


import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						Any    ,     __a				:						Dict    ,     __a				:						Union[str, Any]     ):
							a__      =TapasConfig.from_json_file(UpperCAmelCase_     )
							# set absolute/relative position embeddings parameter
							a__      =reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														a__      =TapasForQuestionAnswering(config=UpperCAmelCase_     )
							elif task == "WTQ":
														# run_task_main.py hparams
														a__      =4
														a__      =True
														# hparam_utils.py hparams
														a__      =0.66_46_94
														a__      =0.20_79_51
														a__      =0.12_11_94
														a__      =True
														a__      =True
														a__      =False
														a__      =0.0_35_25_13

														a__      =TapasForQuestionAnswering(config=UpperCAmelCase_     )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														a__      =4
														a__      =False
														# hparam_utils.py hparams
														a__      =36.45_19
														a__      =0.90_34_21
														a__      =2_22.0_88
														a__      =True
														a__      =True
														a__      =True
														a__      =0.76_31_41

														a__      =TapasForQuestionAnswering(config=UpperCAmelCase_     )
							elif task == "TABFACT":
														a__      =TapasForSequenceClassification(config=UpperCAmelCase_     )
							elif task == "MLM":
														a__      =TapasForMaskedLM(config=UpperCAmelCase_     )
							elif task == "INTERMEDIATE_PRETRAINING":
														a__      =TapasModel(config=UpperCAmelCase_     )
							else:
														raise ValueError(f"""Task {task} not supported."""     )

							print(f"""Building PyTorch model from configuration: {config}"""     )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_     )

							# Save pytorch-model (weights and configuration)
							print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
							model.save_pretrained(UpperCAmelCase_     )

							# Save tokenizer files
							print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
							a__      =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'    ,     model_max_length=512     )
							tokenizer.save_pretrained(UpperCAmelCase_     )

							print('Used relative position embeddings:'    ,     model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
	_lowerCAmelCase:						str   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
	)
	parser.add_argument(
	    '--reset_position_index_per_cell',
	    default=False,
	    action='store_true',
	    help='Whether to use relative position embeddings or not. Defaults to True.',
	)
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--tapas_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained TAPAS 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.'
	)
	_lowerCAmelCase:						Any   				=       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)





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


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Dict										=	{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'visual_bert'




 def __init__(						self   ,    _lowerCamelCase=3_0522   ,    _lowerCamelCase=768   ,    _lowerCamelCase=512   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-12   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )

       a		:Any									=			vocab_size
       a		:str									=			max_position_embeddings
       a		:str									=			hidden_size
       a		:List[Any]									=			visual_embedding_dim
       a		:str									=			num_hidden_layers
       a		:Optional[int]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:Union[str, Any]									=			hidden_dropout_prob
       a		:List[Any]									=			attention_probs_dropout_prob
       a		:int									=			initializer_range
       a		:List[Any]									=			type_vocab_size
       a		:str									=			layer_norm_eps
       a		:Optional[int]									=			bypass_transformer
       a		:str									=			special_visual_initialize





