






from __future__ import annotations

import math

def      _lowerCamelCase	(						__A    :							int )      ->					bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,		int(math.sqrt(__A ) + 1 )		,		6 ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


SCREAMING_SNAKE_CASE 							=						[num for num in range(3, 100001, 2) if not is_prime(num)]

def      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    if not isinstance(__A		,		__A ):
        raise ValueError('''n must be an integer''' )
    if n <= 0:
        raise ValueError('''n must be >= 0''' )

    _UpperCAmelCase				:      Tuple			  =				[]
    for num in range(len(__A ) ):
        _UpperCAmelCase				:      List[str]			  =				0
        while 2 * i * i <= odd_composites[num]:
            _UpperCAmelCase				:      Dict			  =				odd_composites[num] - 2 * i * i
            if is_prime(__A ):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num] )
            if len(__A ) == n:
                return list_nums

    return []

def      _lowerCamelCase	(						)      ->					int:
    return compute_nums(1 )[0]


if __name__ == "__main__":
  print(F'{solution() = }')












from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)
@add_end_docstrings(__lowercase			)
class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  **_A) ->		int:

     """simple docstring"""



     super().__init__(**_A)

     if self.framework != "pt":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
     # No specific FOR_XXX available yet


 def __call__(	self							,  _A							,  **_A) ->		List[str]:

     """simple docstring"""



     return super().__call__(_A							,  **_A)


 def  snake_case__				(	self							,  **_A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				{}
     if "candidate_labels" in kwargs:
         _UpperCAmelCase				:      Union[str, Any]			  =				kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         _UpperCAmelCase				:      Dict			  =				kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}


 def  snake_case__				(	self							,  _A							,  _A=None							,  _A="This is a sound of {}.") ->		Tuple:

     """simple docstring"""



     if isinstance(_A							,  _A):
         if audio.startswith('''http://''') or audio.startswith('''https://'''):
             # We need to actually check for a real protocol, otherwise it's impossible to use a local file
             # like http_huggingface_co.png
             _UpperCAmelCase				:      List[Any]			  =				requests.get(_A).content
         else:
             with open(_A							,  '''rb''') as f:
                 _UpperCAmelCase				:      List[str]			  =				f.read()

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				ffmpeg_read(_A							,  self.feature_extractor.sampling_rate)

     if not isinstance(_A							,  np.ndarray):
         raise ValueError('''We expect a numpy ndarray as input''')
     if len(audio.shape) != 1:
         raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''')

     _UpperCAmelCase				:      Optional[Any]			  =				self.feature_extractor(
         [audio]							,  sampling_rate=self.feature_extractor.sampling_rate							,  return_tensors='''pt''')
     _UpperCAmelCase				:      Dict			  =				candidate_labels
     _UpperCAmelCase				:      List[str]			  =				[hypothesis_template.format(_A) for x in candidate_labels]
     _UpperCAmelCase				:      Optional[Any]			  =				self.tokenizer(_A							,  return_tensors=self.framework							,  padding=_A)
     _UpperCAmelCase				:      str			  =				[text_inputs]
     return inputs


 def  snake_case__				(	self							,  _A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				model_inputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      Union[str, Any]			  =				model_inputs.pop('''text_inputs''')
     if isinstance(text_inputs[0]							,  _A):
         _UpperCAmelCase				:      int			  =				text_inputs[0]
     else:
         # Batching case.
         _UpperCAmelCase				:      Dict			  =				text_inputs[0][0]

     _UpperCAmelCase				:      str			  =				self.model(**_A							,  **_A)

     _UpperCAmelCase				:      str			  =				{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs


 def  snake_case__				(	self							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				model_outputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      str			  =				model_outputs['''logits'''][0]

     if self.framework == "pt":
         _UpperCAmelCase				:      int			  =				logits.softmax(dim=0)
         _UpperCAmelCase				:      Optional[Any]			  =				probs.tolist()
     else:
         raise ValueError('''`tf` framework not supported.''')

     _UpperCAmelCase				:      List[str]			  =				[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(_A							,  _A)							,  key=lambda _A: -x[0])
     ]
     return result




