


import timeit

class   lowerCamelCase						:

       def __init__(      self						,    a__       ):






        '''simple docstring'''






        if isinstance(a__						,    a__       ):  # Initializes a matrix identical to the one provided.
         snake_case :			List[str]							=   arg
         snake_case :			int							=   len(a__       )
        else:  # Initializes a square matrix of the given size and set values to zero.
         snake_case :			Dict							=   arg
         snake_case :			List[str]							=   [[0 for _ in range(self.n       )] for _ in range(self.n       )]



       def __mul__(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Dict							=   Matrix(self.n       )
        for i in range(self.n       ):
         for j in range(self.n       ):
          for k in range(self.n       ):
           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix







def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->List[Any]:


 '''simple docstring'''
 snake_case :			Any							=   Matrix([[1, 0], [0, 1]]							)
 while b > 0:
  if b & 1:
   matrix *= a
  a *= a
  b >>= 1
 return matrix






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Any:


 '''simple docstring'''
 # Trivial Cases
 if n == 1:
  return fa
 elif n == 2:
  return fa
 snake_case :			int							=   Matrix([[1, 1], [1, 0]]							)
 snake_case :			int							=   modular_exponentiation(_UpperCAmelCase			,  n - 2							)
 return fa * matrix.t[0][0] + fa * matrix.t[0][1]






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Tuple:


 '''simple docstring'''
 # Trivial Cases
 if n == 1:
  return fa
 elif n == 2:
  return fa

 snake_case :			List[Any]							=   fa
 snake_case :			str							=   fa
 n -= 2

 while n > 0:
  snake_case	,       snake_case :			Optional[Any]							=   fn_a + fn_a, fn_a
  n -= 1

 return fn_a






def  _UpperCAmelCase						( )		->Dict:


 '''simple docstring'''
 snake_case :			List[str]							=   '''\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'''
 snake_case :			Any							=   '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
 snake_case :			int							=   timeit.timeit(setup=_UpperCAmelCase			,  stmt=_UpperCAmelCase			,  number=100							)
 print('''With matrix exponentiation the average execution time is '''			,  exec_time / 100							)
 return exec_time






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


 '''simple docstring'''
 snake_case :			Optional[int]							=   '''\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'''
 snake_case :			Union[str, Any]							=   '''simple_fibonacci(randint(1,70000), 1, 1)'''
 snake_case :			Optional[int]							=   timeit.timeit(setup=_UpperCAmelCase			,  stmt=_UpperCAmelCase			,  number=100							)
 print(
     '''Without matrix exponentiation the average execution time is '''			,  exec_time / 100							)
 return exec_time






def  _UpperCAmelCase						( )		->Optional[int]:


 '''simple docstring'''
 matrix_exponentiation_time()
 simple_fibonacci_time()


if __name__ == "__main__":
  main()











import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
