



from __future__ import annotations

import math




class 			snake_case_						:






							def __init__(   self			,     a_      ):
									a_     :      int 						=     size
									# approximate the overall size of segment tree with given value
									a_     :      int 						=     [0 for i in range(0			,     4 * size      )]
									# create array to store lazy update
									a_     :      Dict 						=     [0 for i in range(0			,     4 * size      )]
									a_     :      Optional[Any] 						=     [0 for i in range(0			,     4 * size      )]  # flag for lazy update






							def snake_case_  (   self			,     a_      ):
									return idx * 2






							def snake_case_  (   self			,     a_      ):
									return idx * 2 + 1






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):

									if left_element == right_element:
											a_     :      int 						=     a[left_element - 1]
									else:
											a_     :      Union[str, Any] 						=     (left_element + right_element) // 2
											self.build(self.left(a_      )			,     a_			,     a_			,     a_      )
											self.build(self.right(a_      )			,     mid + 1			,     a_			,     a_      )
											a_     :      Any 						=     max(
											    self.segment_tree[self.left(a_      )]			,     self.segment_tree[self.right(a_      )]      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):

									if self.flag[idx] is True:
											a_     :      Any 						=     self.lazy[idx]
											a_     :      Any 						=     False
											if left_element != right_element:
													a_     :      Tuple 						=     self.lazy[idx]
													a_     :      Dict 						=     self.lazy[idx]
													a_     :      Tuple 						=     True
													a_     :      int 						=     True

									if right_element < a or left_element > b:
											return True
									if left_element >= a and right_element <= b:
											a_     :      str 						=     val
											if left_element != right_element:
													a_     :      Optional[int] 						=     val
													a_     :      Any 						=     val
													a_     :      int 						=     True
													a_     :      int 						=     True
											return True
									a_     :      Optional[int] 						=     (left_element + right_element) // 2
									self.update(self.left(a_      )			,     a_			,     a_			,     a_			,     a_			,     a_      )
									self.update(self.right(a_      )			,     mid + 1			,     a_			,     a_			,     a_			,     a_      )
									a_     :      List[Any] 						=     max(
									    self.segment_tree[self.left(a_      )]			,     self.segment_tree[self.right(a_      )]      )
									return True






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_      ):

									if self.flag[idx] is True:
											a_     :      Union[str, Any] 						=     self.lazy[idx]
											a_     :      int 						=     False
											if left_element != right_element:
													a_     :      List[str] 						=     self.lazy[idx]
													a_     :      Tuple 						=     self.lazy[idx]
													a_     :      Dict 						=     True
													a_     :      Tuple 						=     True
									if right_element < a or left_element > b:
											return -math.inf
									if left_element >= a and right_element <= b:
											return self.segment_tree[idx]
									a_     :      Any 						=     (left_element + right_element) // 2
									a_     :      int 						=     self.query(self.left(a_      )			,     a_			,     a_			,     a_			,     a_      )
									a_     :      str 						=     self.query(self.right(a_      )			,     mid + 1			,     a_			,     a_			,     a_      )
									return max(a_			,     a_      )






							def __str__(   self      ):
									return str([self.query(1			,     1			,     self.size			,     a_			,     a_      ) for i in range(1			,     self.size + 1      )]      )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
					SCREAMING_SNAKE_CASE_				= 15
					SCREAMING_SNAKE_CASE_				= SegmentTree(size)
					segt.build(1, 1, size, A)
					print(segt.query(1, 1, size, 4, 6))
					print(segt.query(1, 1, size, 7, 11))
					print(segt.query(1, 1, size, 7, 12))
					segt.update(1, 1, size, 1, 3, 1_11)
					print(segt.query(1, 1, size, 1, 15))
					segt.update(1, 1, size, 7, 8, 2_35)
					print(segt)




"""simple docstring"""
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_				= {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     finetuning_task.lower() if finetuning_task is not None else ""
		if finetuning_task in GLUE_TASKS_NUM_LABELS:
				print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		a_     :      Tuple 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		print(F"""Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)
		print(F"""Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--xlnet_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained XLNet model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
					)
					parser.add_argument(
					    """--finetuning_task""",
					    default=None,
					    type=str,
					    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					print(args)

					convert_xlnet_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
					)