"""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(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							args.log_outputs
	_lowercase :    List[str]							=							'_'.join(args.dataset.split('/'			) + [args.config, args.split]			)

	# load metric
	_lowercase :    List[Any]							=							load_metric('wer'			)
	_lowercase :    Dict							=							load_metric('cer'			)

	# compute metrics
	_lowercase :    str							=							wer.compute(references=result['target']     ,predictions=result['prediction']			)
	_lowercase :    List[str]							=							cer.compute(references=result['target']     ,predictions=result['prediction']			)

	# print & log results
	_lowercase :    Optional[Any]							=							F'''WER: {wer_result}\nCER: {cer_result}'''
	print(__UpperCAmelCase			)

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		_lowercase :    Optional[int]							=							F'''log_{dataset_id}_predictions.txt'''
		_lowercase :    Dict							=							F'''log_{dataset_id}_targets.txt'''

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

			result.map(__UpperCAmelCase     ,with_indices=__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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

	_lowercase :    Optional[Any]							=							re.sub(__UpperCAmelCase     ,''     ,text.lower()			)

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

	for t in token_sequences_to_ignore:
		_lowercase :    Any							=							' '.join(text.split(__UpperCAmelCase			)			)

	return text


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							load_dataset(args.dataset     ,args.config     ,split=args.split     ,use_auth_token=__UpperCAmelCase			)

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

	# load processor
	_lowercase :    str							=							AutoFeatureExtractor.from_pretrained(args.model_id			)
	_lowercase :    str							=							feature_extractor.sampling_rate

	# resample audio
	_lowercase :    Optional[int]							=							dataset.cast_column('audio'     ,Audio(sampling_rate=__UpperCAmelCase			)			)

	# load eval pipeline
	if args.device is None:
		_lowercase :    Union[str, Any]							=							0 if torch.cuda.is_available() else -1
	_lowercase :    Optional[int]							=							pipeline('automatic-speech-recognition'     ,model=args.model_id     ,device=args.device			)

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

		_lowercase :    str							=							prediction['text']
		_lowercase :    List[Any]							=							normalize_text(batch['sentence']			)
		return batch

	# run inference on all examples
	_lowercase :    List[str]							=							dataset.map(__UpperCAmelCase     ,remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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.',
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()

						main(args)




"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["input_features", "attention_mask"]







				def __init__( self    :       int			,					lowerCamelCase_    :       List[str]=8_0			,					lowerCamelCase_    :       Tuple=1_6_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       List[Any]=1_0			,					lowerCamelCase_    :       List[str]=2_5			,					lowerCamelCase_    :       List[Any]="hamming_window"			,					lowerCamelCase_    :       Tuple=3_2768.0			,					lowerCamelCase_    :       int=0.97			,					lowerCamelCase_    :       Optional[int]=1.0			,					lowerCamelCase_    :       Optional[Any]=True			,					lowerCamelCase_    :       Union[str, Any]=True			,					lowerCamelCase_    :       Optional[int]=False			,					**lowerCamelCase_    :       Optional[Any]			,					):

					"""simple docstring"""






					super().__init__(feature_size=lowerCamelCase_			,					sampling_rate=lowerCamelCase_			,					padding_value=lowerCamelCase_			,					**lowerCamelCase_						)

					_lowercase :    Dict							=							feature_size
					_lowercase :    Dict							=							sampling_rate
					_lowercase :    Tuple							=							padding_value
					_lowercase :    int							=							hop_length
					_lowercase :    Any							=							win_length
					_lowercase :    Union[str, Any]							=							frame_signal_scale
					_lowercase :    Tuple							=							preemphasis_coeff
					_lowercase :    Tuple							=							mel_floor
					_lowercase :    Tuple							=							normalize_means
					_lowercase :    List[Any]							=							normalize_vars
					_lowercase :    List[str]							=							win_function
					_lowercase :    int							=							return_attention_mask

					_lowercase :    Optional[Any]							=							win_length * sampling_rate // 1_0_0_0
					_lowercase :    Tuple							=							hop_length * sampling_rate // 1_0_0_0

					_lowercase :    str							=							optimal_fft_length(self.sample_size						)
					_lowercase :    Dict							=							(self.n_fft // 2) + 1







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       np.array						):

					"""simple docstring"""






					if self.win_function == "hamming_window":
						_lowercase :    List[Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function			,					periodic=lowerCamelCase_						)
					else:
						_lowercase :    Union[str, Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function						)

					_lowercase :    Tuple							=							mel_filter_bank(
					    num_frequency_bins=self.n_freqs			,					num_mel_filters=self.feature_size			,					min_frequency=0.0			,					max_frequency=self.sampling_rate / 2.0			,					sampling_rate=self.sampling_rate			,					)

