



'''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						:		Tuple						      = logging.get_logger(__name__)



def 	__lowerCAmelCase       (				lowerCamelCase :    bytes					,		lowerCamelCase :    int       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	f'''{sampling_rate}'''
	__lowerCAmelCase       				=	"1"
	__lowerCAmelCase       				=	"f32le"
	__lowerCAmelCase       				=	[
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

	try:
		__lowerCAmelCase       				=	subprocess.Popen(lowerCAmelCase__					,		stdin=subprocess.PIPE					,		stdout=subprocess.PIPE       )
	except FileNotFoundError:
		raise ValueError("ffmpeg was not found but is required to load audio files from filename"       )
	__lowerCAmelCase       				=	ffmpeg_process.communicate(lowerCAmelCase__       )
	__lowerCAmelCase       				=	output_stream[0]

	__lowerCAmelCase       				=	np.frombuffer(lowerCAmelCase__					,		np.floataa       )
	if audio.shape[0] == 0:
		raise ValueError("Malformed soundfile"       )
	return audio






@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class        UpperCAmelCase__						(		_SCREAMING_SNAKE_CASE			):



					def __init__(     self      ,					*UpperCamelCase      ,					**UpperCamelCase  )  ->      Dict:
						# Default, might be overriden by the model.config.
						__lowerCAmelCase       				=	5
						super().__init__(*UpperCamelCase      ,					**UpperCamelCase  )

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

						self.check_model_type(UpperCamelCase  )



					def __call__(     self      ,					UpperCamelCase      ,					**UpperCamelCase      ,					)  ->      Any:
						return super().__call__(UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase=None      ,					**UpperCamelCase  )  ->      List[str]:
						# No parameters on this pipeline right now
						__lowerCAmelCase       				=	{}
						if top_k is not None:
							if top_k > self.model.config.num_labels:
								__lowerCAmelCase       				=	self.model.config.num_labels
							__lowerCAmelCase       				=	top_k
						return {}, {}, postprocess_params



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Optional[Any]:
						if isinstance(UpperCamelCase      ,					UpperCamelCase  ):
							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
								__lowerCAmelCase       				=	requests.get(UpperCamelCase  ).content
							else:
								with open(UpperCamelCase      ,					"rb"  ) as f:
									__lowerCAmelCase       				=	f.read()

						if isinstance(UpperCamelCase      ,					UpperCamelCase  ):
							__lowerCAmelCase       				=	ffmpeg_read(UpperCamelCase      ,					self.feature_extractor.sampling_rate  )

						if isinstance(UpperCamelCase      ,					UpperCamelCase  ):
							# 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"  )

							__lowerCAmelCase       				=	inputs.pop("raw"      ,					UpperCamelCase  )
							if _inputs is None:
								# Remove path which will not be used from `datasets`.
								inputs.pop("path"      ,					UpperCamelCase  )
								__lowerCAmelCase       				=	inputs.pop("array"      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	inputs.pop("sampling_rate"  )
							__lowerCAmelCase       				=	_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`."  )

								__lowerCAmelCase       				=	F.resample(
								    torch.from_numpy(UpperCamelCase  )      ,					UpperCamelCase      ,					self.feature_extractor.sampling_rate  ).numpy()

						if not isinstance(UpperCamelCase      ,					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"  )

						__lowerCAmelCase       				=	self.feature_extractor(
						    UpperCamelCase      ,					sampling_rate=self.feature_extractor.sampling_rate      ,					return_tensors="pt"  )
						return processed



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      str:
						__lowerCAmelCase       				=	self.model(**UpperCamelCase  )
						return model_outputs



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase=5  )  ->      Optional[int]:
						__lowerCAmelCase       				=	model_outputs.logits[0].softmax(-1  )
						__lowerCAmelCase				,							__lowerCAmelCase       				=	probs.topk(UpperCamelCase  )

						__lowerCAmelCase       				=	scores.tolist()
						__lowerCAmelCase       				=	ids.tolist()

						__lowerCAmelCase       				=	[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase      ,					UpperCamelCase  )]

						return labels



'''simple docstring'''






import math
from decimal import Decimal, getcontext






def 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'		)
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


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

