


'''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 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__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(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:
												__lowerCAmelCase											= F"""log_{dataset_id}_predictions.txt"""
												__lowerCAmelCase											= 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, lowerCamelCase):
																								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 							__magic_name__(  lowerCamelCase):
						__lowerCAmelCase											= '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

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

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

						return text

def 							__magic_name__(  lowerCamelCase):
						# load dataset
						__lowerCAmelCase											= 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
						__lowerCAmelCase											= AutoFeatureExtractor.from_pretrained(args.model_id)
						__lowerCAmelCase											= feature_extractor.sampling_rate

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

						# 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(lowerCamelCase):
												__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(lowerCAmelCase__, remove_columns=dataset.column_names)

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


if __name__ == "__main__":
	_UpperCAmelCase :       Union[str, 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.""",
	)
	_UpperCAmelCase :       Union[str, Any]										= parser.parse_args()

	main(args)





"""simple docstring"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






