




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       if "model" in orig_key:
              A__							=   orig_key.replace('''model.'''			,    ''''''						)
       if "norm1" in orig_key:
              A__							=   orig_key.replace('''norm1'''			,    '''attention.output.LayerNorm'''						)
       if "norm2" in orig_key:
              A__							=   orig_key.replace('''norm2'''			,    '''output.LayerNorm'''						)
       if "norm" in orig_key:
              A__							=   orig_key.replace('''norm'''			,    '''LayerNorm'''						)
       if "transformer" in orig_key:
              A__							=   orig_key.split('''.'''						)[0].split('''_'''						)[-1]
              A__							=   orig_key.replace(f"""transformer_{layer_num}"""			,    f"""encoder.layer.{layer_num}"""						)
       if "mha.attn" in orig_key:
              A__							=   orig_key.replace('''mha.attn'''			,    '''attention.self'''						)
       if "mha" in orig_key:
              A__							=   orig_key.replace('''mha'''			,    '''attention'''						)
       if "W_q" in orig_key:
              A__							=   orig_key.replace('''W_q'''			,    '''self.query'''						)
       if "W_k" in orig_key:
              A__							=   orig_key.replace('''W_k'''			,    '''self.key'''						)
       if "W_v" in orig_key:
              A__							=   orig_key.replace('''W_v'''			,    '''self.value'''						)
       if "ff1" in orig_key:
              A__							=   orig_key.replace('''ff1'''			,    '''intermediate.dense'''						)
       if "ff2" in orig_key:
              A__							=   orig_key.replace('''ff2'''			,    '''output.dense'''						)
       if "ff" in orig_key:
              A__							=   orig_key.replace('''ff'''			,    '''output.dense'''						)
       if "mlm_class" in orig_key:
              A__							=   orig_key.replace('''mlm.mlm_class'''			,    '''cls.predictions.decoder'''						)
       if "mlm" in orig_key:
              A__							=   orig_key.replace('''mlm'''			,    '''cls.predictions.transform'''						)
       if "cls" not in orig_key:
              A__							=   '''yoso.''' + orig_key

       return orig_key







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     A__							=   val

       A__							=   orig_state_dict['''cls.predictions.decoder.bias''']
       A__							=   torch.arange(lowercase_						).expand((1, -1)						) + 2

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   torch.load(lowercase_			,    map_location='''cpu'''						)['''model_state_dict''']
       A__							=   YosoConfig.from_json_file(lowercase_						)
       A__							=   YosoForMaskedLM(lowercase_						)

       A__							=   convert_checkpoint_helper(config.max_position_embeddings			,    lowercase_						)

       print(model.load_state_dict(lowercase_						)						)
       model.eval()
       model.save_pretrained(lowercase_						)

       print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
  _lowerCamelCase     :		int								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for YOSO model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _lowerCamelCase     :		List[Any]								=       parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

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

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

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

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


