


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

_snake_case				=			logging.get_logger(__name__)







def 	A (		A__		,		A__			):
     UpperCAmelCase    :					Union[str, Any]			=      f'{sampling_rate}'
     UpperCAmelCase    :					Union[str, Any]			=      "1"
     UpperCAmelCase    :					int			=      "f32le"
     UpperCAmelCase    :					Any			=      [
         "ffmpeg",
         "-i",
         "pipe:0",
         "-ac",
         ac,
         "-ar",
         ar,
         "-f",
         format_for_conversion,
         "-hide_banner",
         "-loglevel",
         "quiet",
         "pipe:1",
     ]

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

     UpperCAmelCase    :					Optional[Any]			=      np.frombuffer(__UpperCamelCase		,		np.floataa			)
     if audio.shape[0] == 0:
          raise ValueError("Malformed soundfile"			)
     return audio




@add_end_docstrings(lowerCamelCase_  )
class  A_ (			lowerCamelCase_  ):







 """simple docstring"""





 def __init__(      self       :			Optional[Any]							,     *_a       :			str							,     **_a       :			Optional[int]					)     ->      Dict:
      UpperCAmelCase    :					List[str]			=      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       :			Optional[int]							,     _a       :			Union[np.ndarray, bytes, str]							,     **_a       :			str							,     )     ->      str:
      return super().__call__(_lowercase							,     **_lowercase					)
 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			Tuple=None							,     **_a       :			int					)     ->      Optional[Any]:
      UpperCAmelCase    :					List[str]			=      {}
      if top_k is not None:
           if top_k > self.model.config.num_labels:
                UpperCAmelCase    :					Union[str, Any]			=      self.model.config.num_labels
           UpperCAmelCase    :					Any			=      top_k
      return {}, {}, postprocess_params
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			List[str]					)     ->      Any:






      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    :					Tuple			=      requests.get(_lowercase					).content
           else:
                with open(_lowercase							,     "rb"					) as f:
                     UpperCAmelCase    :					Optional[Any]			=      f.read()

      if isinstance(_lowercase							,     _lowercase					):
           UpperCAmelCase    :					Optional[Any]			=      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    :					Union[str, Any]			=      inputs.pop("raw"							,     _lowercase					)
           if _inputs is None:
                # Remove path which will not be used from `datasets`.
                inputs.pop("path"							,     _lowercase					)
                UpperCAmelCase    :					Optional[int]			=      inputs.pop("array"							,     _lowercase					)
           UpperCAmelCase    :					Optional[Any]			=      inputs.pop("sampling_rate"					)
           UpperCAmelCase    :					Dict			=      _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    :					str			=      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    :					Tuple			=      self.feature_extractor(
          _lowercase							,     sampling_rate=self.feature_extractor.sampling_rate							,     return_tensors="pt"					)
      return processed
 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			int					)     ->      List[str]:
      UpperCAmelCase    :					Dict			=      self.model(**_lowercase					)
      return model_outputs

 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Union[str, Any]							,     _a       :			Union[str, Any]=5					)     ->      Optional[Any]:
      UpperCAmelCase    :					Optional[int]			=      model_outputs.logits[0].softmax(-1					)
      UpperCAmelCase	,			UpperCAmelCase    :					int			=      probs.topk(_lowercase					)

      UpperCAmelCase    :					Optional[int]			=      scores.tolist()
      UpperCAmelCase    :					List[Any]			=      ids.tolist()

      UpperCAmelCase    :					List[str]			=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase							,     _lowercase					)]

      return labels




from hashlib import mda, shaaaa

__lowerCamelCase     :					Dict				  =						(shaaaa, mda)







class __snake_case							:




   def __init__(       self     :							str			,      _lowercase     :							int = 8 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     SCREAMING_SNAKE_CASE__     				= size




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     self.bitarray |= h




   def       __a		(       self     :							int			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     return (h & self.bitarray) == h




   def __contains__(       self     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.exists(_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(_lowercase )[2:]
     return res.zfill(self.size )




   @property
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.format_bin(self.bitarray )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     for func in HASH_FUNCTIONS:
       SCREAMING_SNAKE_CASE__     				= (
           int.from_bytes(func(value.encode() ).digest()			,      """little""" ) % self.size
       )
       res |= 2**position
     return res




   def       __a		(       self     :							str			,      _lowercase     :							str ):



     """simple docstring"""






     return self.format_bin(self.hash_(_lowercase ) )




   @property
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(self.bitarray ).count("""1""" )
     return (n_ones / self.size) ** len(_lowercase )
