"""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]					)       ->					str:



							'''simple docstring'''

							__lowerCamelCase    :						str						      =   args.log_outputs
							__lowerCamelCase    :						Optional[Any]						      =   """_""".join(args.dataset.split("/"					) + [args.config, args.split]					)

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

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

							# print & log results
							__lowerCamelCase    :						Tuple						      =   F"""WER: {wer_result}\nCER: {cer_result}"""
							print(_UpperCamelCase					)

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

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

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

																					result.map(_UpperCamelCase  ,      with_indices=_UpperCamelCase					)


def       lowercase_      (      _lowerCamelCase:		str					)       ->					str:



							'''simple docstring'''

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

							__lowerCamelCase    :						List[str]						      =   re.sub(_UpperCamelCase  ,      ""  ,      text.lower()					)

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

							for t in token_sequences_to_ignore:
														__lowerCamelCase    :						Union[str, Any]						      =   """ """.join(text.split(_UpperCamelCase					)					)

							return text


def       lowercase_      (      _lowerCamelCase:		int					)       ->					int:



							'''simple docstring'''

							__lowerCamelCase    :						List[str]						      =   load_dataset(args.dataset  ,      args.config  ,      split=args.split  ,      use_auth_token=_UpperCamelCase					)

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

							# load processor
							__lowerCamelCase    :						Tuple						      =   AutoFeatureExtractor.from_pretrained(args.model_id					)
							__lowerCamelCase    :						str						      =   feature_extractor.sampling_rate

							# resample audio
							__lowerCamelCase    :						int						      =   dataset.cast_column("audio"  ,      Audio(sampling_rate=_UpperCamelCase					)					)

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

							# map function to decode audio
							def map_to_pred(_lowerCamelCase:		Union[str, Any]					):
														__lowerCamelCase    :						int						      =   asr(
														    batch["audio"]["array"]  ,      chunk_length_s=args.chunk_length_s  ,      stride_length_s=args.stride_length_s					)

														__lowerCamelCase    :						List[Any]						      =   prediction["""text"""]
														__lowerCamelCase    :						Optional[int]						      =   normalize_text(batch["sentence"]					)
														return batch

							# run inference on all examples
							__lowerCamelCase    :						Union[str, Any]						      =   dataset.map(_UpperCamelCase  ,      remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
				__A     = 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     = parser.parse_args()

				main(args)



"""simple docstring"""






import math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

							while start <= end:
														if temp[start] is True:
																					in_prime.append(_UpperCamelCase							)
																					for i in range(start * start       ,			end + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Dict      							=					False
														start += 1
							prime += in_prime

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

														for j in range(len(_UpperCamelCase							)							):
																					if temp[j] is True:
																												prime.append(j + low							)

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
