code: 
  """simple docstring"""
  import argparse
  import collections
  from pathlib import Path
  
  import torch
  from torch.serialization import default_restore_location
  
  from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
  
  
  __magic_name__		       =				collections.namedtuple(
      '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
  )
  
  
  
  
  def     __lowerCamelCase	(		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	print(f"Reading saved model from {model_file}"      )
  	_UpperCAmelCase =						torch.load(_UpperCAmelCase				,     map_location=lambda UpperCamelCase__				,     UpperCamelCase__      : default_restore_location(_UpperCAmelCase				,     "cpu"      )      )
  	return CheckpointState(**_UpperCAmelCase      )
  
  
  class    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_   )							->							Optional[Any]:
  								_UpperCAmelCase =						src_file
  
  
  
  							def       _a   (      self   )							->							str:
  								raise NotImplementedError
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						*a_ ,						**a_   )							->							List[str]:
  								if comp_type.startswith("c"   ):
  									return DPRContextEncoderState(*UpperCAmelCase_ ,						**UpperCAmelCase_   )
  								if comp_type.startswith("q"   ):
  									return DPRQuestionEncoderState(*UpperCAmelCase_ ,						**UpperCAmelCase_   )
  								if comp_type.startswith("r"   ):
  									return DPRReaderState(*UpperCAmelCase_ ,						**UpperCAmelCase_   )
  								else:
  									raise ValueError("Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."   )
  
  
  
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"   )[0]   )   )
  								print(f"Loading DPR biencoder from {self.src_file}"   )
  								_UpperCAmelCase =						load_states_from_checkpoint(self.src_file   )
  								_UpperCAmelCase =						model.ctx_encoder, 'ctx_model.'
  								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  								_UpperCAmelCase =						{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
  								for key, value in saved_state.model_dict.items():
  									if key.startswith(UpperCAmelCase_   ):
  										_UpperCAmelCase =						key[len(UpperCAmelCase_   ) :]
  										if not key.startswith("encode_proj."   ):
  											_UpperCAmelCase =						'bert_model.' + key
  										_UpperCAmelCase =						value
  								encoder.load_state_dict(UpperCAmelCase_   )
  								return model
  
  
  
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								_UpperCAmelCase =						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"   )[0]   )   )
  								print(f"Loading DPR biencoder from {self.src_file}"   )
  								_UpperCAmelCase =						load_states_from_checkpoint(self.src_file   )
  								_UpperCAmelCase =						model.question_encoder, 'question_model.'
  								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  								_UpperCAmelCase =						{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
  								for key, value in saved_state.model_dict.items():
  									if key.startswith(UpperCAmelCase_   ):
  										_UpperCAmelCase =						key[len(UpperCAmelCase_   ) :]
  										if not key.startswith("encode_proj."   ):
  											_UpperCAmelCase =						'bert_model.' + key
  										_UpperCAmelCase =						value
  								encoder.load_state_dict(UpperCAmelCase_   )
  								return model
  
  
  
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"   )[0]   )   )
  								print(f"Loading DPR reader from {self.src_file}"   )
  								_UpperCAmelCase =						load_states_from_checkpoint(self.src_file   )
  								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  								_UpperCAmelCase =						{
  								    'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
  								}
  								for key, value in saved_state.model_dict.items():
  									if key.startswith("encoder."   ) and not key.startswith("encoder.encode_proj"   ):
  										_UpperCAmelCase =						'encoder.bert_model.' + key[len("encoder."   ) :]
  									_UpperCAmelCase =						value
  								model.span_predictor.load_state_dict(UpperCAmelCase_   )
  								return model
  
  
  
  
  
  def     __lowerCamelCase	(		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Path(_UpperCAmelCase      )
  	dest_dir.mkdir(exist_ok=_UpperCAmelCase      )
  
  	_UpperCAmelCase =						DPRState.from_type(_UpperCAmelCase				,     src_file=_UpperCAmelCase      )
  	_UpperCAmelCase =						dpr_state.load_dpr_model()
  	model.save_pretrained(_UpperCAmelCase      )
  	model.from_pretrained(_UpperCAmelCase      )  # sanity check
  
  
  if __name__ == "__main__":
  			__magic_name__		       =				argparse.ArgumentParser()
  			# Required parameters
  			parser.add_argument(
  			    '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
  			)
  			parser.add_argument(
  			    '''--src''',
  			    type=str,
  			    help=(
  			        '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
  			        ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
  			        ''' \'retriever\' checkpoints.'''
  			    ),
  			)
  			parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
  			__magic_name__		       =				parser.parse_args()
  
