





'''simple docstring'''




import timeit




class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	Optional[int]     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



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



					def __mul__(		self							:	List[Any]     ,		lowerCamelCase_							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	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 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	Matrix([[1, 0], [0, 1]]					)
		while b > 0:
				if b & 1:
						matrix *= a
				a *= a
				b >>= 1
		return matrix



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		if n == 1:
				return fa
		elif n == 2:
				return fa
		SCREAMING_SNAKE_CASE				:							Tuple						=	Matrix([[1, 1], [1, 0]]					)
		SCREAMING_SNAKE_CASE				:							str						=	modular_exponentiation(lowerCamelCase_	,	n - 2					)
		return fa * matrix.t[0][0] + fa * matrix.t[0][1]



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		if n == 1:
				return fa
		elif n == 2:
				return fa

		SCREAMING_SNAKE_CASE				:							List[str]						=	fa
		SCREAMING_SNAKE_CASE				:							int						=	fa
		n -= 2

		while n > 0:
				SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Any						=	fn_a + fn_a, fn_a
				n -= 1

		return fn_a



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	"""
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
		SCREAMING_SNAKE_CASE				:							List[Any]						=	"""fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
		SCREAMING_SNAKE_CASE				:							List[str]						=	timeit.timeit(setup=lowerCamelCase_	,	stmt=lowerCamelCase_	,	number=1_00					)
		print("""With matrix exponentiation the average execution time is """	,	exec_time / 1_00					)
		return exec_time



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	"""
from random import randint
from __main__ import simple_fibonacci
"""
		SCREAMING_SNAKE_CASE				:							str						=	"""simple_fibonacci(randint(1,70000), 1, 1)"""
		SCREAMING_SNAKE_CASE				:							List[Any]						=	timeit.timeit(setup=lowerCamelCase_	,	stmt=lowerCamelCase_	,	number=1_00					)
		print(
		    """Without matrix exponentiation the average execution time is """	,	exec_time / 1_00					)
		return exec_time



def 						__A						(       ):


		"""simple docstring"""



		matrix_exponentiation_time()
		simple_fibonacci_time()


if __name__ == "__main__":
							main()










'''simple docstring'''




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						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

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

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
		torch.save(model.state_dict()	,	lowerCamelCase_					)
		print(f'''Save configuration file to {pytorch_config_dump_path}'''					)
		with open(lowerCamelCase_	,	"""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
							)



