



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


__snake_case						:int      		=     logging.get_logger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						['''input_values''', '''padding_mask''']



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int = 1     ,		__SCREAMING_SNAKE_CASE							:     int = 24_000     ,		__SCREAMING_SNAKE_CASE							:     float = 0.0     ,		__SCREAMING_SNAKE_CASE							:     float = None     ,		__SCREAMING_SNAKE_CASE							:     float = None     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           super().__init__(feature_size=__SCREAMING_SNAKE_CASE     ,		sampling_rate=__SCREAMING_SNAKE_CASE     ,		padding_value=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				chunk_length_s
           __a						       =				overlap



       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase			(		self							:     Dict):


           '''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							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[bool, str, PaddingStrategy]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, TensorType]] = None     ,		__SCREAMING_SNAKE_CASE							:     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
               __a						       =				True

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

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

           # always return batch
           if not is_batched:
               __a						       =				[np.asarray(__SCREAMING_SNAKE_CASE).T]

           # verify inputs are valid
           for idx, example in enumerate(__SCREAMING_SNAKE_CASE):
               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')

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

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

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

           __a						       =				input_values
           if return_tensors is not None:
               __a						       =				padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE)

           return padded_inputs










import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Tuple      		=     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(
     '''--big_bird_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained BERT 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.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





