



from __future__ import annotations

from typing import Any





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 0			):
         _lowerCAmelCase			,   _lowerCAmelCase 			=  row, column
         _lowerCAmelCase 			=  [[default_value for c in range(_lowerCAmelCase			)] for r in range(_lowerCAmelCase			)]





   def __str__(	self			):
         _lowerCAmelCase 			=  F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

         # Make string identifier
         _lowerCAmelCase 			=  0
         for row_vector in self.array:
               for obj in row_vector:
                     _lowerCAmelCase 			=  max(_lowerCAmelCase       ,     len(str(_lowerCAmelCase			)			)			)
         _lowerCAmelCase 			=  F'''%{max_element_length}s'''

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

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





   def __repr__(	self			):
         return str(self			)





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





   def __getitem__(	self       ,     _lowerCAmelCase			):
         assert self.validate_indicies(_lowerCAmelCase			)
         return self.array[loc[0]][loc[1]]





   def __setitem__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         assert self.validate_indicies(_lowerCAmelCase			)
         _lowerCAmelCase 			=  value





   def __add__(	self       ,     _lowerCAmelCase			):
         assert isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
         assert self.row == another.row and self.column == another.column

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





   def __neg__(	self			):
         _lowerCAmelCase 			=  Matrix(self.row       ,     self.column			)
         for r in range(self.row			):
               for c in range(self.column			):
                     _lowerCAmelCase 			=  -self[r, c]
         return result





   def __sub__(	self       ,     _lowerCAmelCase			):
         return self + (-another)





   def __mul__(	self       ,     _lowerCAmelCase			):

         if isinstance(_lowerCAmelCase       ,     (int, float)			):  # Scalar multiplication
               _lowerCAmelCase 			=  Matrix(self.row       ,     self.column			)
               for r in range(self.row			):
                     for c in range(self.column			):
                           _lowerCAmelCase 			=  self[r, c] * another
               return result
         elif isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):  # Matrix multiplication
               assert self.column == another.row
               _lowerCAmelCase 			=  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 			=  F'''Unsupported type given for another ({type(_lowerCAmelCase			)})'''
               raise TypeError(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  Matrix(self.column       ,     self.row			)
         for r in range(self.row			):
               for c in range(self.column			):
                     _lowerCAmelCase 			=  self[r, c]
         return result





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         assert isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			) and isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
         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 			=  v.transpose()
         _lowerCAmelCase 			=  (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 							UpperCAmelCase__		(				)->None:
          # a^(-1)
          _lowerCAmelCase 			=  Matrix(3     ,				3     ,				0				)
          for i in range(3				):
                _lowerCAmelCase 			=  1
          print(f'''a^(-1) is {ainv}'''				)
          # u, v
          _lowerCAmelCase 			=  Matrix(3     ,				1     ,				0				)
          _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  1, 2, -3
          _lowerCAmelCase 			=  Matrix(3     ,				1     ,				0				)
          _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  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(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)}'''				)
    def 							UpperCAmelCase__		(				)->None:
          import doctest

          doctest.testmod()


    testa()




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

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


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