





"""simple docstring"""
import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

lowerCamelCase_			    =	logging.get_logger(__name__)



def   __lowerCamelCase     (							a_  :	Dict				,     a_  :	Optional[Any]							)    ->   np.array:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						f'''{sampling_rate}'''
      __SCREAMING_SNAKE_CASE     :int	       =						'''1'''
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''f32le'''
      __SCREAMING_SNAKE_CASE     :Dict	       =						[
          '''ffmpeg''',
          '''-i''',
          '''pipe:0''',
          '''-ac''',
          ac,
          '''-ar''',
          ar,
          '''-f''',
          format_for_conversion,
          '''-hide_banner''',
          '''-loglevel''',
          '''quiet''',
          '''pipe:1''',
      ]

      try:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						subprocess.Popen(a_				,     stdin=subprocess.PIPE				,     stdout=subprocess.PIPE							)
      except FileNotFoundError:
            raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						ffmpeg_process.communicate(a_							)
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						output_stream[0]

      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						np.frombuffer(a_				,     np.floataa							)
      if audio.shape[0] == 0:
            raise ValueError('''Malformed soundfile'''							)
      return audio







@add_end_docstrings(a_						)
class       _SCREAMING_SNAKE_CASE(     a_						):




       def __init__(		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						5
             super().__init__(*_snake_case	,**_snake_case							)

             if self.framework != "pt":
                   raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''							)

             self.check_model_type(_snake_case							)




       def __call__(		self	,SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)    ->   int:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__=None	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						{}
             if top_k is not None:
                   if top_k > self.model.config.num_labels:
                         __SCREAMING_SNAKE_CASE     :int	       =						self.model.config.num_labels
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						top_k
             return {}, {}, postprocess_params




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             if isinstance(_snake_case	,_snake_case							):
                   if inputs.startswith('''http://'''							) or inputs.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
                         __SCREAMING_SNAKE_CASE     :Dict	       =						requests.get(_snake_case							).content
                   else:
                         with open(_snake_case	,'''rb'''							) as f:
                               __SCREAMING_SNAKE_CASE     :int	       =						f.read()

             if isinstance(_snake_case	,_snake_case							):
                   __SCREAMING_SNAKE_CASE     :str	       =						ffmpeg_read(_snake_case	,self.feature_extractor.sampling_rate							)

             if isinstance(_snake_case	,_snake_case							):
                   # Accepting `"array"` which is the key defined in `datasets` for
                   # better integration
                   if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                         raise ValueError(
                             '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                             '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                             '''containing the sampling_rate associated with that array'''							)

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						inputs.pop('''raw'''	,_snake_case							)
                   if _inputs is None:
                         # Remove path which will not be used from `datasets`.
                         inputs.pop('''path'''	,_snake_case							)
                         __SCREAMING_SNAKE_CASE     :str	       =						inputs.pop('''array'''	,_snake_case							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						inputs.pop('''sampling_rate'''							)
                   __SCREAMING_SNAKE_CASE     :Any	       =						_inputs
                   if in_sampling_rate != self.feature_extractor.sampling_rate:
                         import torch

                         if is_torchaudio_available():
                               from torchaudio import functional as F
                         else:
                               raise ImportError(
                                   '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                                   '''The torchaudio package can be installed through: `pip install torchaudio`.'''							)

                         __SCREAMING_SNAKE_CASE     :Tuple	       =						F.resample(
                             torch.from_numpy(_snake_case							)	,_snake_case	,self.feature_extractor.sampling_rate							).numpy()

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

             __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.feature_extractor(
                 _snake_case	,sampling_rate=self.feature_extractor.sampling_rate	,return_tensors='''pt'''							)
             return processed




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.model(**_snake_case							)
             return model_outputs




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=5							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						model_outputs.logits[0].softmax(-1							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						probs.topk(_snake_case							)

             __SCREAMING_SNAKE_CASE     :Dict	       =						scores.tolist()
             __SCREAMING_SNAKE_CASE     :Dict	       =						ids.tolist()

             __SCREAMING_SNAKE_CASE     :Any	       =						[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(_snake_case	,_snake_case							)]

             return labels






"""simple docstring"""
import math
from collections.abc import Generator



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            # only need to check for factors up to sqrt(i)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						int(math.sqrt(a_							)							) + 1
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)				,     2							))
      # It's useless to test even numbers as they will not be prime
      if max_n > 2:
            yield 2  # Because 2 will not be tested, it's necessary to yield it now
      for i in (n for n in numbers if n > 1):
            __SCREAMING_SNAKE_CASE     :int	       =						int(math.sqrt(a_							)							) + 1
            for j in range(3				,     a_				,     2							):
                  # As we removed the even numbers, we don't need them now
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      from timeit import timeit

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''from __main__ import slow_primes, primes, fast_primes'''
      print(timeit('''slow_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''fast_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)


if __name__ == "__main__":
  lowerCamelCase_          =	int(input("Calculate primes up to:\n>> ").strip())
  for ret in primes(number):
    print(ret)
  benchmark()