



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						  =     logging.get_logger(__name__)





def 						__lowerCamelCase       (      __magic_name__	:  bytes     , __magic_name__	:  int						):
       a__:       Optional[int]											=F"{sampling_rate}"
       a__:       Union[str, Any]											="1"
       a__:       List[str]											="f32le"
       a__:       Dict											=[
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

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

       a__:       str											=np.frombuffer(__magic_name__     , np.floataa						)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"						)
       return audio







@add_end_docstrings(_a   )
class 	lowerCamelCase__ (		_a   ):


      def __init__( self						:  Union[str, Any]      ,       *_a						:  Dict      ,       **_a						:  Tuple			):
             # Default, might be overriden by the model.config.
             a__:       List[Any]											=5
             super().__init__(*_a      ,       **_a			)

             if self.framework != "pt":
                    raise ValueError(F"The {self.__class__} is only available in PyTorch."			)

             self.check_model_type(_a			)


      def __call__( self						:  Tuple      ,       _a						:  Union[np.ndarray, bytes, str]      ,       **_a						:  Union[str, Any]      ,       ):
             return super().__call__(_a      ,       **_a			)


      def   _lowerCamelCase			( self						:  int      ,       _a						:  Tuple=None      ,       **_a						:  int			):
             # No parameters on this pipeline right now
             a__:       Optional[int]											={}
             if top_k is not None:
                    if top_k > self.model.config.num_labels:
                           a__:       Union[str, Any]											=self.model.config.num_labels
                    a__:       Union[str, Any]											=top_k
             return {}, {}, postprocess_params


      def   _lowerCamelCase			( self						:  Any      ,       _a						:  Union[str, Any]			):
             if isinstance(_a      ,       _a			):
                    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
                           a__:       List[str]											=requests.get(_a			).content
                    else:
                           with open(_a      ,       "rb"			) as f:
                                  a__:       Dict											=f.read()

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

             if isinstance(_a      ,       _a			):
                    # 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"			)

                    a__:       Tuple											=inputs.pop("raw"      ,       _a			)
                    if _inputs is None:
                           # Remove path which will not be used from `datasets`.
                           inputs.pop("path"      ,       _a			)
                           a__:       int											=inputs.pop("array"      ,       _a			)
                    a__:       Optional[Any]											=inputs.pop("sampling_rate"			)
                    a__:       Optional[int]											=_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`."			)

                           a__:       Union[str, Any]											=F.resample(
                               torch.from_numpy(_a			)      ,       _a      ,       self.feature_extractor.sampling_rate			).numpy()

             if not isinstance(_a      ,       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"			)

             a__:       Union[str, Any]											=self.feature_extractor(
                 _a      ,       sampling_rate=self.feature_extractor.sampling_rate      ,       return_tensors="pt"			)
             return processed


      def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Tuple			):
             a__:       str											=self.model(**_a			)
             return model_outputs







      def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple      ,       _a						:  Tuple=5			):
             a__:       Tuple											=model_outputs.logits[0].softmax(-1			)
             a__:       Any											=probs.topk(_a			)

             a__:       Union[str, Any]											=scores.tolist()
             a__:       Dict											=ids.tolist()

             a__:       Optional[int]											=[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a      ,       _a			)]

             return labels










from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


						def   _lowerCamelCase			( self						:  int			):
													return self.head.data if self.head is not None else None


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=temp.next


# Code execution starts here
if __name__ == "__main__":
		# Start with the empty stack
		__UpperCAmelCase						  =     Stack()

		# Insert 4 at the beginning. So stack becomes 4->None
		print('''Stack operations using Doubly LinkedList''')
		stack.push(4)

		# Insert 5 at the beginning. So stack becomes 4->5->None
		stack.push(5)

		# Insert 6 at the beginning. So stack becomes 4->5->6->None
		stack.push(6)

		# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
		stack.push(7)

		# Print the stack
		stack.print_stack()

		# Print the top element
		print('''\nTop element is ''', stack.top())

		# Print the stack size
		print('''Size of the stack is ''', len(stack))

		# pop the top element
		stack.pop()

		# pop the top element
		stack.pop()

		# two elements have now been popped off
		stack.print_stack()

		# Print True if the stack is empty else False
		print('''\nstack is empty:''', stack.is_empty())





