






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      lowerCAmelCase__	(    _a       :			List[Any]  ,      _a       :			Optional[Any]  ,      _a       :			Optional[int]  ,      _a       :			Optional[int]  ,      _a       :			Any    ):
		# Initialise PyTorch model.
		# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
		# TapasConfig to False.

		# initialize configuration from json file
		snake_case_				:							str						 =       TapasConfig.from_json_file(_a    )
		# set absolute/relative position embeddings parameter
		snake_case_				:							int						 =       reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				snake_case_				:							int						 =       TapasForQuestionAnswering(config=_a    )
		elif task == "WTQ":
				# run_task_main.py hparams
				snake_case_				:							Tuple						 =       4
				snake_case_				:							Dict						 =       True
				# hparam_utils.py hparams
				snake_case_				:							int						 =       0.664_694
				snake_case_				:							Dict						 =       0.207_951
				snake_case_				:							Optional[Any]						 =       0.121_194
				snake_case_				:							Dict						 =       True
				snake_case_				:							Dict						 =       True
				snake_case_				:							Any						 =       False
				snake_case_				:							List[Any]						 =       0.0_352_513

				snake_case_				:							Dict						 =       TapasForQuestionAnswering(config=_a    )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				snake_case_				:							Any						 =       4
				snake_case_				:							Dict						 =       False
				# hparam_utils.py hparams
				snake_case_				:							Optional[int]						 =       36.4_519
				snake_case_				:							List[Any]						 =       0.903_421
				snake_case_				:							Union[str, Any]						 =       222.088
				snake_case_				:							Any						 =       True
				snake_case_				:							Optional[int]						 =       True
				snake_case_				:							List[Any]						 =       True
				snake_case_				:							Dict						 =       0.763_141

				snake_case_				:							Tuple						 =       TapasForQuestionAnswering(config=_a    )
		elif task == "TABFACT":
				snake_case_				:							str						 =       TapasForSequenceClassification(config=_a    )
		elif task == "MLM":
				snake_case_				:							Dict						 =       TapasForMaskedLM(config=_a    )
		elif task == "INTERMEDIATE_PRETRAINING":
				snake_case_				:							Tuple						 =       TapasModel(config=_a    )
		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(_a  ,      _a  ,      _a    )

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

		# Save tokenizer files
		print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
		snake_case_				:							Dict						 =       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,      model_max_length=5_12    )
		tokenizer.save_pretrained(_a    )

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


if __name__ == "__main__":
							lowercase  :							Any			  =    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.'''
							)
							lowercase  :							Union[str, 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
