






'''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_					:			Union[str, Any]     						=				logging.get_logger(__name__)







def 						__magic_name__(			_A    ,   _A       ):
						'''simple docstring'''





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

						try:
												UpperCamelCase__       =			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"""       )
						UpperCamelCase__       =			ffmpeg_process.communicate(_A       )
						UpperCamelCase__       =			output_stream[0]

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







@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			def __init__( self				:    str			,   *lowercase				:    Optional[Any]			,   **lowercase				:    int      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			5
									super().__init__(*lowercase			,   **lowercase      )

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

									self.check_model_type(lowercase      )






			def __call__( self				:    Tuple			,   lowercase				:    Union[np.ndarray, bytes, str]			,   **lowercase				:    Optional[int]			,   )				->     int:

									'''simple docstring'''







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






			def 					A   ( self				:    Optional[int]			,   lowercase				:    List[Any]=None			,   **lowercase				:    Tuple      )				->     Any:

									'''simple docstring'''







									UpperCamelCase__       =			{}
									if top_k is not None:
															if top_k > self.model.config.num_labels:
																					UpperCamelCase__       =			self.model.config.num_labels
															UpperCamelCase__       =			top_k
									return {}, {}, postprocess_params






			def 					A   ( self				:    Union[str, Any]			,   lowercase				:    Tuple      )				->     Any:

									'''simple docstring'''







									if isinstance(lowercase			,   lowercase      ):
															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
																					UpperCamelCase__       =			requests.get(lowercase      ).content
															else:
																					with open(lowercase			,   """rb"""      ) as f:
																											UpperCamelCase__       =			f.read()

									if isinstance(lowercase			,   lowercase      ):
															UpperCamelCase__       =			ffmpeg_read(lowercase			,   self.feature_extractor.sampling_rate      )

									if isinstance(lowercase			,   lowercase      ):
															# 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"""      )

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

																					UpperCamelCase__       =			F.resample(
																					    torch.from_numpy(lowercase      )			,   lowercase			,   self.feature_extractor.sampling_rate      ).numpy()

									if not isinstance(lowercase			,   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"""      )

									UpperCamelCase__       =			self.feature_extractor(
									    lowercase			,   sampling_rate=self.feature_extractor.sampling_rate			,   return_tensors="""pt"""      )
									return processed






			def 					A   ( self				:    Optional[Any]			,   lowercase				:    Union[str, Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.model(**lowercase      )
									return model_outputs







			def 					A   ( self				:    List[Any]			,   lowercase				:    str			,   lowercase				:    List[str]=5      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			model_outputs.logits[0].softmax(-1      )
									UpperCamelCase__     , UpperCamelCase__       =			probs.topk(lowercase      )

									UpperCamelCase__       =			scores.tolist()
									UpperCamelCase__       =			ids.tolist()

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

									return labels








'''simple docstring'''


from itertools import combinations_with_replacement







def 						__magic_name__(			_A = 100       ):
						'''simple docstring'''





						UpperCamelCase__       =			[*list(range(1    ,   21       )       ), 25]
						UpperCamelCase__       =			[2 * x for x in range(1    ,   21       )] + [50]
						UpperCamelCase__       =			[3 * x for x in range(1    ,   21       )]
						UpperCamelCase__       =			singles + doubles + triples + [0]

						UpperCamelCase__       =			0
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_A    ,   2       ):
																		UpperCamelCase__       =			double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


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