

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =["""input_features""", """is_longer"""]




							def __init__( self					,						_A=6_4					,						_A=4_8_0_0_0					,						_A=4_8_0					,						_A=1_0					,						_A=1_0_2_4					,						_A=0.0					,						_A=False					,						_A = 0					,						_A = 1_4_0_0_0					,						_A = None					,						_A = "fusion"					,						_A = "repeatpad"					,						**_A					,						):
												super().__init__(
												    feature_size=_A					,						sampling_rate=_A					,						padding_value=_A					,						return_attention_mask=_A					,						**_A					,						)
												__lowerCAmelCase					=							top_db
												__lowerCAmelCase					=							truncation
												__lowerCAmelCase					=							padding
												__lowerCAmelCase					=							fft_window_size
												__lowerCAmelCase					=							(fft_window_size >> 1) + 1
												__lowerCAmelCase					=							hop_length
												__lowerCAmelCase					=							max_length_s
												__lowerCAmelCase					=							max_length_s * sampling_rate
												__lowerCAmelCase					=							sampling_rate
												__lowerCAmelCase					=							frequency_min
												__lowerCAmelCase					=							frequency_max
												__lowerCAmelCase					=							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,						num_mel_filters=_A					,						min_frequency=_A					,						max_frequency=_A					,						sampling_rate=_A					,						norm=_A					,						mel_scale='htk'					,						)
												__lowerCAmelCase					=							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,						num_mel_filters=_A					,						min_frequency=_A					,						max_frequency=_A					,						sampling_rate=_A					,						norm='slaney'					,						mel_scale='slaney'					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							copy.deepcopy(self.__dict__							)
												__lowerCAmelCase					=							self.__class__.__name__
												if "mel_filters" in output:
																	del output["mel_filters"]
												if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
												return output




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							spectrogram(
												    _A					,						window_function(self.fft_window_size					,						'hann'							)					,						frame_length=self.fft_window_size					,						hop_length=self.hop_length					,						power=2.0					,						mel_filters=_A					,						log_mel='dB'					,						)
												return log_mel_spectrogram.T




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							np.array_split(list(range(0					,						total_frames - chunk_frames + 1							)							)					,						3							)
												if len(ranges[1]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	__lowerCAmelCase					=							[0]
												if len(ranges[2]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	__lowerCAmelCase					=							[0]
												# randomly choose index for each part
												__lowerCAmelCase					=							np.random.choice(ranges[0]							)
												__lowerCAmelCase					=							np.random.choice(ranges[1]							)
												__lowerCAmelCase					=							np.random.choice(ranges[2]							)

												__lowerCAmelCase					=							mel[idx_front : idx_front + chunk_frames, :]
												__lowerCAmelCase					=							mel[idx_middle : idx_middle + chunk_frames, :]
												__lowerCAmelCase					=							mel[idx_back : idx_back + chunk_frames, :]

												__lowerCAmelCase					=							torch.tensor(mel[None, None, :]							)
												__lowerCAmelCase					=							torch.nn.functional.interpolate(
												    _A					,						size=[chunk_frames, 6_4]					,						mode='bilinear'					,						align_corners=_A							)
												__lowerCAmelCase					=							mel_shrink[0][0].numpy()
												__lowerCAmelCase					=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,						axis=0							)
												return mel_fusion




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A							):
												if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																						__lowerCAmelCase					=							True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						__lowerCAmelCase					=							len(_A							) - max_length
																						__lowerCAmelCase					=							np.random.randint(0					,						overflow + 1							)
																						__lowerCAmelCase					=							waveform[idx : idx + max_length]
																						__lowerCAmelCase					=							self._np_extract_fbank_features(_A					,						self.mel_filters_slaney							)[None, :]
																	elif truncation == "fusion":
																						__lowerCAmelCase					=							self._np_extract_fbank_features(_A					,						self.mel_filters							)
																						__lowerCAmelCase					=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						__lowerCAmelCase					=							mel.shape[0]
																						if chunk_frames == total_frames:
																											# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																											# In this case, we just use the whole audio.
																											__lowerCAmelCase					=							np.stack([mel, mel, mel, mel]					,						axis=0							)
																											__lowerCAmelCase					=							False
																						else:
																											__lowerCAmelCase					=							self._random_mel_fusion(_A					,						_A					,						_A							)
																											__lowerCAmelCase					=							True
																	else:
																						raise NotImplementedError(F"""data_truncating {truncation} not implemented"""							)

