






"""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 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						List[str]	    =   args.log_outputs
	A_		:						str	    =   """_""".join(args.dataset.split("""/"""					) + [args.config, args.split]					)

	# load metric
	A_		:						int	    =   load_metric("""wer"""					)
	A_		:						List[Any]	    =   load_metric("""cer"""					)

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

	# print & log results
	A_		:						List[Any]	    =   F"""WER: {wer_result}\nCER: {cer_result}"""
	print(snake_case__					)

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		A_		:						Optional[Any]	    =   F"""log_{dataset_id}_predictions.txt"""
		A_		:						Union[str, Any]	    =   F"""log_{dataset_id}_targets.txt"""

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

			result.map(snake_case__   ,   with_indices=snake_case__					)






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

	A_		:						Union[str, Any]	    =   re.sub(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_		:						Any	    =   ["""\n\n""", """\n""", """   """, """  """]

	for t in token_sequences_to_ignore:
		A_		:						Dict	    =   """ """.join(text.split(snake_case__					)					)

	return text






def 			__UpperCamelCase	(		snake_case__					):
	# load dataset
	A_		:						str	    =   load_dataset(args.dataset   ,   args.config   ,   split=args.split   ,   use_auth_token=snake_case__					)

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

	# load processor
	A_		:						Dict	    =   AutoFeatureExtractor.from_pretrained(args.model_id					)
	A_		:						Tuple	    =   feature_extractor.sampling_rate

	# resample audio
	A_		:						List[Any]	    =   dataset.cast_column("""audio"""   ,   Audio(sampling_rate=snake_case__					)					)

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

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

		A_		:						Any	    =   prediction["""text"""]
		A_		:						int	    =   normalize_text(batch["""sentence"""]					)
		return batch

	# run inference on all examples
	A_		:						List[Any]	    =   dataset.map(snake_case__   ,   remove_columns=dataset.column_names					)

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


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)









"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

