


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 (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				lowerCAmelCase_ :      Dict    				=				args.log_outputs
				lowerCAmelCase_ :      Optional[int]    				=				'''_'''.join(args.dataset.split('''/''' ) + [args.config, args.split] )

				# load metric
				lowerCAmelCase_ :      Optional[Any]    				=				load_metric('''wer''' )
				lowerCAmelCase_ :      Union[str, Any]    				=				load_metric('''cer''' )

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

				# print & log results
				lowerCAmelCase_ :      Optional[Any]    				=				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_ :      Optional[int]    				=				f"""log_{dataset_id}_predictions.txt"""
								lowerCAmelCase_ :      Optional[int]    				=				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 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				lowerCAmelCase_ :      Optional[Any]    				=				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_ :      List[str]    				=				['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								lowerCAmelCase_ :      Optional[int]    				=				''' '''.join(text.split(lowerCAmelCase_ ) )

				return text



def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[str]:
				lowerCAmelCase_ :      Dict    				=				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_ :      int    				=				AutoFeatureExtractor.from_pretrained(args.model_id )
				lowerCAmelCase_ :      Optional[Any]    				=				feature_extractor.sampling_rate

				# resample audio
				lowerCAmelCase_ :      Tuple    				=				dataset.cast_column('''audio''' ,   Audio(sampling_rate=lowerCAmelCase_ ) )

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

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

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

				# run inference on all examples
				lowerCAmelCase_ :      str    				=				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			:  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			:  Any     =parser.parse_args()

	main(args)



_UpperCAmelCase			:  Dict     =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
				lowerCAmelCase_ :      Optional[int]    				=				0
				lowerCAmelCase_ :      List[str]    				=				0
				while place < len(lowerCAmelCase_ ):
								if (place + 1 < len(lowerCAmelCase_ )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				[]
				for arabic, roman in ROMAN:
								((lowerCAmelCase_)				,  (lowerCAmelCase_)) :      Optional[int]    				=				divmod(lowerCAmelCase_ ,   lowerCAmelCase_ )
								result.append(roman * factor )
								if number == 0:
												break
				return "".join(lowerCAmelCase_ )


if __name__ == "__main__":
						import doctest

						doctest.testmod()