'''simple docstring'''





from importlib import import_module

from .logging import get_logger


_lowerCAmelCase  :Dict  				=    get_logger(__name__)






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=None )							-> Any:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attrs or []
         if module is not None:
              for key in module.__dict__:
                   if key in attrs or not key.startswith('__' ):
                        setattr(self    ,							lowercase__    ,							getattr(lowercase__    ,							lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       module._original_module if isinstance(lowercase__    ,							_PatchedModuleObj ) else module






class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   Dict							=						[]






    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=None )							-> int:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       obj
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       target
         SCREAMING_SNAKE_CASE					:			int    	=       new
         SCREAMING_SNAKE_CASE					:			str    	=       target.split('.' )[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       attrs or []






    def __enter__(       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.target.split('.' )

         # Patch modules:
         # it's used to patch attributes of submodules like "os.path.join";
         # in this case we need to patch "os" and "os.path"

         for i in range(len(lowercase__ ) ):
              try:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       import_module('.'.join(submodules[: i + 1] ) )
              except ModuleNotFoundError:
                   continue
              # We iterate over all the globals in self.obj in case we find "os" or "os.path"
              for attr in self.obj.__dir__():
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       getattr(self.obj    ,							lowercase__ )
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(lowercase__    ,							_PatchedModuleObj ) and obj_attr._original_module is submodule)
                   ):
                        SCREAMING_SNAKE_CASE					:			int    	=       obj_attr
                        # patch at top level
                        setattr(self.obj    ,							lowercase__    ,							_PatchedModuleObj(lowercase__    ,							attrs=self.attrs ) )
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       getattr(self.obj    ,							lowercase__ )
                        # construct lower levels patches
                        for key in submodules[i + 1 :]:
                             setattr(lowercase__    ,							lowercase__    ,							_PatchedModuleObj(getattr(lowercase__    ,							lowercase__    ,							lowercase__ )    ,							attrs=self.attrs ) )
                             SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							lowercase__ )
                        # finally set the target attribute
                        setattr(lowercase__    ,							lowercase__    ,							self.new )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

         if submodules:  # if it's an attribute of a submodule like "os.path.join"
              try:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(import_module('.'.join(lowercase__ ) )    ,							lowercase__ )
              except (AttributeError, ModuleNotFoundError):
                   return
              # We iterate over all the globals in self.obj in case we find "os.path.join"
              for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj    ,							lowercase__ ) is attr_value:
                        SCREAMING_SNAKE_CASE					:			Any    	=       getattr(self.obj    ,							lowercase__ )
                        setattr(self.obj    ,							lowercase__    ,							self.new )
         elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
              SCREAMING_SNAKE_CASE					:			str    	=       globals()['__builtins__'][target_attr]
              setattr(self.obj    ,							lowercase__    ,							self.new )
         else:
              raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )






    def __exit__(       self    ,							*lowercase__ )							-> Optional[int]:
         for attr in list(self.original ):
              setattr(self.obj    ,							lowercase__    ,							self.original.pop(lowercase__ ) )






    def       _UpperCamelCase    (       self )							-> Dict:
         self.__enter__()
         self._active_patches.append(self )






    def       _UpperCamelCase    (       self )							-> str:
         try:
              self._active_patches.remove(self )
         except ValueError:
              # If the patch hasn't been started this will fail
              return None

         return self.__exit__()




'''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)



