





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class      lowerCAmelCase							(      __lowerCAmelCase    ):
 UpperCAmelCase__									=	"Wav2Vec2FeatureExtractor"
 UpperCAmelCase__									=	"AutoTokenizer"
 def __init__(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple			)    ->			int:
     super().__init__(lowerCamelCase__    ,  lowerCamelCase__			)
     lowerCamelCase__					:   Optional[Any]      				=	self.feature_extractor
     lowerCamelCase__					:   Optional[int]      				=	False
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Union[str, Any]:






     try:
         return super().from_pretrained(lowerCamelCase__    ,  **lowerCamelCase__			)
     except OSError:
         warnings.warn(
             F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
             ' include a `tokenizer_class` attribute is deprecated and will be '
             'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
             ' attribute to either your `config.json` or `tokenizer_config.json` '
             'file to suppress this warning: '    ,  lowerCamelCase__    ,  )

         lowerCamelCase__					:   Optional[Any]      				=	WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__    ,  **lowerCamelCase__			)
         lowerCamelCase__					:   List[Any]      				=	WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase__    ,  **lowerCamelCase__			)

         return cls(feature_extractor=lowerCamelCase__    ,  tokenizer=lowerCamelCase__			)
 def __call__(   self						:   Any    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:






     if self._in_target_context_manager:
         return self.current_processor(*lowerCamelCase__    ,  **lowerCamelCase__			)

     if "raw_speech" in kwargs:
         warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
         lowerCamelCase__					:   List[str]      				=	kwargs.pop('raw_speech'			)
     else:
         lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('audio'    ,  lowerCamelCase__			)
     lowerCamelCase__					:   Any      				=	kwargs.pop('sampling_rate'    ,  lowerCamelCase__			)
     lowerCamelCase__					:   Union[str, Any]      				=	kwargs.pop('text'    ,  lowerCamelCase__			)
     if len(lowerCamelCase__			) > 0:
         lowerCamelCase__					:   Union[str, Any]      				=	args[0]
         lowerCamelCase__					:   List[Any]      				=	args[1:]

     if audio is None and text is None:
         raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

     if audio is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	self.feature_extractor(lowerCamelCase__    ,  *lowerCamelCase__    ,  sampling_rate=lowerCamelCase__    ,  **lowerCamelCase__			)
     if text is not None:
         lowerCamelCase__					:   Tuple      				=	self.tokenizer(lowerCamelCase__    ,  **lowerCamelCase__			)

     if text is None:
         return inputs
     elif audio is None:
         return encodings
     else:
         lowerCamelCase__					:   Any      				=	encodings['''input_ids''']
         return inputs
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:






     if self._in_target_context_manager:
         return self.current_processor.pad(*lowerCamelCase__    ,  **lowerCamelCase__			)

     lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('input_features'    ,  lowerCamelCase__			)
     lowerCamelCase__					:   List[Any]      				=	kwargs.pop('labels'    ,  lowerCamelCase__			)
     if len(lowerCamelCase__			) > 0:
         lowerCamelCase__					:   str      				=	args[0]
         lowerCamelCase__					:   List[str]      				=	args[1:]

     if input_features is not None:
         lowerCamelCase__					:   Any      				=	self.feature_extractor.pad(lowerCamelCase__    ,  *lowerCamelCase__    ,  **lowerCamelCase__			)
     if labels is not None:
         lowerCamelCase__					:   Tuple      				=	self.tokenizer.pad(lowerCamelCase__    ,  **lowerCamelCase__			)

     if labels is None:
         return input_features
     elif input_features is None:
         return labels
     else:
         lowerCamelCase__					:   int      				=	labels['''input_ids''']
         return input_features
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   int			)    ->			List[str]:
     return self.tokenizer.batch_decode(*lowerCamelCase__    ,  **lowerCamelCase__			)
 def   A_			(   self						:   List[Any]    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			int:
     return self.tokenizer.decode(*lowerCamelCase__    ,  **lowerCamelCase__			)







 @contextmanager
 def   A_			(   self						:   Optional[int]			)    ->			Optional[Any]:
     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your audio inputs, or in a separate call.'			)
     lowerCamelCase__					:   int      				=	True
     lowerCamelCase__					:   str      				=	self.tokenizer
     yield
     lowerCamelCase__					:   str      				=	self.feature_extractor
     lowerCamelCase__					:   Union[str, Any]      				=	False












from __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


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





