
     """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
 )
 parser.add_argument(
     """--device""",
     type=int,
     default=None,
     help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



