




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 :    str      						=      logging.get_logger(__name__)







def 		lowercase__ (		__A:						bytes				,__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Optional[Any]     				=				F'''{sampling_rate}'''
					__magic_name__				:						int     				=				'''1'''
					__magic_name__				:						List[str]     				=				'''f32le'''
					__magic_name__				:						Optional[Any]     				=				[
					    '''ffmpeg''',
					    '''-i''',
					    '''pipe:0''',
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]

					try:
										__magic_name__				:						int     				=				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'''					)
					__magic_name__				:						Optional[Any]     				=				ffmpeg_process.communicate(__A					)
					__magic_name__				:						int     				=				output_stream[0]

					__magic_name__				:						List[Any]     				=				np.frombuffer(__A				,np.floataa					)
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''					)
					return audio


@add_end_docstrings(_lowerCamelCase    )
class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			def __init__(			self      : Union[str, Any]							,    *lowerCamelCase_      : Tuple							,    **lowerCamelCase_      : Union[str, Any]	)    -> int:
								# Default, might be overriden by the model.config.
								__magic_name__				:						Any     				=				5
								super().__init__(*lowerCamelCase_							,    **lowerCamelCase_	)

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

								self.check_model_type(lowerCamelCase_	)

			def __call__(			self      : str							,    lowerCamelCase_      : Union[np.ndarray, bytes, str]							,    **lowerCamelCase_      : Optional[Any]							,    )    -> str:
								return super().__call__(lowerCamelCase_							,    **lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Optional[int]=None							,    **lowerCamelCase_      : Dict	)    -> List[Any]:
								# No parameters on this pipeline right now
								__magic_name__				:						int     				=				{}
								if top_k is not None:
													if top_k > self.model.config.num_labels:
																		__magic_name__				:						Dict     				=				self.model.config.num_labels
													__magic_name__				:						Tuple     				=				top_k
								return {}, {}, postprocess_params

			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : Optional[Any]	)    -> Optional[Any]:
								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													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
																		__magic_name__				:						Union[str, Any]     				=				requests.get(lowerCamelCase_	).content
													else:
																		with open(lowerCamelCase_							,    '''rb'''	) as f:
																							__magic_name__				:						Optional[Any]     				=				f.read()

								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													__magic_name__				:						Dict     				=				ffmpeg_read(lowerCamelCase_							,    self.feature_extractor.sampling_rate	)

								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													# 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'''	)

													__magic_name__				:						List[str]     				=				inputs.pop('''raw'''							,    lowerCamelCase_	)
													if _inputs is None:
																		# Remove path which will not be used from `datasets`.
																		inputs.pop('''path'''							,    lowerCamelCase_	)
																		__magic_name__				:						Dict     				=				inputs.pop('''array'''							,    lowerCamelCase_	)
													__magic_name__				:						Any     				=				inputs.pop('''sampling_rate'''	)
													__magic_name__				:						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`.'''	)

																		__magic_name__				:						Optional[Any]     				=				F.resample(
																		    torch.from_numpy(lowerCamelCase_	)							,    lowerCamelCase_							,    self.feature_extractor.sampling_rate	).numpy()

								if not isinstance(lowerCamelCase_							,    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'''	)

								__magic_name__				:						Optional[Any]     				=				self.feature_extractor(
								    lowerCamelCase_							,    sampling_rate=self.feature_extractor.sampling_rate							,    return_tensors='''pt'''	)
								return processed

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : List[str]	)    -> List[str]:
								__magic_name__				:						Tuple     				=				self.model(**lowerCamelCase_	)
								return model_outputs


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : int							,    lowerCamelCase_      : str=5	)    -> Any:
								__magic_name__				:						Union[str, Any]     				=				model_outputs.logits[0].softmax(-1	)
								__magic_name__				,     __magic_name__				:						Dict     				=				probs.topk(lowerCamelCase_	)

								__magic_name__				:						Union[str, Any]     				=				scores.tolist()
								__magic_name__				:						str     				=				ids.tolist()

								__magic_name__				:						Union[str, Any]     				=				[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase_							,    lowerCamelCase_	)]

								return labels









import math
from collections.abc import Generator







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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)
										__magic_name__				:						Tuple     				=				int(math.sqrt(__A					)					) + 1
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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):
										__magic_name__				:						str     				=				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 		lowercase__ (		):





					'''simple docstring'''






					from timeit import timeit

					__magic_name__				:						Dict     				=				'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''fast_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)


if __name__ == "__main__":
	__lowerCamelCase :    Union[str, Any]      						=      int(input('''Calculate primes up to:\n>> ''').strip())
	for ret in primes(number):
		print(ret)
	benchmark()



