
from collections import OrderedDict
from typing import Mapping

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


snake_case__					=				logging.get_logger(__name__)

snake_case__					=				{
    """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
    """distilbert-base-uncased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
    """distilbert-base-cased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
    """distilbert-base-multilingual-cased""": (
        """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
    ),
    """distilbert-base-uncased-finetuned-sst-2-english""": (
        """https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
    ),
}







class 				UpperCAmelCase						( _UpperCamelCase       ):
					a__:			int      	=					'distilbert'
					a__:			str      	=					{
					    'hidden_size': 'dim',
					    'num_attention_heads': 'n_heads',
					    'num_hidden_layers': 'n_layers',
					}

					def __init__(      self   :       List[Any]			,   lowerCAmelCase   :       str=3_0522			,   lowerCAmelCase   :       Optional[Any]=512			,   lowerCAmelCase   :       Optional[Any]=False			,   lowerCAmelCase   :       Union[str, Any]=6			,   lowerCAmelCase   :       Optional[int]=12			,   lowerCAmelCase   :       Optional[int]=768			,   lowerCAmelCase   :       Optional[int]=4 * 768			,   lowerCAmelCase   :       Optional[Any]=0.1			,   lowerCAmelCase   :       List[str]=0.1			,   lowerCAmelCase   :       Optional[int]="gelu"			,   lowerCAmelCase   :       List[Any]=0.02			,   lowerCAmelCase   :       Optional[Any]=0.1			,   lowerCAmelCase   :       Optional[Any]=0.2			,   lowerCAmelCase   :       int=0			,   **lowerCAmelCase   :       int			,   ):
											lowercase							:     str									=	vocab_size
											lowercase							:     List[str]									=	max_position_embeddings
											lowercase							:     Dict									=	sinusoidal_pos_embds
											lowercase							:     Optional[int]									=	n_layers
											lowercase							:     Tuple									=	n_heads
											lowercase							:     Union[str, Any]									=	dim
											lowercase							:     Optional[Any]									=	hidden_dim
											lowercase							:     Dict									=	dropout
											lowercase							:     Optional[int]									=	attention_dropout
											lowercase							:     int									=	activation
											lowercase							:     Dict									=	initializer_range
											lowercase							:     Optional[Any]									=	qa_dropout
											lowercase							:     List[str]									=	seq_classif_dropout
											super().__init__(**_UpperCAmelCase			,   pad_token_id=_UpperCAmelCase       )







class 				UpperCAmelCase						( _UpperCamelCase       ):

					@property
					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											if self.task == "multiple-choice":
																	lowercase							:     Dict									=	{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																	lowercase							:     Dict									=	{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
							)




