


"""simple docstring"""


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__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			TapasConfig.from_json_file(SCREAMING_SNAKE_CASE_   )
				# 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=SCREAMING_SNAKE_CASE_   )
				elif task == "WTQ":
								# run_task_main.py hparams
								_A					  =			4
								_A					  =			True
								# hparam_utils.py hparams
								_A					  =			0.664_694
								_A					  =			0.207_951
								_A					  =			0.121_194
								_A					  =			True
								_A					  =			True
								_A					  =			False
								_A					  =			0.0_352_513

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

								_A					  =			TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
				elif task == "TABFACT":
								_A					  =			TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE_   )
				elif task == "MLM":
								_A					  =			TapasForMaskedLM(config=SCREAMING_SNAKE_CASE_   )
				elif task == "INTERMEDIATE_PRETRAINING":
								_A					  =			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}"""   )
				_A					  =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"     ,			model_max_length=512   )
				tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_   )

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


if __name__ == "__main__":
			a_       							=    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."
			)
			a_       							=    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,
			)



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)