'''simple docstring'''

from ....configuration_utils import PretrainedConfig
from ....utils import logging


_A			:      Union[str, Any]					      =				logging.get_logger(__name__)

_A			:      Optional[int]					      =				{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}







class 				_lowercase     (				__UpperCAmelCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					'''van'''
 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=2_24      ,       SCREAMING_SNAKE_CASE__   :	List[str]=3      ,       SCREAMING_SNAKE_CASE__   :	Any=[7, 3, 3, 3]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=[4, 2, 2, 2]      ,       SCREAMING_SNAKE_CASE__   :	Tuple=[64, 1_28, 3_20, 5_12]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=[3, 3, 12, 3]      ,       SCREAMING_SNAKE_CASE__   :	Dict=[8, 8, 4, 4]      ,       SCREAMING_SNAKE_CASE__   :	Any="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=1e-2      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0.0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0.0      ,       **SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    int:
  super().__init__(**__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						image_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						patch_sizes
  __lowerCAmelCase											=						strides
  __lowerCAmelCase											=						hidden_sizes
  __lowerCAmelCase											=						depths
  __lowerCAmelCase											=						mlp_ratios
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						initializer_range
  __lowerCAmelCase											=						layer_norm_eps
  __lowerCAmelCase											=						layer_scale_init_value
  __lowerCAmelCase											=						drop_path_rate
  __lowerCAmelCase											=						dropout_rate











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
 )