  			__magic_name__		       =				Path(args.src)
  			__magic_name__		       =				f'''converted-{src_file.name}''' if args.dest is None else args.dest
  			__magic_name__		       =				Path(dest_dir)
  			assert src_file.exists()
  			assert (
  			    args.type is not None
  			), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  			convert(args.type, src_file, dest_dir)
  
  
  
  
  
style_context: 
  """simple docstring"""
  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
  
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						f"{sampling_rate}"
  	_UpperCAmelCase =						"1"
  	_UpperCAmelCase =						"f32le"
  	_UpperCAmelCase =						[
  	    "ffmpeg",
  	    "-i",
  	    "pipe:0",
  	    "-ac",
  	    ac,
  	    "-ar",
  	    ar,
  	    "-f",
  	    format_for_conversion,
  	    "-hide_banner",
  	    "-loglevel",
  	    "quiet",
  	    "pipe:1",
  	]
  
  	try:
  		_UpperCAmelCase =						subprocess.Popen(UpperCamelCase__				,     stdin=subprocess.PIPE				,     stdout=subprocess.PIPE      )
  	except FileNotFoundError:
  		raise ValueError("ffmpeg was not found but is required to load audio files from filename"      )
  	_UpperCAmelCase =						ffmpeg_process.communicate(UpperCamelCase__      )
  	_UpperCAmelCase =						output_stream[0]
  
  	_UpperCAmelCase =						np.frombuffer(UpperCamelCase__				,     np.floataa      )
  	if audio.shape[0] == 0:
  		raise ValueError("Malformed soundfile"      )
  	return audio
  
  
  
  @add_end_docstrings(lowerCamelCase						)
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def __init__(      self ,						*a_ ,						**a_   )							->							Union[str, Any]:
  								# Default, might be overriden by the model.config.
  								_UpperCAmelCase =						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 ,						a_ ,						**a_ ,						)							->							int:
  								return super().__call__(a_ ,						**a_   )
  
  
  
  							def       _a   (      self ,						a_=None ,						**a_   )							->							Dict:
  								# No parameters on this pipeline right now
  								_UpperCAmelCase =						{}
  								if top_k is not None:
  									if top_k > self.model.config.num_labels:
  										_UpperCAmelCase =						self.model.config.num_labels
  									_UpperCAmelCase =						top_k
  								return {}, {}, postprocess_params
  
  
  
  							def       _a   (      self ,						a_   )							->							str:
  								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
  										_UpperCAmelCase =						requests.get(a_   ).content
  									else:
  										with open(a_ ,						"rb"   ) as f:
  											_UpperCAmelCase =						f.read()
  
  								if isinstance(a_ ,						a_   ):
  									_UpperCAmelCase =						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"   )
  
  									_UpperCAmelCase =						inputs.pop("raw" ,						a_   )
  									if _inputs is None:
  										# Remove path which will not be used from `datasets`.
  										inputs.pop("path" ,						a_   )
  										_UpperCAmelCase =						inputs.pop("array" ,						a_   )
  									_UpperCAmelCase =						inputs.pop("sampling_rate"   )
  									_UpperCAmelCase =						_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`."   )
  
  										_UpperCAmelCase =						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"   )
  
  								_UpperCAmelCase =						self.feature_extractor(
  								    a_ ,						sampling_rate=self.feature_extractor.sampling_rate ,						return_tensors="pt"   )
  								return processed
  
  
  
  							def       _a   (      self ,						a_   )							->							List[str]:
  								_UpperCAmelCase =						self.model(**a_   )
  								return model_outputs
  
  
  
  							def       _a   (      self ,						a_ ,						a_=5   )							->							int:
  								_UpperCAmelCase =						model_outputs.logits[0].softmax(-1   )
  								_UpperCAmelCase ,      _UpperCAmelCase =						probs.topk(a_   )
  
  								_UpperCAmelCase =						scores.tolist()
  								_UpperCAmelCase =						ids.tolist()
  
  								_UpperCAmelCase =						[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a_ ,						a_   )]
  
  								return labels
  
  
  
  
  