


"""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        lowercase							(		lowerCAmelCase__ :     Dataset    ,					lowerCAmelCase__ :     Dict[str, str]			) ->      Dict:
		__a           =			args.log_outputs
		__a           =			'''_'''.join(args.dataset.split('''/'''			) + [args.config, args.split]			)

		# load metric
		__a           =			load_metric('''wer'''			)
		__a           =			load_metric('''cer'''			)

		# compute metrics
		__a           =			wer.compute(references=result['''target''']    ,					predictions=result['''prediction''']			)
		__a           =			cer.compute(references=result['''target''']    ,					predictions=result['''prediction''']			)

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

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

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

		return text

def        lowercase							(		lowerCAmelCase__ :     int			) ->      Any:
		# load dataset
		__a           =			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
		__a           =			AutoFeatureExtractor.from_pretrained(args.model_id			)
		__a           =			feature_extractor.sampling_rate

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

		# load eval pipeline
		if args.device is None:
				__a           =			0 if torch.cuda.is_available() else -1
		__a           =			pipeline('''automatic-speech-recognition'''    ,					model=args.model_id    ,					device=args.device			)

		# map function to decode audio
		def map_to_pred(lowerCAmelCase__ :     Any			):
				__a           =			asr(
				    batch['''audio''']['''array''']    ,					chunk_length_s=args.chunk_length_s    ,					stride_length_s=args.stride_length_s			)

				__a           =			prediction['''text''']
				__a           =			normalize_text(batch['''sentence''']			)
				return batch

		# run inference on all examples
		__a           =			dataset.map(lowerCAmelCase__    ,					remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
				lowercase_			     =  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.",
				)
				lowercase_			     =  parser.parse_args()

				main(args)










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			set()

		# Replace all the whitespace in our sentence
		__a           =			input_str.replace(''' '''    ,					''''''			)
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()			)
		return len(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			[False] * 26
		for char in input_str:
				if char.islower():
						__a           =			True
				elif char.isupper():
						__a           =			True
		return all(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
		print(timeit('''is_pangram()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_faster()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_fastest()'''    ,					setup=lowerCAmelCase__			)			)
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()






