






"""simple docstring"""


import math


class    snake_case						:


   '''simple docstring'''

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

     '''simple docstring'''
     __A		     =					len(_lowerCamelCase    )
     __A		     =					[0] * (
         4 * self.N
     )  # approximate the overall size of segment tree with array N
     self.build(1,			0,			self.N - 1    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     return idx * 2


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     return idx * 2 + 1


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

     '''simple docstring'''
     if l == r:
       __A		     =					A[l]
     else:
       __A		     =					(l + r) // 2
       self.build(self.left(_lowerCamelCase    ),			_lowerCamelCase,			_lowerCamelCase    )
       self.build(self.right(_lowerCamelCase    ),			mid + 1,			_lowerCamelCase    )
       __A		     =					max(self.st[self.left(_lowerCamelCase    )],			self.st[self.right(_lowerCamelCase    )]    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	int,			_lowerCamelCase					:	str    ):

     '''simple docstring'''
     return self.update_recursive(1,			0,			self.N - 1,			a - 1,			b - 1,			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	int,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	int    ):  # noqa: E741

     '''simple docstring'''
     if r < a or l > b:
       return True
     if l == r:
       __A		     =					val
       return True
     __A		     =					(l + r) // 2
     self.update_recursive(self.left(_lowerCamelCase    ),			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )
     self.update_recursive(self.right(_lowerCamelCase    ),			mid + 1,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )
     __A		     =					max(self.st[self.left(_lowerCamelCase    )],			self.st[self.right(_lowerCamelCase    )]    )
     return True


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     return self.query_recursive(1,			0,			self.N - 1,			a - 1,			b - 1    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Union[str, Any]    ):  # noqa: E741

     '''simple docstring'''
     if r < a or l > b:
       return -math.inf
     if l >= a and r <= b:
       return self.st[idx]
     __A		     =					(l + r) // 2
     __A		     =					self.query_recursive(self.left(_lowerCamelCase    ),			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )
     __A		     =					self.query_recursive(self.right(_lowerCamelCase    ),			mid + 1,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )
     return max(_lowerCamelCase,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					[]
     for i in range(1,			N + 1    ):
       show_list += [self.query(_lowerCamelCase,			_lowerCamelCase    )]
     print(_lowerCamelCase    )


if __name__ == "__main__":
       lowercase_								=	[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
       lowercase_								=	15
       lowercase_								=	SegmentTree(A)
       print(segt.query(4, 6))
       print(segt.query(7, 11))
       print(segt.query(7, 12))
       segt.update(1, 3, 111)
       print(segt.query(1, 15))
       segt.update(7, 8, 235)
       segt.show_data()












"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




