


from __future__ import annotations

from typing import Any
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = 0						)   ->			Any:







								'''simple docstring'''

								__lowercase					, __lowercase 					=   row, column
								__lowercase 					=   [[default_value for c in range(_snake_case						)] for r in range(_snake_case						)]






					def __str__(				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   F'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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

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






					def __repr__(				self						)   ->			List[str]:







								'''simple docstring'''

								return str(self						)






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								if not (isinstance(_snake_case ,       (list, tuple)						) and len(_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 ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								assert self.validate_indicies(_snake_case						)
								return self.array[loc[0]][loc[1]]






					def __setitem__(				self ,       snake_case_ ,       snake_case_						)   ->			str:







								'''simple docstring'''

								assert self.validate_indicies(_snake_case						)
								__lowercase 					=   value






					def __add__(				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								assert isinstance(_snake_case ,       _snake_case						)
								assert self.row == another.row and self.column == another.column

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






					def __neg__(				self						)   ->			int:







								'''simple docstring'''

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






					def __sub__(				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self + (-another)






					def __mul__(				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

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






					def A      (				self						)   ->			int:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			str:







								'''simple docstring'''

								assert isinstance(_snake_case ,       _snake_case						) and isinstance(_snake_case ,       _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
								__lowercase 					=   v.transpose()
								__lowercase 					=   (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 		lowercase_  (     ):


				'''simple docstring'''







				__lowercase 					=   Matrix(3       ,			3       ,			0  )
				for i in range(3  ):
							__lowercase 					=   1
				print(F'a^(-1) is {ainv}'  )
				# u, v
				__lowercase 					=   Matrix(3       ,			1       ,			0  )
				__lowercase					, __lowercase					, __lowercase 					=   1, 2, -3
				__lowercase 					=   Matrix(3       ,			1       ,			0  )
				__lowercase					, __lowercase					, __lowercase 					=   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(__UpperCamelCase       ,			__UpperCamelCase  )}'  )


	def 		lowercase_  (     ):


				'''simple docstring'''







				import doctest

				doctest.testmod()





	testa()









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				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(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


