


"""simple docstring"""



import math
import os
import sys







def   __lowerCAmelCase						(						lowercase  :  str )	->						Optional[int]:


    """simple docstring"""





    snake_case    :      str      					=    ""
    try:
        with open(_A						,							"rb" ) as binary_file:
            snake_case    :      Optional[Any]      					=    binary_file.read()
        for dat in data:
            snake_case    :      List[str]      					=    F'{dat:08b}'
            result += curr_byte
        return result
    except OSError:
        print("File not accessible" )
        sys.exit()







def   __lowerCAmelCase						(						lowercase  :  dict[str, str]						,							lowercase  :  str						,							lowercase  :  int						,							lowercase  :  str )	->						Tuple:


    """simple docstring"""





    lexicon.pop(_A )
    snake_case    :      List[str]      					=    last_match_id

    if math.loga(_A ).is_integer():
        for curr_key in lexicon:
            snake_case    :      List[str]      					=    "0" + lexicon[curr_key]

    snake_case    :      Any      					=    bin(_A )[2:]







def   __lowerCAmelCase						(						lowercase  :  str )	->						str:


    """simple docstring"""





    snake_case    :      Tuple      					=    {"0": "0", "1": "1"}
    snake_case					,snake_case    :      Tuple      					=    "", ""
    snake_case    :      int      					=    len(_A )

    for i in range(len(_A ) ):
        curr_string += data_bits[i]
        if curr_string not in lexicon:
            continue

        snake_case    :      Optional[Any]      					=    lexicon[curr_string]
        result += last_match_id
        add_key_to_lexicon(_A						,							_A						,							_A						,							_A )
        index += 1
        snake_case    :      Optional[int]      					=    ""

    while curr_string != "" and curr_string not in lexicon:
        curr_string += "0"

    if curr_string != "":
        snake_case    :      List[Any]      					=    lexicon[curr_string]
        result += last_match_id

    return result







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str )	->						Dict:


    """simple docstring"""





    snake_case    :      int      					=    os.path.getsize(_A )
    snake_case    :      int      					=    bin(_A )[2:]
    snake_case    :      int      					=    len(_A )

    return "0" * (length_length - 1) + file_length_binary + compressed







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str )	->						Dict:


    """simple docstring"""





    snake_case    :      Optional[int]      					=    8
    try:
        with open(_A						,							"wb" ) as opened_file:
            snake_case    :      List[Any]      					=    [
                to_write[i : i + byte_length]
                for i in range(0						,							len(_A )						,							_A )
            ]

            if len(result_byte_array[-1] ) % byte_length == 0:
                result_byte_array.append("10000000" )
            else:
                result_byte_array[-1] += "1" + "0" * (
                    byte_length - len(result_byte_array[-1] ) - 1
                )

            for elem in result_byte_array:
                opened_file.write(int(_A						,							2 ).to_bytes(1						,							byteorder="big" ) )
    except OSError:
        print("File not accessible" )
        sys.exit()







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str )	->						List[str]:


    """simple docstring"""





    snake_case    :      Dict      					=    read_file_binary(_A )
    snake_case    :      Any      					=    compress_data(_A )
    snake_case    :      List[Any]      					=    add_file_length(_A						,							_A )
    write_file_binary(_A						,							_A )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])








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
