

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowerCamelCase           =					logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    **lowercase_       )							->    Dict:






     """simple docstring"""






     super().__init__(**lowercase_       )

     if self.framework != "pt":
       raise ValueError(F"""The {self.__class__} is only available in PyTorch."""       )
     # No specific FOR_XXX available yet



   def __call__(				self,    lowercase_,    **lowercase_       )							->    Tuple:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    int:






     """simple docstring"""






     a__  ={}
     if "candidate_labels" in kwargs:
       a__  =kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
       a__  =kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=None,    lowercase_="This is a sound of {}."       )							->    Union[str, Any]:






     """simple docstring"""






     if isinstance(lowercase_,    lowercase_       ):
       if audio.startswith('''http://'''       ) or audio.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
         a__  =requests.get(lowercase_       ).content
       else:
         with open(lowercase_,    '''rb'''       ) as f:
           a__  =f.read()

     if isinstance(lowercase_,    lowercase_       ):
       a__  =ffmpeg_read(lowercase_,    self.feature_extractor.sampling_rate       )

     if not isinstance(lowercase_,    np.ndarray       ):
       raise ValueError('''We expect a numpy ndarray as input'''       )
     if len(audio.shape       ) != 1:
       raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''       )

     a__  =self.feature_extractor(
         [audio],    sampling_rate=self.feature_extractor.sampling_rate,    return_tensors='''pt'''       )
     a__  =candidate_labels
     a__  =[hypothesis_template.format(lowercase_       ) for x in candidate_labels]
     a__  =self.tokenizer(lowercase_,    return_tensors=self.framework,    padding=lowercase_       )
     a__  =[text_inputs]
     return inputs



   def  _UpperCAmelCase (				self,    lowercase_       )							->    str:






     """simple docstring"""






     a__  =model_inputs.pop('''candidate_labels'''       )
     a__  =model_inputs.pop('''text_inputs'''       )
     if isinstance(text_inputs[0],    lowercase_       ):
       a__  =text_inputs[0]
     else:
       # Batching case.
       a__  =text_inputs[0][0]

     a__  =self.model(**lowercase_,    **lowercase_       )

     a__  ={
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Any:






     """simple docstring"""






     a__  =model_outputs.pop('''candidate_labels'''       )
     a__  =model_outputs['''logits'''][0]

     if self.framework == "pt":
       a__  =logits.softmax(dim=0       )
       a__  =probs.tolist()
     else:
       raise ValueError('''`tf` framework not supported.'''       )

     a__  =[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(lowercase_,    lowercase_       ),    key=lambda lowercase_       : -x[0]       )
     ]
     return result
