



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__              =							logging.get_logger(__name__)

lowerCAmelCase__              =							{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}






class 			__snake_case			(      _lowercase):
  snake_case__							:  Optional[Any]         =		"camembert"




  def __init__(					self	:		Optional[Any]		,   __lowerCAmelCase	:		Any=3_0_5_2_2		,   __lowerCAmelCase	:		List[str]=7_6_8		,   __lowerCAmelCase	:		List[str]=1_2		,   __lowerCAmelCase	:		Optional[int]=1_2		,   __lowerCAmelCase	:		List[Any]=3_0_7_2		,   __lowerCAmelCase	:		Union[str, Any]="gelu"		,   __lowerCAmelCase	:		Union[str, Any]=0.1		,   __lowerCAmelCase	:		Optional[int]=0.1		,   __lowerCAmelCase	:		Optional[int]=5_1_2		,   __lowerCAmelCase	:		str=2		,   __lowerCAmelCase	:		int=0.02		,   __lowerCAmelCase	:		List[Any]=1E-12		,   __lowerCAmelCase	:		Union[str, Any]=1		,   __lowerCAmelCase	:		Optional[Any]=0		,   __lowerCAmelCase	:		List[Any]=2		,   __lowerCAmelCase	:		str="absolute"		,   __lowerCAmelCase	:		Any=True		,   __lowerCAmelCase	:		Optional[int]=None		,   **__lowerCAmelCase	:		Optional[int]		,   ):






         """simple docstring"""






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

         _lowerCamelCase		:							Tuple            =					vocab_size
         _lowerCamelCase		:							str            =					hidden_size
         _lowerCamelCase		:							Union[str, Any]            =					num_hidden_layers
         _lowerCamelCase		:							Any            =					num_attention_heads
         _lowerCamelCase		:							Optional[Any]            =					hidden_act
         _lowerCamelCase		:							List[str]            =					intermediate_size
         _lowerCamelCase		:							Optional[Any]            =					hidden_dropout_prob
         _lowerCamelCase		:							List[Any]            =					attention_probs_dropout_prob
         _lowerCamelCase		:							Optional[Any]            =					max_position_embeddings
         _lowerCamelCase		:							Tuple            =					type_vocab_size
         _lowerCamelCase		:							Tuple            =					initializer_range
         _lowerCamelCase		:							Dict            =					layer_norm_eps
         _lowerCamelCase		:							List[Any]            =					position_embedding_type
         _lowerCamelCase		:							int            =					use_cache
         _lowerCamelCase		:							List[str]            =					classifier_dropout






class 			__snake_case			(      _lowercase):




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






         """simple docstring"""






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







'''simple docstring'''

import argparse

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


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				BigBirdForPreTraining(lowerCAmelCase_    )

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

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


if __name__ == "__main__":
							__lowerCAmelCase		     =  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.'''
							)
							__lowerCAmelCase		     =  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
							)




