



from __future__ import annotations
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  a__												= TypeError(
      '''Matrices must be formed from a list of zero or more lists containing at '''
      '''least one and the same number of values, each of which must be of type '''
      '''int or float.'''      )
  if len(SCREAMING_SNAKE_CASE      ) != 0:
   a__												= len(rows[0]      )
   if cols == 0:
    raise error
   for row in rows:
    if len(SCREAMING_SNAKE_CASE      ) != cols:
     raise error
    for value in row:
     if not isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):
      raise error
   a__												= rows
  else:
   a__												= []

 def 						_UpperCAmelCase       (	self      ) ->			list[list[int]]:
  return [[row[i] for row in self.rows] for i in range(len(self.rows[0]      )      )]

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return len(self.rows      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return len(self.rows[0]      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			tuple[int, int]:
  return (self.num_rows, self.num_columns)

 @property
 def 						_UpperCAmelCase       (	self      ) ->			bool:
  return self.order[0] == self.order[1]

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= [
      [0 if column_num != row_num else 1 for column_num in range(self.num_rows      )]
      for row_num in range(self.num_rows      )
  ]
  return Matrix(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  if not self.is_square:
   return 0
  if self.order == (0, 0):
   return 1
  if self.order == (1, 1):
   return int(self.rows[0][0]      )
  if self.order == (2, 2):
   return int(
       (self.rows[0][0] * self.rows[1][1])
       - (self.rows[0][1] * self.rows[1][0])      )
  else:
   return sum(
       self.rows[0][column] * self.cofactors().rows[0][column]
       for column in range(self.num_columns      )      )

 def 						_UpperCAmelCase       (	self      ) ->			bool:
  return bool(self.determinant()      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= [
      [
          self.rows[other_row][other_column]
          for other_column in range(self.num_columns      )
          if other_column != column
      ]
      for other_row in range(self.num_rows      )
      if other_row != row
  ]
  return Matrix(SCREAMING_SNAKE_CASE      ).determinant()

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			int:
  if (row + column) % 2 == 0:
   return self.get_minor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  return -1 * self.get_minor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  return Matrix(
      [
          [self.get_minor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) for column in range(self.num_columns      )]
          for row in range(self.num_rows      )
      ]      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  return Matrix(
      [
          [
              self.minors().rows[row][column]
              if (row + column) % 2 == 0
              else self.minors().rows[row][column] * -1
              for column in range(self.minors().num_columns      )
          ]
          for row in range(self.minors().num_rows      )
      ]      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= [
      [self.cofactors().rows[column][row] for column in range(self.num_columns      )]
      for row in range(self.num_rows      )
  ]
  return Matrix(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= self.determinant()
  if not determinant:
   raise TypeError('''Only matrices with a non-zero determinant have an inverse'''      )
  return self.adjugate() * (1 / determinant)

 def __repr__(	self      ) ->			str:
  return str(self.rows      )

 def __str__(	self      ) ->			str:
  if self.num_rows == 0:
   return "[]"
  if self.num_rows == 1:
   return "[[" + ". ".join(str(self.rows[0]      )      ) + "]]"
  return (
      "["
      + "\n ".join(
          [
              '''[''' + '''. '''.join([str(SCREAMING_SNAKE_CASE      ) for value in row]      ) + '''.]'''
              for row in self.rows
          ]      )
      + "]"
  )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			None:
  a__												= TypeError('''Row must be a list containing all ints and/or floats'''      )
  if not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   raise type_error
  for value in row:
   if not isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):
    raise type_error
  if len(SCREAMING_SNAKE_CASE      ) != self.num_columns:
   raise ValueError(
       '''Row must be equal in length to the other rows in the matrix'''      )
  if position is None:
   self.rows.append(SCREAMING_SNAKE_CASE      )
  else:
   a__												= self.rows[0:position] + [row] + self.rows[position:]

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			None:
  a__												= TypeError(
      '''Column must be a list containing all ints and/or floats'''      )
  if not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   raise type_error
  for value in column:
   if not isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):
    raise type_error
  if len(SCREAMING_SNAKE_CASE      ) != self.num_rows:
   raise ValueError(
       '''Column must be equal in length to the other columns in the matrix'''      )
  if position is None:
   a__												= [self.rows[i] + [column[i]] for i in range(self.num_rows      )]
  else:
   a__												= [
       self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
       for i in range(self.num_rows      )
   ]

 def __eq__(	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  if not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   return NotImplemented
  return self.rows == other.rows

 def __ne__(	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  return not self == other

 def __neg__(	self      ) ->			Matrix:
  return self * -1

 def __add__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  if self.order != other.order:
   raise ValueError('''Addition requires matrices of the same order'''      )
  return Matrix(
      [
          [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns      )]
          for i in range(self.num_rows      )
      ]      )

 def __sub__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  if self.order != other.order:
   raise ValueError('''Subtraction requires matrices of the same order'''      )
  return Matrix(
      [
          [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns      )]
          for i in range(self.num_rows      )
      ]      )

 def __mul__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  if isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):
   return Matrix(
       [[int(element * other      ) for element in row] for row in self.rows]      )
  elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   if self.num_columns != other.num_rows:
    raise ValueError(
        '''The number of columns in the first matrix must '''
        '''be equal to the number of rows in the second'''      )
   return Matrix(
       [
           [Matrix.dot_product(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) for column in other.columns()]
           for row in self.rows
       ]      )
  else:
   raise TypeError(
       '''A Matrix can only be multiplied by an int, float, or another matrix'''      )

 def __pow__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  if not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   raise TypeError('''A Matrix can only be raised to the power of an int'''      )
  if not self.is_square:
   raise ValueError('''Only square matrices can be raised to a power'''      )
  if other == 0:
   return self.identity()
  if other < 0:
   if self.is_invertable():
    return self.inverse() ** (-other)
   raise ValueError(
       '''Only invertable matrices can be raised to a negative power'''      )
  a__												= self
  for _ in range(other - 1      ):
   result *= self
  return result



 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			int:
  return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE      )      )      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()










import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 a__												= (
     ('''layer.''', '''layer_'''),
     ('''word_embeddings.weight''', '''word_embeddings'''),
     ('''position_embeddings.weight''', '''position_embeddings'''),
     ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
     ('''.''', '''/'''),
     ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
     ('''LayerNorm/bias''', '''LayerNorm/beta'''),
     ('''weight''', '''kernel'''),
 )

 if not os.path.isdir(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   a__												= state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose       ):
    a__												= torch_tensor.T
   a__												= create_tf_var(tensor=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 a__												= BertModel.from_pretrained(
     pretrained_model_name_or_path=args.model_name							,  state_dict=torch.load(args.pytorch_model_path       )							,  cache_dir=args.cache_dir							,  )

 convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





