
"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


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

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

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

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       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}''' )
							__snake_case     			=       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__":
		_SCREAMING_SNAKE_CASE	       =      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."""
		)
		_SCREAMING_SNAKE_CASE	       =      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 __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