					_lowercase :    Tuple							=							spectrogram(
					    one_waveform * self.frame_signal_scale			,					window=lowerCamelCase_			,					frame_length=self.sample_size			,					hop_length=self.sample_stride			,					fft_length=self.n_fft			,					center=lowerCamelCase_			,					preemphasis=self.preemphasis_coeff			,					mel_filters=lowerCamelCase_			,					mel_floor=self.mel_floor			,					log_mel='log'			,					)
					return msfc_features.T







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					if self.normalize_means:
						_lowercase :    Optional[int]							=							x[:input_length].mean(axis=0						)
						_lowercase :    int							=							np.subtract(lowerCamelCase_			,					lowerCamelCase_						)
					if self.normalize_vars:
						_lowercase :    int							=							x[:input_length].std(axis=0						)
						_lowercase :    Optional[Any]							=							np.divide(lowerCamelCase_			,					lowerCamelCase_						)

					if input_length < x.shape[0]:
						_lowercase :    Dict							=							padding_value

					# make sure array is in float32
					_lowercase :    Tuple							=							x.astype(np.floataa						)

					return x







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[np.ndarray]			,					lowerCamelCase_    :       Optional[np.ndarray] = None						):

					"""simple docstring"""






					_lowercase :    Dict							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
					return [self._normalize_one(lowerCamelCase_			,					lowerCamelCase_			,					self.padding_value						) for x, n in zip(lowerCamelCase_			,					lowerCamelCase_						)]







				def __call__( self    :       Dict			,					lowerCamelCase_    :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,					lowerCamelCase_    :       Union[bool, str, PaddingStrategy] = False			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					lowerCamelCase_    :       Optional[int] = None			,					**lowerCamelCase_    :       Optional[int]			,					):

					"""simple docstring"""






					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
							    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
							    F''' {self.sampling_rate} and not {sampling_rate}.'''						)
					else:
						logger.warning(
						    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.'						)

					_lowercase :    Optional[Any]							=							isinstance(lowerCamelCase_			,					np.ndarray						) and len(raw_speech.shape						) > 1
					if is_batched_numpy and len(raw_speech.shape						) > 2:
						raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''						)
					_lowercase :    Optional[int]							=							is_batched_numpy or (
					    isinstance(lowerCamelCase_			,					(list, tuple)						) and (isinstance(raw_speech[0]			,					(np.ndarray, tuple, list)						))
					)

					if is_batched:
						_lowercase :    str							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for speech in raw_speech]
					elif not is_batched and not isinstance(lowerCamelCase_			,					np.ndarray						):
						_lowercase :    Tuple							=							np.asarray(lowerCamelCase_			,					dtype=np.floataa						)
					elif isinstance(lowerCamelCase_			,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
						_lowercase :    Optional[int]							=							raw_speech.astype(np.floataa						)

					# always return batch
					if not is_batched:
						_lowercase :    str							=							[raw_speech]

					# extract fbank features
					_lowercase :    Optional[Any]							=							[self._extract_mfsc_features(lowerCamelCase_						) for one_waveform in raw_speech]

					# convert into correct format for padding
					_lowercase :    Optional[int]							=							BatchFeature({'input_features': features}						)

					_lowercase :    Tuple							=							self.pad(
					    lowerCamelCase_			,					padding=lowerCamelCase_			,					max_length=lowerCamelCase_			,					truncation=lowerCamelCase_			,					pad_to_multiple_of=lowerCamelCase_			,					return_attention_mask=lowerCamelCase_			,					**lowerCamelCase_			,					)
					# make sure list is in array format
					_lowercase :    Dict							=							padded_inputs.get('input_features'						)
					if isinstance(input_features[0]			,					lowerCamelCase_						):
						_lowercase :    List[str]							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for feature in input_features]

					_lowercase :    List[Any]							=							padded_inputs.get('attention_mask'						)
					if attention_mask is not None:
						_lowercase :    Union[str, Any]							=							[np.asarray(lowerCamelCase_			,					dtype=np.intaa						) for array in attention_mask]

					if self.normalize_means or self.normalize_vars:
						_lowercase :    int							=							(
						    np.array(lowerCamelCase_			,					dtype=np.intaa						)
						    if self._get_padding_strategies(lowerCamelCase_			,					max_length=lowerCamelCase_						) is not PaddingStrategy.DO_NOT_PAD
						    and padding
						    else None
						)
						_lowercase :    List[Any]							=							self.normalize(
						    padded_inputs['input_features']			,					attention_mask=lowerCamelCase_						)

					if return_tensors is not None:
						_lowercase :    Union[str, Any]							=							padded_inputs.convert_to_tensors(lowerCamelCase_						)

					return padded_inputs



