



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

lowerCAmelCase					:				int   	=logging.get_logger(__name__)







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[Any]					     =					F"""{sampling_rate}"""
						lowerCAmelCase	:   Any					     =					"""1"""
						lowerCAmelCase	:   Union[str, Any]					     =					"""f32le"""
						lowerCAmelCase	:   Any					     =					[
						    """ffmpeg""",
						    """-i""",
						    """pipe:0""",
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]

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

						lowerCAmelCase	:   int					     =					np.frombuffer(lowercase__			,np.floataa  )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""  )
						return audio






@add_end_docstrings(_lowerCamelCase  )
class 	_a			(      _lowerCamelCase  ):






			def __init__(    self    ,  *lowercase_    ,  **lowercase_ )	->					int:
									lowerCAmelCase	:   Union[str, Any]					     =					5
									super().__init__(*__lowerCamelCase    ,  **__lowerCamelCase )

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

									self.check_model_type(__lowerCamelCase )






			def __call__(    self    ,  lowercase_    ,  **lowercase_    ,  )	->					str:
									return super().__call__(__lowerCamelCase    ,  **__lowerCamelCase )






			def 							_snake_case   (    self    ,  lowercase_=None    ,  **lowercase_ )	->					List[Any]:
									lowerCAmelCase	:   str					     =					{}
									if top_k is not None:
															if top_k > self.model.config.num_labels:
																					lowerCAmelCase	:   Optional[Any]					     =					self.model.config.num_labels
															lowerCAmelCase	:   Optional[Any]					     =					top_k
									return {}, {}, postprocess_params






			def 							_snake_case   (    self    ,  lowercase_ )	->					Any:




									if isinstance(__lowerCamelCase    ,  __lowerCamelCase ):
															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	:   int					     =					requests.get(__lowerCamelCase ).content
															else:
																					with open(__lowerCamelCase    ,  """rb""" ) as f:
																											lowerCAmelCase	:   Union[str, Any]					     =					f.read()

									if isinstance(__lowerCamelCase    ,  __lowerCamelCase ):
															lowerCAmelCase	:   Any					     =					ffmpeg_read(__lowerCamelCase    ,  self.feature_extractor.sampling_rate )

									if isinstance(__lowerCamelCase    ,  __lowerCamelCase ):
															# 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	:   Union[str, Any]					     =					inputs.pop("""raw"""    ,  __lowerCamelCase )
															if _inputs is None:
																					# Remove path which will not be used from `datasets`.
																					inputs.pop("""path"""    ,  __lowerCamelCase )
																					lowerCAmelCase	:   int					     =					inputs.pop("""array"""    ,  __lowerCamelCase )
															lowerCAmelCase	:   List[str]					     =					inputs.pop("""sampling_rate""" )
															lowerCAmelCase	:   Optional[Any]					     =					_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	:   Optional[int]					     =					F.resample(
																					    torch.from_numpy(__lowerCamelCase )    ,  __lowerCamelCase    ,  self.feature_extractor.sampling_rate ).numpy()

									if not isinstance(__lowerCamelCase    ,  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	:   str					     =					self.feature_extractor(
									    __lowerCamelCase    ,  sampling_rate=self.feature_extractor.sampling_rate    ,  return_tensors="""pt""" )
									return processed






			def 							_snake_case   (    self    ,  lowercase_ )	->					str:
									lowerCAmelCase	:   Union[str, Any]					     =					self.model(**__lowerCamelCase )
									return model_outputs






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_=5 )	->					Optional[int]:
									lowerCAmelCase	:   Tuple					     =					model_outputs.logits[0].softmax(-1 )
									lowerCAmelCase     ,	lowerCAmelCase	:   int					     =					probs.topk(__lowerCamelCase )

									lowerCAmelCase	:   Optional[Any]					     =					scores.tolist()
									lowerCAmelCase	:   Optional[Any]					     =					ids.tolist()

									lowerCAmelCase	:   List[str]					     =					[{"""score""": score, """label""": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase    ,  __lowerCamelCase )]

									return labels









'''simple docstring'''





def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  (
     first_input_list_length
     if first_input_list_length > second_input_list_length
     else second_input_list_length
 )
 lowerCamelCase__	=  []
 for char_count in range(lowercase__):
  if char_count < first_input_list_length:
   output_result_list.append(first_input_list[char_count])
  if char_count < second_input_list_length:
   output_result_list.append(second_input_list[char_count])

 return output_result_list


if __name__ == "__main__":
 print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)

