
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()
lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	str	,		__a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    RobertaPreLayerNormConfig.from_pretrained(
         __a	,		architectures=["""RobertaPreLayerNormForMaskedLM"""]						)

     # convert state_dict
     UpperCamelCase__				=    torch.load(hf_hub_download(repo_id=__a	,		filename="""pytorch_model.bin"""						)						)
     UpperCamelCase__				=    {}
     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."""						):
               UpperCamelCase__				=    """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

          UpperCamelCase__				=    tensor_value

     UpperCamelCase__				=    RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__a	,		config=__a	,		state_dict=__a						)
     model.save_pretrained(__a						)

     # convert tokenizer
     UpperCamelCase__				=    AutoTokenizer.from_pretrained(__a						)
     tokenizer.save_pretrained(__a						)


if __name__ == "__main__":
    lowerCamelCase_						     =					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.'''
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)







from __future__ import annotations

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

       for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                 return dest
       return ""






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(__a	,		__a	,		__a						)

     UpperCamelCase__				=    Counter(graph.get_nodes()						)
     UpperCamelCase__				=    start

     for _ in range(__a						):
          UpperCamelCase__				=    graph.transition(__a						)
          visited[node] += 1

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





