






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				:	Optional[int]    						=    logging.get_logger(__name__)






def    UpperCAmelCase__	(						lowerCamelCase  : bytes ,lowerCamelCase  : int						):
   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(UpperCamelCase   )
class 		lowerCAmelCase_							(     UpperCamelCase   ):





     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       ,   )		->							Union[str, Any]:
        return super().__call__(_UpperCAmelCase       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase=None       ,   **_UpperCAmelCase     )		->							Optional[int]:
        # 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 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Dict:
        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 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							int:
        lowerCamelCase											=self.model(**_UpperCAmelCase     )
        return model_outputs





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase=5     )		->							Tuple:
        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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase				:	List[str]    						=    {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase				:	Optional[Any]    						=    [
       'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'UniSpeechSatForAudioFrameClassification',
       'UniSpeechSatForCTC',
       'UniSpeechSatForPreTraining',
       'UniSpeechSatForSequenceClassification',
       'UniSpeechSatForXVector',
       'UniSpeechSatModel',
       'UniSpeechSatPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase				:	Optional[Any]    						=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)