

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

lowerCamelCase      				=     random.Random()


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=1.0		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None      ):

					"""simple docstring"""






					if rng is None:
										__lowerCAmelCase					=							global_rng

					__lowerCAmelCase					=							[]
					for batch_idx in range(shape[0]      ):
										values.append([]      )
										for _ in range(shape[1]      ):
															values[-1].append(rng.random() * scale      )

					return values





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=7					,						_A=4_0_0					,						_A=2_0_0_0					,						_A=2_0_4_8					,						_A=1_2_8					,						_A=1					,						_A=5_1_2					,						_A=3_0					,						_A=4_4_1_0_0					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							min_seq_length
												__lowerCAmelCase					=							max_seq_length
												__lowerCAmelCase					=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												__lowerCAmelCase					=							spectrogram_length
												__lowerCAmelCase					=							feature_size
												__lowerCAmelCase					=							num_audio_channels
												__lowerCAmelCase					=							hop_length
												__lowerCAmelCase					=							chunk_length
												__lowerCAmelCase					=							sampling_rate




							def        A__  ( self							):
												return {
												    "spectrogram_length": self.spectrogram_length,
												    "feature_size": self.feature_size,
												    "num_audio_channels": self.num_audio_channels,
												    "hop_length": self.hop_length,
												    "chunk_length": self.chunk_length,
												    "sampling_rate": self.sampling_rate,
												}




							def        A__  ( self					,						_A=False					,						_A=False							):





												def _flatten(_A							):
																	return list(itertools.chain(*UpperCamelCase__							)							)

												if equal_length:
																	__lowerCAmelCase					=							[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
												else:
																	# make sure that inputs increase in size
																	__lowerCAmelCase					=							[
																	    floats_list((x, self.feature_size)							)
																	    for x in range(self.min_seq_length					,						self.max_seq_length					,						self.seq_length_diff							)
																	]
												if numpify:
																	__lowerCAmelCase					=							[np.asarray(UpperCamelCase__							) for x in speech_inputs]
												return speech_inputs





@require_torch
@require_torchaudio
class     snake_case_			(				UpperCamelCase_			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =TvltFeatureExtractor




							def        A__  ( self							):
												__lowerCAmelCase					=							TvltFeatureExtractionTester(self							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.feature_extraction_class(**self.feat_extract_dict							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'spectrogram_length'							)							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'feature_size'							)							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'num_audio_channels'							)							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'hop_length'							)							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'chunk_length'							)							)
												self.assertTrue(hasattr(UpperCamelCase__					,						'sampling_rate'							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.feature_extraction_class(**self.feat_extract_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	__lowerCAmelCase					=							feat_extract_first.save_pretrained(UpperCamelCase__							)[0]
																	check_json_file_has_correct_format(UpperCamelCase__							)
																	__lowerCAmelCase					=							self.feature_extraction_class.from_pretrained(UpperCamelCase__							)

												__lowerCAmelCase					=							feat_extract_first.to_dict()
												__lowerCAmelCase					=							feat_extract_second.to_dict()
												__lowerCAmelCase					=							dict_first.pop('mel_filters'							)
												__lowerCAmelCase					=							dict_second.pop('mel_filters'							)
												self.assertTrue(np.allclose(UpperCamelCase__					,						UpperCamelCase__							)							)
												self.assertEqual(UpperCamelCase__					,						UpperCamelCase__							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.feature_extraction_class(**self.feat_extract_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	__lowerCAmelCase					=							os.path.join(UpperCamelCase__					,						'feat_extract.json'							)
																	feat_extract_first.to_json_file(UpperCamelCase__							)
																	__lowerCAmelCase					=							self.feature_extraction_class.from_json_file(UpperCamelCase__							)

												__lowerCAmelCase					=							feat_extract_first.to_dict()
												__lowerCAmelCase					=							feat_extract_second.to_dict()
												__lowerCAmelCase					=							dict_first.pop('mel_filters'							)
												__lowerCAmelCase					=							dict_second.pop('mel_filters'							)
												self.assertTrue(np.allclose(UpperCamelCase__					,						UpperCamelCase__							)							)
												self.assertEqual(UpperCamelCase__					,						UpperCamelCase__							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.feature_extraction_class(**self.feat_extract_dict							)

												# create three inputs of length 800, 1000, and 1200
												__lowerCAmelCase					=							[floats_list((1, x)							)[0] for x in range(8_0_0					,						1_4_0_0					,						2_0_0							)]
												__lowerCAmelCase					=							[np.asarray(UpperCamelCase__							) for speech_input in speech_inputs]

												# Test not batched input
												__lowerCAmelCase					=							feature_extractor(np_speech_inputs[0]					,						return_tensors='np'					,						sampling_rate=4_4_1_0_0							).audio_values

												self.assertTrue(encoded_audios.ndim == 4							)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

												# Test batched
												__lowerCAmelCase					=							feature_extractor(UpperCamelCase__					,						return_tensors='np'					,						sampling_rate=4_4_1_0_0							).audio_values

												self.assertTrue(encoded_audios.ndim == 4							)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

												# Test audio masking
												__lowerCAmelCase					=							feature_extractor(
												    UpperCamelCase__					,						return_tensors='np'					,						sampling_rate=4_4_1_0_0					,						mask_audio=UpperCamelCase__							).audio_values

												self.assertTrue(encoded_audios.ndim == 4							)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

												# Test 2-D numpy arrays are batched.
												__lowerCAmelCase					=							[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
												__lowerCAmelCase					=							np.asarray(UpperCamelCase__							)
												__lowerCAmelCase					=							feature_extractor(UpperCamelCase__					,						return_tensors='np'					,						sampling_rate=4_4_1_0_0							).audio_values
												self.assertTrue(encoded_audios.ndim == 4							)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							load_dataset('hf-internal-testing/librispeech_asr_dummy'					,						'clean'					,						split='validation'							)
												# automatic decoding with librispeech
												__lowerCAmelCase					=							ds.sort('id'							).select(range(UpperCamelCase__							)							)[:num_samples]['''audio''']

												return [x["array"] for x in speech_samples]




							def        A__  ( self							):
												__lowerCAmelCase					=							self._load_datasamples(1							)
												__lowerCAmelCase					=							TvltFeatureExtractor()
												__lowerCAmelCase					=							feature_extractor(UpperCamelCase__					,						return_tensors='pt'							).audio_values

												self.assertEquals(audio_values.shape					,						(1, 1, 1_9_2, 1_2_8)							)

												__lowerCAmelCase					=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,						UpperCamelCase__					,						atol=1e-4							)							)









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)






