


'''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 	_A				(lowerCAmelCase__  :Dataset				,						lowerCAmelCase__  :Dict[str, str]		)   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							args.log_outputs
     _a			      =							'_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     _a			      =							load_metric('wer'		)
     _a			      =							load_metric('cer'		)

     # compute metrics
     _a			      =							wer.compute(references=result['target']				,						predictions=result['prediction']		)
     _a			      =							cer.compute(references=result['target']				,						predictions=result['prediction']		)

     # print & log results
     _a			      =							f'WER: {wer_result}\nCER: {cer_result}'
     print(lowerCAmelCase__		)

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

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

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

               result.map(lowerCAmelCase__				,						with_indices=lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :str		)   ->  str:




     '''simple docstring'''



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

     _a			      =							re.sub(lowerCAmelCase__				,						''				,						text.lower()		)

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

     for t in token_sequences_to_ignore:
          _a			      =							' '.join(text.split(lowerCAmelCase__		)		)

     return text






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							load_dataset(args.dataset				,						args.config				,						split=args.split				,						use_auth_token=lowerCAmelCase__		)

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

     # load processor
     _a			      =							AutoFeatureExtractor.from_pretrained(args.model_id		)
     _a			      =							feature_extractor.sampling_rate

     # resample audio
     _a			      =							dataset.cast_column('audio'				,						Audio(sampling_rate=lowerCAmelCase__		)		)

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

     # map function to decode audio
     def map_to_pred(lowerCAmelCase__  :str		):
          _a			      =							asr(
              batch['audio']['array']				,						chunk_length_s=args.chunk_length_s				,						stride_length_s=args.stride_length_s		)

          _a			      =							prediction['text']
          _a			      =							normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     _a			      =							dataset.map(lowerCAmelCase__				,						remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 a_					:    Optional[int]          =    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.",
 )
 a_					:    Tuple          =    parser.parse_args()

 main(args)





'''simple docstring'''






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def 	_A				(lowerCAmelCase__  :np.ndarray		)   ->  np.ndarray:




     '''simple docstring'''



     return input_array.reshape((input_array.size, 1)		)






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.mean(1		)
          # Centralize the data of class i
          _a			      =							data - column_reshape(lowerCAmelCase__		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(lowerCAmelCase__				,						centered_data.T		)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							features.mean(1		)
     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.shape[1]
          _a			      =							data.mean(1		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     if features.any():
          _a			      =							features.mean(1		)
          # Center the dataset
          _a			      =							features - np.reshape(lowerCAmelCase__				,						(data_mean.size, 1)		)
          _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		) / features.shape[1]
          _a      ,   _a			      =							np.linalg.eigh(lowerCAmelCase__		)
          # Take all the columns in the reverse order (-1), and then takes only the first
          _a			      =							eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          _a			      =							np.dot(filtered_eigenvectors.T				,						lowerCAmelCase__		)
          logging.info('Principal Component Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a      ,   _a			      =							eigh(
              covariance_between_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						covariance_within_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						)
          _a			      =							eigenvectors[:, ::-1][:, :dimensions]
          _a      ,   _a      ,   _a			      =							np.linalg.svd(lowerCAmelCase__		)
          _a			      =							svd_matrix[:, 0:dimensions]
          _a			      =							np.dot(filtered_svd_matrix.T				,						lowerCAmelCase__		)
          logging.info('Linear Discriminant Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
     _a			      =							np.array([0, 0, 0, 1, 1]		)
     _a			      =							2
     _a			      =							2

     # Assert that the function raises an AssertionError if dimensions > classes
     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							linear_discriminant_analysis(
              lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
          if isinstance(lowerCAmelCase__				,						np.ndarray		):
               raise AssertionError(
                   'Did not raise AssertionError for dimensions > classes'		)
          assert error_info.type is AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
     _a			      =							2
     _a			      =							np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							principal_component_analysis(lowerCAmelCase__				,						lowerCAmelCase__		)
          if not np.allclose(lowerCAmelCase__				,						lowerCAmelCase__		):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
 import doctest

 doctest.testmod()

