


"""simple docstring"""



from __future__ import annotations

from decimal import Decimal

from numpy import array







def   __lowerCAmelCase						(						lowercase  :  list[list[float]] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      str      					=    Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(_A ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
        # Calculate the determinant of the matrix
        snake_case    :      Any      					=    float(
            d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
        if determinant == 0:
            raise ValueError("This matrix has no inverse." )

        # Creates a copy of the matrix with swapped positions of the elements
        snake_case    :      int      					=    [[0.0, 0.0], [0.0, 0.0]]
        snake_case					,snake_case    :      Optional[int]      					=    matrix[1][1], matrix[0][0]
        snake_case					,snake_case    :      int      					=    -matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(_A ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(_A ) == 3
        and len(matrix[0] ) == 3
        and len(matrix[1] ) == 3
        and len(matrix[2] ) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        snake_case    :      Tuple      					=    float(
            (
                (d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
                + (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
                + (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
            )
            - (
                (d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
                + (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
                + (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
            ) )
        if determinant == 0:
            raise ValueError("This matrix has no inverse." )

        # Creating cofactor matrix
        snake_case    :      Any      					=    [
            [d(0.0 ), d(0.0 ), d(0.0 )],
            [d(0.0 ), d(0.0 ), d(0.0 )],
            [d(0.0 ), d(0.0 ), d(0.0 )],
        ]
        snake_case    :      Optional[int]      					=    (d(matrix[1][1] ) * d(matrix[2][2] )) - (
            d(matrix[1][2] ) * d(matrix[2][1] )
        )
        snake_case    :      List[Any]      					=    -(
            (d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
        )
        snake_case    :      Optional[int]      					=    (d(matrix[1][0] ) * d(matrix[2][1] )) - (
            d(matrix[1][1] ) * d(matrix[2][0] )
        )
        snake_case    :      Dict      					=    -(
            (d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
        )
        snake_case    :      Tuple      					=    (d(matrix[0][0] ) * d(matrix[2][2] )) - (
            d(matrix[0][2] ) * d(matrix[2][0] )
        )
        snake_case    :      Optional[Any]      					=    -(
            (d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
        )
        snake_case    :      List[str]      					=    (d(matrix[0][1] ) * d(matrix[1][2] )) - (
            d(matrix[0][2] ) * d(matrix[1][1] )
        )
        snake_case    :      Optional[int]      					=    -(
            (d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
        )
        snake_case    :      Optional[int]      					=    (d(matrix[0][0] ) * d(matrix[1][1] )) - (
            d(matrix[0][1] ) * d(matrix[1][0] )
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        snake_case    :      List[Any]      					=    array(_A )
        for i in range(3 ):
            for j in range(3 ):
                snake_case    :      int      					=    cofactor_matrix[j][i]

                # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        snake_case    :      Any      					=    array(_A )
        for i in range(3 ):
            for j in range(3 ):
                inverse_matrix[i][j] /= d(_A )

                # Calculate the inverse of the matrix
        return [[float(d(_A ) ) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3." )








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
