

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase =				logging.get_logger(__name__)







class      __lowercase			(    __UpperCAmelCase					):
  snake_case_				       =       ["input_values", "padding_mask"]

  def __init__(  self		:			List[Any]					,A		:			int = 1					,A		:			int = 24_000					,A		:			float = 0.0					,A		:			float = None					,A		:			float = None					,**A		:			str					,):






   '''simple docstring'''
   super().__init__(feature_size=_lowerCamelCase					,sampling_rate=_lowerCamelCase					,padding_value=_lowerCamelCase					,**_lowerCamelCase    )
   UpperCAmelCase__					:  List[str]					   =					chunk_length_s
   UpperCAmelCase__					:  List[Any]					   =					overlap

  @property
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   if self.chunk_length_s is None:
    return None
   else:
    return int(self.chunk_length_s * self.sampling_rate    )

  @property
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   if self.chunk_length_s is None or self.overlap is None:
    return None
   else:
    return max(1					,int((1.0 - self.overlap) * self.chunk_length    )    )




  def __call__(  self		:			List[str]					,A		:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,A		:			Optional[Union[bool, str, PaddingStrategy]] = None					,A		:			Optional[bool] = False					,A		:			Optional[int] = None					,A		:			Optional[Union[str, TensorType]] = None					,A		:			Optional[int] = None					,):






   '''simple docstring'''
   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
         f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
         f" {self.sampling_rate} and not {sampling_rate}."    )
   else:
    logger.warning(
        """It is strongly recommended to pass the `sampling_rate` argument to this function. """
        """Failing to do so can result in silent errors that might be hard to debug."""    )

   if padding and truncation:
    raise ValueError("""Both padding and truncation were set. Make sure you only set one."""    )
   elif padding is None:
    # by default let's pad the inputs
    UpperCAmelCase__					:  Tuple					   =					True

   UpperCAmelCase__					:  List[Any]					   =					bool(
       isinstance(_lowerCamelCase					,(list, tuple)    ) and (isinstance(raw_audio[0]					,(np.ndarray, tuple, list)    ))    )

   if is_batched:
    UpperCAmelCase__					:  Any					   =					[np.asarray(_lowerCamelCase					,dtype=np.floataa    ).T for audio in raw_audio]
   elif not is_batched and not isinstance(_lowerCamelCase					,np.ndarray    ):
    UpperCAmelCase__					:  Dict					   =					np.asarray(_lowerCamelCase					,dtype=np.floataa    )
   elif isinstance(_lowerCamelCase					,np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
    UpperCAmelCase__					:  Tuple					   =					raw_audio.astype(np.floataa    )

   # always return batch
   if not is_batched:
    UpperCAmelCase__					:  List[str]					   =					[np.asarray(_lowerCamelCase    ).T]

   # verify inputs are valid
   for idx, example in enumerate(_lowerCamelCase    ):
    if example.ndim > 2:
     raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}"    )
    if self.feature_size == 1 and example.ndim != 1:
     raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels"    )
    if self.feature_size == 2 and example.shape[-1] != 2:
     raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels"    )

   UpperCAmelCase__					:  Any					   =					None
   UpperCAmelCase__					:  str					   =					BatchFeature({"""input_values""": raw_audio}    )
   if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
    if truncation:
     UpperCAmelCase__					:  Any					   =					min(array.shape[0] for array in raw_audio    )
     UpperCAmelCase__					:  Optional[Any]					   =					int(np.floor(max_length / self.chunk_stride    )    )
     UpperCAmelCase__					:  List[str]					   =					(nb_step - 1) * self.chunk_stride + self.chunk_length
    elif padding:
     UpperCAmelCase__					:  Any					   =					max(array.shape[0] for array in raw_audio    )
     UpperCAmelCase__					:  Tuple					   =					int(np.ceil(max_length / self.chunk_stride    )    )
     UpperCAmelCase__					:  Optional[Any]					   =					(nb_step - 1) * self.chunk_stride + self.chunk_length
     UpperCAmelCase__					:  str					   =					"""max_length"""
    else:
     UpperCAmelCase__					:  Tuple					   =					input_values

        # normal padding on batch
   if padded_inputs is None:
    UpperCAmelCase__					:  Optional[Any]					   =					self.pad(
        _lowerCamelCase					,max_length=_lowerCamelCase					,truncation=_lowerCamelCase					,padding=_lowerCamelCase					,return_attention_mask=_lowerCamelCase					,)
    if padding:
     UpperCAmelCase__					:  Any					   =					padded_inputs.pop("""attention_mask"""    )

   UpperCAmelCase__					:  Any					   =					[]
   for example in padded_inputs.pop("""input_values"""    ):
    if self.feature_size == 1:
     UpperCAmelCase__					:  int					   =					example[..., None]
    input_values.append(example.T    )

   UpperCAmelCase__					:  int					   =					input_values
   if return_tensors is not None:
    UpperCAmelCase__					:  Dict					   =					padded_inputs.convert_to_tensors(_lowerCamelCase    )

   return padded_inputs








"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 if "model" in orig_key:
  UpperCAmelCase__					:  List[str]					   =					orig_key.replace("""model."""							,    """"""      )
 if "norm1" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""norm1"""							,    """attention.output.LayerNorm"""      )
 if "norm2" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""norm2"""							,    """output.LayerNorm"""      )
 if "norm" in orig_key:
  UpperCAmelCase__					:  int					   =					orig_key.replace("""norm"""							,    """LayerNorm"""      )
 if "transformer" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.split("""."""      )[0].split("""_"""      )[-1]
  UpperCAmelCase__					:  Dict					   =					orig_key.replace(F"transformer_{layer_num}"							,    F"encoder.layer.{layer_num}"      )
 if "mha.attn" in orig_key:
  UpperCAmelCase__					:  Tuple					   =					orig_key.replace("""mha.attn"""							,    """attention.self"""      )
 if "mha" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""mha"""							,    """attention"""      )
 if "W_q" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_q"""							,    """self.query"""      )
 if "W_k" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""W_k"""							,    """self.key"""      )
 if "W_v" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_v"""							,    """self.value"""      )
 if "ff1" in orig_key:
  UpperCAmelCase__					:  Optional[int]					   =					orig_key.replace("""ff1"""							,    """intermediate.dense"""      )
 if "ff2" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""ff2"""							,    """output.dense"""      )
 if "ff" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""ff"""							,    """output.dense"""      )
 if "mlm_class" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""mlm.mlm_class"""							,    """cls.predictions.decoder"""      )
 if "mlm" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""mlm"""							,    """cls.predictions.transform"""      )
 if "cls" not in orig_key:
  UpperCAmelCase__					:  Dict					   =					"""yoso.""" + orig_key

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   UpperCAmelCase__					:  Dict					   =					val

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					convert_checkpoint_helper(config.max_position_embeddings							,    __UpperCamelCase      )

 print(model.load_state_dict(__UpperCamelCase      )      )
 model.eval()
 model.save_pretrained(__UpperCamelCase      )

 print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The json file for YOSO model config.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





