





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  :Any       ,	__a  :str       ,	__a  :Any       ,	__a  :Optional[Any]       ,	__a  :Tuple       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  TapasConfig.from_json_file(__a       )
		# 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=__a       )
		elif task == "WTQ":
				# run_task_main.py hparams
				A__     =  4
				A__     =  True
				# hparam_utils.py hparams
				A__     =  0.664694
				A__     =  0.207951
				A__     =  0.121194
				A__     =  True
				A__     =  True
				A__     =  False
				A__     =  0.0352513

				A__     =  TapasForQuestionAnswering(config=__a       )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				A__     =  4
				A__     =  False
				# hparam_utils.py hparams
				A__     =  36.4519
				A__     =  0.903421
				A__     =  222.088
				A__     =  True
				A__     =  True
				A__     =  True
				A__     =  0.763141

				A__     =  TapasForQuestionAnswering(config=__a       )
		elif task == "TABFACT":
				A__     =  TapasForSequenceClassification(config=__a       )
		elif task == "MLM":
				A__     =  TapasForMaskedLM(config=__a       )
		elif task == "INTERMEDIATE_PRETRAINING":
				A__     =  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}'       )
		A__     =  TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""       ,	model_max_length=5_1_2       )
		tokenizer.save_pretrained(__a       )

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


if __name__ == "__main__":
					A       :   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.'''
					)
					A       :   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,
					)










def   __lowerCamelCase   (				__a  :Dict       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  []
		A__     =  []
		A__     =  {
		    """^""": 3,
		    """*""": 2,
		    """/""": 2,
		    """%""": 2,
		    """+""": 1,
		    """-""": 1,
		}  # Priority of each operator
		A__     =  len(__a       ) if (len(__a       ) > 7) else 7

		# Print table header for output
		print(
		    """Symbol""".center(8       )       ,	"""Stack""".center(__a       )       ,	"""Postfix""".center(__a       )       ,	sep=""" | """       ,	)
		print("""-""" * (print_width * 3 + 7)       )

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(__a       )  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(__a       )  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()       )  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(__a       ) == 0:
								stack.append(__a       )  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(__a       ) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()       )  # pop stack & add to Postfix
								stack.append(__a       )  # push x to stack

				print(
				    x.center(8       )       ,	("""""".join(__a       )).ljust(__a       )       ,	("""""".join(__a       )).ljust(__a       )       ,	sep=""" | """       ,	)  # Output in tabular format

		while len(__a       ) > 0:  # while stack is not empty
				post_fix.append(stack.pop()       )  # pop stack & add to Postfix
				print(
				    """ """.center(8       )       ,	("""""".join(__a       )).ljust(__a       )       ,	("""""".join(__a       )).ljust(__a       )       ,	sep=""" | """       ,	)  # Output in tabular format

		return "".join(__a       )  # return Postfix as str







def   __lowerCamelCase   (				__a  :List[str]       )						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  list(infix[::-1]       )  # reverse the infix equation

		for i in range(len(__a       )       ):
				if infix[i] == "(":
						A__     =  """)"""  # change "(" to ")"
				elif infix[i] == ")":
						A__     =  """("""  # change ")" to "("

		return (infix_2_postfix("""""".join(__a       )       ))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
					A       :   Union[str, Any]					= input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
					A       :   Dict					= ''''''.join(Infix.split())  # Remove spaces from the input
					print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



