


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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= TapasConfig.from_json_file(SCREAMING_SNAKE_CASE	)
		# set absolute/relative position embeddings parameter
		lowercase__				= reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowercase__				= TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase__				= 4
				lowercase__				= True
				# hparam_utils.py hparams
				lowercase__				= 0.664_694
				lowercase__				= 0.207_951
				lowercase__				= 0.121_194
				lowercase__				= True
				lowercase__				= True
				lowercase__				= False
				lowercase__				= 0.0_352_513

				lowercase__				= TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase__				= 4
				lowercase__				= False
				# hparam_utils.py hparams
				lowercase__				= 36.4_519
				lowercase__				= 0.903_421
				lowercase__				= 222.088
				lowercase__				= True
				lowercase__				= True
				lowercase__				= True
				lowercase__				= 0.763_141

				lowercase__				= TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE	)
		elif task == "TABFACT":
				lowercase__				= TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE	)
		elif task == "MLM":
				lowercase__				= TapasForMaskedLM(config=SCREAMING_SNAKE_CASE	)
		elif task == "INTERMEDIATE_PRETRAINING":
				lowercase__				= TapasModel(config=SCREAMING_SNAKE_CASE	)
		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(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

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

		# Save tokenizer files
		print(f'Save tokenizer files to {pytorch_dump_path}'	)
		lowercase__				= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''		,							model_max_length=5_12	)
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
			lowerCAmelCase			=				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			=				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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