												else:
																	__lowerCAmelCase					=							False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																						if padding == "repeat":
																											__lowerCAmelCase					=							int(max_length / len(_A							)							)
																											__lowerCAmelCase					=							np.stack(np.tile(_A					,						n_repeat + 1							)							)[:max_length]
																						if padding == "repeatpad":
																											__lowerCAmelCase					=							int(max_length / len(_A							)							)
																											__lowerCAmelCase					=							np.stack(np.tile(_A					,						_A							)							)
																						__lowerCAmelCase					=							np.pad(_A					,						(0, max_length - waveform.shape[0])					,						mode='constant'					,						constant_values=0							)

																	if truncation == "fusion":
																						__lowerCAmelCase					=							self._np_extract_fbank_features(_A					,						self.mel_filters							)
																						__lowerCAmelCase					=							np.stack([input_mel, input_mel, input_mel, input_mel]					,						axis=0							)
																	else:
																						__lowerCAmelCase					=							self._np_extract_fbank_features(_A					,						self.mel_filters_slaney							)[None, :]

												return input_mel, longer




							def __call__( self					,						_A					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						**_A					,						):
												__lowerCAmelCase					=							truncation if truncation is not None else self.truncation
												__lowerCAmelCase					=							padding if padding else self.padding

												if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																						    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																						    F""" was sampled with {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.'							)

												__lowerCAmelCase					=							isinstance(_A					,						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}"""							)
												__lowerCAmelCase					=							is_batched_numpy or (
												    isinstance(_A					,						(list, tuple)							) and (isinstance(raw_speech[0]					,						(np.ndarray, tuple, list)							))
												)

												if is_batched:
																	__lowerCAmelCase					=							[np.asarray(_A					,						dtype=np.floataa							) for speech in raw_speech]
												elif not is_batched and not isinstance(_A					,						np.ndarray							):
																	__lowerCAmelCase					=							np.asarray(_A					,						dtype=np.floataa							)
												elif isinstance(_A					,						np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																	__lowerCAmelCase					=							raw_speech.astype(np.floataa							)

												# always return batch
												if not is_batched:
																	__lowerCAmelCase					=							[np.asarray(_A							)]

												# convert to mel spectrogram, truncate and pad if needed.
												__lowerCAmelCase					=							[
												    self._get_input_mel(_A					,						max_length if max_length else self.nb_max_samples					,						_A					,						_A							)
												    for waveform in raw_speech
												]

												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							[]
												for mel, longer in padded_inputs:
																	input_mel.append(_A							)
																	is_longer.append(_A							)

												if truncation == "fusion" and sum(_A							) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	__lowerCAmelCase					=							np.random.randint(0					,						len(_A							)							)
																	__lowerCAmelCase					=							True

												if isinstance(input_mel[0]					,						_A							):
																	__lowerCAmelCase					=							[np.asarray(_A					,						dtype=np.floataa							) for feature in input_mel]

												# is_longer is a list of bool
												__lowerCAmelCase					=							[[longer] for longer in is_longer]

												__lowerCAmelCase					=							{'input_features': input_mel, 'is_longer': is_longer}
												__lowerCAmelCase					=							BatchFeature(_A							)

												if return_tensors is not None:
																	__lowerCAmelCase					=							input_features.convert_to_tensors(_A							)

												return input_features









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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

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

					# print & log results
					__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							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!
					__lowerCAmelCase					=							['\n\n', '\n', '   ', '  ']

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							AutoFeatureExtractor.from_pretrained(args.model_id      )
					__lowerCAmelCase					=							feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

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

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

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

					# run inference on all examples
					__lowerCAmelCase					=							dataset.map(UpperCAmelCase__		,					remove_columns=dataset.column_names      )

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


if __name__ == "__main__":
		lowerCamelCase      				=     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      				=     parser.parse_args()

		main(args)






