
import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__	:int										=					logging.get_logger(__name__)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		RobertaPreLayerNormConfig.from_pretrained(
					    lowerCAmelCase__				,    architectures=['''RobertaPreLayerNormForMaskedLM''']	)

					# convert state_dict
					lowercase					=		torch.load(hf_hub_download(repo_id=lowerCAmelCase__				,    filename='''pytorch_model.bin'''	)	)
					lowercase					=		{}
					for tensor_key, tensor_value in original_state_dict.items():
										# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
										if tensor_key.startswith('''roberta.'''	):
															lowercase					=		'''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''	) :]

										# The original implementation contains weights which are not used, remove them from the state_dict
										if tensor_key.endswith('''.self.LayerNorm.weight'''	) or tensor_key.endswith('''.self.LayerNorm.bias'''	):
															continue

										lowercase					=		tensor_value

					lowercase					=		RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=lowerCAmelCase__				,    config=lowerCAmelCase__				,    state_dict=lowerCAmelCase__	)
					model.save_pretrained(lowerCAmelCase__	)

					# convert tokenizer
					lowercase					=		AutoTokenizer.from_pretrained(lowerCAmelCase__	)
					tokenizer.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
							lowercase__	:str										=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowercase__	:Any										=					parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






from __future__ import annotations

from random import random







class        lowercase	:




							def __init__(       self     ,A__ = None):
												lowercase					=		value
												lowercase					=		random()
												lowercase					=		None
												lowercase					=		None




							def __repr__(       self):
												from pprint import pformat

												if self.left is None and self.right is None:
																	return f'\'{self.value}: {self.prior:.5}\''
												else:
																	return pformat(
																	    {f'{self.value}: {self.prior:.5}': (self.left, self.right)}     ,indent=1)





							def __str__(       self):
												lowercase					=		str(self.value) + ''' '''
												lowercase					=		str(self.left or '''''')
												lowercase					=		str(self.right or '''''')
												return value + left + right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					if root is None:  # None tree is split into 2 Nones
										return None, None
					elif root.value is None:
										return None, None
					else:
										if value < root.value:
															lowercase			,		lowercase					=		split(root.left				,    lowerCAmelCase__	)
															return left, root
										else:
															lowercase			,		lowercase					=		split(root.right				,    lowerCAmelCase__	)
															return root, right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					if (not left) or (not right):  # If one node is None, return the other
										return left or right
					elif left.prior < right.prior:
										lowercase					=		merge(left.right				,    lowerCAmelCase__	)
										return left
					else:
										lowercase					=		merge(lowerCAmelCase__				,    right.left	)
										return right



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		Node(lowerCAmelCase__	)
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    lowerCAmelCase__	)
					return merge(merge(lowerCAmelCase__				,    lowerCAmelCase__	)				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    value - 1	)
					lowercase			,		lowercase					=		split(lowerCAmelCase__				,    lowerCAmelCase__	)
					return merge(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if not root:  # None
										return
					else:
										inorder(root.left	)
										print(root.value				,    end=''','''	)
										inorder(root.right	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					for arg in args.split():
										if arg[0] == "+":
															lowercase					=		insert(lowerCAmelCase__				,    int(arg[1:]	)	)

										elif arg[0] == "-":
															lowercase					=		erase(lowerCAmelCase__				,    int(arg[1:]	)	)

										else:
															print('''Unknown command'''	)

					return root



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		None
					print(
					    '''enter numbers to create a tree, + value to add value into treap, '''
					    '''- value to erase all nodes with value. \'q\' to quit. '''	)

					lowercase					=		input()
					while args != "q":
										lowercase					=		interact_treap(lowerCAmelCase__				,    lowerCAmelCase__	)
										print(lowerCAmelCase__	)
										lowercase					=		input()

					print('''good by!'''	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




