



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 		UpperCAmelCase						(						__UpperCAmelCase				):
   SCREAMING_SNAKE_CASE__							=     ["input_values", "padding_mask"]





   def __init__(	self       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = 24_000       ,     _lowerCAmelCase = 0.0       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         super().__init__(feature_size=_lowerCamelCase       ,     sampling_rate=_lowerCamelCase       ,     padding_value=_lowerCamelCase       ,     **_lowerCamelCase			)
         _lowerCAmelCase 			=  chunk_length_s
         _lowerCAmelCase 			=  overlap





   @property
   def 	__lowerCAmelCase							(	self			):
         if self.chunk_length_s is None:
               return None
         else:
               return int(self.chunk_length_s * self.sampling_rate			)





   @property
   def 	__lowerCAmelCase							(	self			):
         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       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     ):
         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
               _lowerCAmelCase 			=  True

         _lowerCAmelCase 			=  bool(
             isinstance(_lowerCamelCase       ,     (list, tuple)			) and (isinstance(raw_audio[0]       ,     (np.ndarray, tuple, list)			))			)

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

         # always return batch
         if not is_batched:
               _lowerCAmelCase 			=  [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'''			)

         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  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:
                     _lowerCAmelCase 			=  min(array.shape[0] for array in raw_audio			)
                     _lowerCAmelCase 			=  int(np.floor(max_length / self.chunk_stride			)			)
                     _lowerCAmelCase 			=  (nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                     _lowerCAmelCase 			=  max(array.shape[0] for array in raw_audio			)
                     _lowerCAmelCase 			=  int(np.ceil(max_length / self.chunk_stride			)			)
                     _lowerCAmelCase 			=  (nb_step - 1) * self.chunk_stride + self.chunk_length
                     _lowerCAmelCase 			=  '''max_length'''
               else:
                     _lowerCAmelCase 			=  input_values

        # normal padding on batch
         if padded_inputs is None:
               _lowerCAmelCase 			=  self.pad(
                   _lowerCamelCase       ,     max_length=_lowerCamelCase       ,     truncation=_lowerCamelCase       ,     padding=_lowerCamelCase       ,     return_attention_mask=_lowerCamelCase       ,     )
               if padding:
                     _lowerCAmelCase 			=  padded_inputs.pop('''attention_mask'''			)

         _lowerCAmelCase 			=  []
         for example in padded_inputs.pop('''input_values'''			):
               if self.feature_size == 1:
                     _lowerCAmelCase 			=  example[..., None]
               input_values.append(example.T			)

         _lowerCAmelCase 			=  input_values
         if return_tensors is not None:
               _lowerCAmelCase 			=  padded_inputs.convert_to_tensors(_lowerCamelCase			)

         return padded_inputs




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()     ,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--albert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ALBERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)