"""simple docstring"""





from __future__ import annotations

from typing import Any




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Any      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_of_nodes
											_SCREAMING_SNAKE_CASE		:						list[list[int]]			  =    []
											_SCREAMING_SNAKE_CASE		:						dict[int, int]			  =    {}





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							int      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											self.m_edges.append([u_node, v_node, weight])





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							int):



											"""simple docstring"""







											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							int):



											"""simple docstring"""







											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.find_component(UpperCamelCase__)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							list[int]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if component_size[u_node] <= component_size[v_node]:
																		_SCREAMING_SNAKE_CASE		:						int			  =    v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(UpperCamelCase__)

											elif component_size[u_node] >= component_size[v_node]:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.find_component(UpperCamelCase__)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(UpperCamelCase__)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
											_SCREAMING_SNAKE_CASE		:						int			  =    0

											_SCREAMING_SNAKE_CASE		:						list[Any]			  =    [-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									_SCREAMING_SNAKE_CASE		:						int			  =    edge

																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.m_component[u]
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(UpperCamelCase__      ,    UpperCamelCase__):
																																_SCREAMING_SNAKE_CASE		:						str			  =    edge

																																_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.m_component[u]
																																_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(UpperCamelCase__      ,    UpperCamelCase__      ,    UpperCamelCase__)
																																							print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [-1] * self.m_num_of_nodes
											print(f"""The total weight of the minimal spanning tree is: {mst_weight}""")



def      lowerCamelCase_()->    List[Any]:
							pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()



"""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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[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
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    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}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    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__":
			lowerCAmelCase_     =	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_     =	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,
			)


