






"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

UpperCAmelCase_     :				Tuple         =				TypeVar("""T""")




class 							lowerCAmelCase__       (    Generic[T]				):






      '''simple docstring'''

      def __init__(		self  :						Optional[Any]		,			lowercase_  :						list[T]		,			lowercase_  :						Callable[[T, T], T]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any | T							=	None

          SCREAMING_SNAKE_CASE_   :						int							=	len(lowercase_)
          SCREAMING_SNAKE_CASE_   :						list[T]							=	[any_type for _ in range(self.N)] + arr
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	fnc
          self.build()
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          for p in range(self.N - 1		,			0		,			-1):
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.fn(self.st[p * 2]		,			self.st[p * 2 + 1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						int		,			lowercase_  :						T):




          '''simple docstring'''


          p += self.N
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	v
          while p > 1:
              SCREAMING_SNAKE_CASE_   :						Dict							=	p // 2
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.fn(self.st[p * 2]		,			self.st[p * 2 + 1])



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						int		,			lowercase_  :						int):  # noqa: E741




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Dict							=	l + self.N, r + self.N

          SCREAMING_SNAKE_CASE_   :						T | None							=	None
          while l <= r:
              if l % 2 == 1:
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.st[l] if res is None else self.fn(lowercase_		,			self.st[l])
              if r % 2 == 0:
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.st[r] if res is None else self.fn(lowercase_		,			self.st[r])
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Any							=	(l + 1) // 2, (r - 1) // 2
          return res


if __name__ == "__main__":
  from functools import reduce

  UpperCAmelCase_     :				Optional[Any]         =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  UpperCAmelCase_     :				Dict         =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  UpperCAmelCase_     :				int         =				SegmentTree(test_array, min)
  UpperCAmelCase_     :				List[str]         =				SegmentTree(test_array, max)
  UpperCAmelCase_     :				str         =				SegmentTree(test_array, lambda a, b: a + b)



  def       _A    ()				-> None:
      """simple docstring"""






      for i in range(len(__a							)							):
          for j in range(__a					,       len(__a							)							):
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	reduce(__a					,       test_array[i : j + 1]							)
              SCREAMING_SNAKE_CASE_   :						List[str]							=	reduce(__a					,       test_array[i : j + 1]							)
              SCREAMING_SNAKE_CASE_   :						int							=	reduce(lambda __a					,       __a							: a + b					,       test_array[i : j + 1]							)
              assert min_range == min_segment_tree.query(__a					,       __a							)
              assert max_range == max_segment_tree.query(__a					,       __a							)
              assert sum_range == sum_segment_tree.query(__a					,       __a							)

  test_all_segments()

  for index, value in test_updates.items():
    UpperCAmelCase_     :				str         =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()










"""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


UpperCAmelCase_     :				int         =				{
    """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       _A    (__a					,       __a					,       __a					,       __a=None							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	XLNetConfig.from_json_file(__a							)

    SCREAMING_SNAKE_CASE_   :						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}'							)
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	GLUE_TASKS_NUM_LABELS[finetuning_task]
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForSequenceClassification(__a							)
    elif "squad" in finetuning_task:
        SCREAMING_SNAKE_CASE_   :						str							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForQuestionAnswering(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	XLNetLMHeadModel(__a							)

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

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	os.path.join(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(__a					,       __a							)
    print(f'Save PyTorch model to {os.path.abspath(__a							)}'							)
    torch.save(model.state_dict()					,       __a							)
    print(f'Save configuration file to {os.path.abspath(__a							)}'							)
    with open(__a					,       '''w'''					,       encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_     :				str         =				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""",
  )
  UpperCAmelCase_     :				int         =				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
  )


