

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 		UpperCamelCase      (lowercase_:				Dataset					,   lowercase_:				Dict[str, str]      )  ->   List[str]:
	A__							:					int       =	args.log_outputs
	A__							:					str       =	"""_""".join(args.dataset.split("""/"""      ) + [args.config, args.split]      )

	# load metric
	A__							:					str       =	load_metric("""wer"""      )
	A__							:					Optional[int]       =	load_metric("""cer"""      )

	# compute metrics
	A__							:					Dict       =	wer.compute(references=result["""target"""]					,   predictions=result["""prediction"""]      )
	A__							:					Union[str, Any]       =	cer.compute(references=result["""target"""]					,   predictions=result["""prediction"""]      )

	# print & log results
	A__							:					Union[str, Any]       =	f"""WER: {wer_result}\nCER: {cer_result}"""
	print(lowercase_      )

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		A__							:					int       =	f"""log_{dataset_id}_predictions.txt"""
		A__							:					List[str]       =	f"""log_{dataset_id}_targets.txt"""

		with open(lowercase_					,   """w"""      ) as p, open(lowercase_					,   """w"""      ) as t:
			# mapping function to write output
			def write_to_file(lowercase_:				List[Any]					,   lowercase_:				List[Any]      ):
				p.write(f"""{i}""" + """\n"""      )
				p.write(batch["""prediction"""] + """\n"""      )
				t.write(f"""{i}""" + """\n"""      )
				t.write(batch["""target"""] + """\n"""      )

			result.map(lowercase_					,   with_indices=lowercase_      )

def 		UpperCamelCase      (lowercase_:				str      )  ->   str:
	A__							:					Optional[int]       =	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	A__							:					int       =	re.sub(lowercase_					,   """"""					,   text.lower()      )

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

	for t in token_sequences_to_ignore:
		A__							:					Optional[Any]       =	""" """.join(text.split(lowercase_      )      )

	return text

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   Any:
	# load dataset
	A__							:					Any       =	load_dataset(args.dataset					,   args.config					,   split=args.split					,   use_auth_token=lowercase_      )

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

	# load processor
	A__							:					Tuple       =	AutoFeatureExtractor.from_pretrained(args.model_id      )
	A__							:					List[Any]       =	feature_extractor.sampling_rate

	# resample audio
	A__							:					List[str]       =	dataset.cast_column("""audio"""					,   Audio(sampling_rate=lowercase_      )      )

	# load eval pipeline
	if args.device is None:
		A__							:					List[Any]       =	0 if torch.cuda.is_available() else -1
	A__							:					Optional[Any]       =	pipeline("""automatic-speech-recognition"""					,   model=args.model_id					,   device=args.device      )

	# map function to decode audio
	def map_to_pred(lowercase_:				str      ):
		A__							:					Optional[Any]       =	asr(
		    batch["""audio"""]["""array"""]					,   chunk_length_s=args.chunk_length_s					,   stride_length_s=args.stride_length_s      )

		A__							:					str       =	prediction["""text"""]
		A__							:					Dict       =	normalize_text(batch["""sentence"""]      )
		return batch

	# run inference on all examples
	A__							:					int       =	dataset.map(lowercase_					,   remove_columns=dataset.column_names      )

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


if __name__ == "__main__":
		A_ :				List[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.',
		)
		A_ :				List[Any]						 =    parser.parse_args()

		main(args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A_ :				Optional[Any]						 =    {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				str						 =    ['BartphoTokenizer']

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	A_ :				Optional[int]						 =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



