



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case						:int      		=     logging.get_logger(__name__)

__snake_case						:Tuple      		=     {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						'''data2vec-text'''



       def __init__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=30_522     ,		__SCREAMING_SNAKE_CASE							:     Dict=768     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=12     ,		__SCREAMING_SNAKE_CASE							:     str=3_072     ,		__SCREAMING_SNAKE_CASE							:     Any="gelu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=0.1     ,		__SCREAMING_SNAKE_CASE							:     Tuple=512     ,		__SCREAMING_SNAKE_CASE							:     Dict=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.02     ,		__SCREAMING_SNAKE_CASE							:     int=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=1     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0     ,		__SCREAMING_SNAKE_CASE							:     Any=2     ,		__SCREAMING_SNAKE_CASE							:     Dict="absolute"     ,		__SCREAMING_SNAKE_CASE							:     Dict=True     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		bos_token_id=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				position_embedding_type
           __a						       =				use_cache
           __a						       =				classifier_dropout


class     _A       (	__UpperCAmelCase    ):



       @property
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           if self.task == "multiple-choice":
               __a						       =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
               __a						       =				{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
               ])










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
 )





