


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



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

   return orig_key



def     __lowerCAmelCase						(							snake_case	:    Optional[Any]						,      snake_case	:    int )       ->							int:
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase:				Optional[int]     =	orig_state_dict.pop(snake_case )

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         __lowerCamelCase:				List[Any]     =	val

   __lowerCamelCase:				Any     =	orig_state_dict["""cls.predictions.decoder.bias"""]
   __lowerCamelCase:				Dict     =	torch.arange(snake_case ).expand((1, -1) ) + 2

   return orig_state_dict



def     __lowerCAmelCase						(							snake_case	:    Union[str, Any]						,      snake_case	:    List[Any]						,      snake_case	:    int )       ->							List[Any]:
   __lowerCamelCase:				List[Any]     =	torch.load(snake_case						,      map_location="""cpu""" )["""model_state_dict"""]
   __lowerCamelCase:				List[Any]     =	YosoConfig.from_json_file(snake_case )
   __lowerCamelCase:				Dict     =	YosoForMaskedLM(snake_case )

   __lowerCamelCase:				int     =	convert_checkpoint_helper(config.max_position_embeddings						,      snake_case )

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

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


if __name__ == "__main__":
   _A		:				Union[str, Any]									=       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.'''
   )
   _A		:				Optional[int]									=       parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






from __future__ import annotations

from typing import Any



class        a					:





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 0							):
        __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	row, column
        __lowerCamelCase:				Optional[Any]     =	[[default_value for c in range(SCREAMING_SNAKE_CASE_							)] for r in range(SCREAMING_SNAKE_CASE_							)]





     def __str__(    self					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	F'Matrix consist of {self.row} rows and {self.column} columns\n'

        # Make string identifier
        __lowerCamelCase:				Optional[int]     =	0
        for row_vector in self.array:
           for obj in row_vector:
              __lowerCamelCase:				int     =	max(SCREAMING_SNAKE_CASE_    ,	len(str(SCREAMING_SNAKE_CASE_							)							)							)
        __lowerCamelCase:				Union[str, Any]     =	F'%{max_element_length}s'

        # Make string and return
        def single_line(SCREAMING_SNAKE_CASE_					:				list[float]							) -> str:
           nonlocal string_format_identifier
           __lowerCamelCase:				Any     =	"""["""
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector							)
           line += "]"
           return line

        s += "\n".join(single_line(SCREAMING_SNAKE_CASE_							) for row_vector in self.array							)
        return s





     def __repr__(    self					:				Union[str, Any]							):
        return str(self							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        if not (isinstance(SCREAMING_SNAKE_CASE_    ,	(list, tuple)							) and len(SCREAMING_SNAKE_CASE_							) == 2):
           return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
        else:
           return True





     def __getitem__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        return self.array[loc[0]][loc[1]]





     def __setitem__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]    ,	SCREAMING_SNAKE_CASE_					:				float							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	value





     def __add__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == another.row and self.column == another.column

        # Add
        __lowerCamelCase:				Any     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				Tuple     =	self[r, c] + another[r, c]
        return result





     def __neg__(    self					:				Any							):
        __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				int     =	-self[r, c]
        return result





     def __sub__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        return self + (-another)





     def __mul__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int | float | Matrix							):

        if isinstance(SCREAMING_SNAKE_CASE_    ,	(int, float)							):  # Scalar multiplication
           __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
           for r in range(self.row							):
              for c in range(self.column							):
                 __lowerCamelCase:				List[Any]     =	self[r, c] * another
           return result
        elif isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):  # Matrix multiplication
           assert self.column == another.row
           __lowerCamelCase:				Optional[int]     =	Matrix(self.row    ,	another.column							)
           for r in range(self.row							):
              for c in range(another.column							):
                 for i in range(self.column							):
                    result[r, c] += self[r, i] * another[i, c]
           return result
        else:
           __lowerCamelCase:				int     =	F'Unsupported type given for another ({type(SCREAMING_SNAKE_CASE_							)})'
           raise TypeError(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				int     =	Matrix(self.column    ,	self.row							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				List[str]     =	self[r, c]
        return result







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Matrix    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) and isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        __lowerCamelCase:				Optional[Any]     =	v.transpose()
        __lowerCamelCase:				Any     =	(v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
           return None  # It's not invertable
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



   def     __lowerCAmelCase						(							)       ->							None:
      # a^(-1)
      __lowerCamelCase:				int     =	Matrix(3						,      3						,      0 )
      for i in range(3 ):
         __lowerCamelCase:				List[Any]     =	1
      print(f'a^(-1) is {ainv}' )
      # u, v
      __lowerCamelCase:				List[str]     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[Any]     =	1, 2, -3
      __lowerCamelCase:				Dict     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	4, -2, 5
      print(f'u is {u}' )
      print(f'v is {v}' )
      print(f'uv^T is {u * v.transpose()}' )
      # Sherman Morrison
      print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case						,      snake_case )}' )



   def     __lowerCAmelCase						(							)       ->							None:
      import doctest

      doctest.testmod()







   testa()


