






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

UpperCamelCase												=      logging.get_logger(__name__)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    np.array:






						"""simple docstring"""





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

						try:
												_SCREAMING_SNAKE_CASE       =    subprocess.Popen(snake_case__			,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       =    ffmpeg_process.communicate(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    output_stream[0]

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




@add_end_docstrings(_UpperCAmelCase       )
class  __UpperCAmelCase						(_UpperCAmelCase       ):







			def __init__(      self:    List[Any] ,      *UpperCAmelCase_:    Tuple ,      **UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    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:    List[Any] ,      UpperCAmelCase_:    Union[np.ndarray, bytes, str] ,      **UpperCAmelCase_:    List[str] ,      ):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    List[Any] ,      UpperCAmelCase_:    Optional[int]=None ,      **UpperCAmelCase_:    int	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    Optional[Any]	):







									'''simple docstring'''




									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
																					_SCREAMING_SNAKE_CASE       =    requests.get(UpperCAmelCase_	).content
															else:
																					with open(UpperCAmelCase_ ,      """rb"""	) as f:
																											_SCREAMING_SNAKE_CASE       =    f.read()

									if isinstance(UpperCAmelCase_ ,      UpperCAmelCase_	):
															_SCREAMING_SNAKE_CASE       =    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"""	)

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

									_SCREAMING_SNAKE_CASE       =    self.feature_extractor(
									    UpperCAmelCase_ ,      sampling_rate=self.feature_extractor.sampling_rate ,      return_tensors="""pt"""	)
									return processed







			def 				UpperCamelCase (      self:    Dict ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model(**UpperCAmelCase_	)
									return model_outputs







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Dict=5	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    model_outputs.logits[0].softmax(-1	)
									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    probs.topk(UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    scores.tolist()
									_SCREAMING_SNAKE_CASE       =    ids.tolist()

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

									return labels









import math
from timeit import timeit

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    abs(snake_case__						)
						while True:
												_SCREAMING_SNAKE_CASE       =    n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						return 1 if n == 0 else math.floor(math.log(abs(snake_case__						)			,10						) + 1						)

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						return len(str(abs(snake_case__						)						)						)

def 			__lowerCamelCase     (						)				->    None:






						"""simple docstring"""





						from collections.abc import Callable

						def benchmark_a_function(snake_case__			,snake_case__						) -> None:
												_SCREAMING_SNAKE_CASE       =    F'{func.__name__}({value})'
												_SCREAMING_SNAKE_CASE       =    timeit(F'__main__.{call}'			,setup="""import __main__"""						)
												print(F'{call}: {func(snake_case__						)} -- {timing} seconds'						)

						for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(snake_case__			,snake_case__						)
												print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()

