





'''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    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]     )   ->    int:
			# 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
			a__     = TapasConfig.from_json_file(__lowerCamelCase     )
			# 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=__lowerCamelCase     )
			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=__lowerCamelCase     )
			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=__lowerCamelCase     )
			elif task == "TABFACT":
						a__     = TapasForSequenceClassification(config=__lowerCamelCase     )
			elif task == "MLM":
						a__     = TapasForMaskedLM(config=__lowerCamelCase     )
			elif task == "INTERMEDIATE_PRETRAINING":
						a__     = TapasModel(config=__lowerCamelCase     )
			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(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

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

			# 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(__lowerCamelCase     )

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


if __name__ == "__main__":
		lowerCAmelCase_   :   List[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."
		)
		lowerCAmelCase_   :   Tuple       =					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'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

lowerCAmelCase_   :   str       =					ort.SessionOptions()
lowerCAmelCase_   :   Tuple       =					ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCAmelCase_   :   Any       =					["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCAmelCase_   :   str       =					ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCAmelCase_   :   Tuple       =					ort.RunOptions()

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCAmelCase_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))



