

'''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 						__lowerCamelCase	(       __snake_case					:		Dataset,		__snake_case					:		Dict[str, str]	)	->							int:






						"""simple docstring"""





						A__					:       Any     =args.log_outputs
						A__					:       Tuple     ="_".join(args.dataset.split("""/"""	) + [args.config, args.split]	)

						# load metric
						A__					:       str     =load_metric("""wer"""	)
						A__					:       Dict     =load_metric("""cer"""	)

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

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

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

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

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

																		result.map(_SCREAMING_SNAKE_CASE,		with_indices=_SCREAMING_SNAKE_CASE	)
def 						__lowerCamelCase	(       __snake_case					:		str	)	->							str:






						"""simple docstring"""





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

						A__					:       Union[str, Any]     =re.sub(_SCREAMING_SNAKE_CASE,		"""""",		text.lower()	)

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

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

						return text
def 						__lowerCamelCase	(       __snake_case					:		Tuple	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       Optional[int]     =load_dataset(args.dataset,		args.config,		split=args.split,		use_auth_token=_SCREAMING_SNAKE_CASE	)

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

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

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

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

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

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

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

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


if __name__ == "__main__":
		__snake_case			:     int										=      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.',
		)
		__snake_case			:     int										=      parser.parse_args()

		main(args)




'''simple docstring'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

