

'''simple docstring'''


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
						import torch

lowerCAmelCase__      				=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
						from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







						def     SCREAMING_SNAKE_CASE(	cls	)     ->     Dict:
							return cls







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,)     ->     int:
	UpperCAmelCase_		:   Optional[Any]													=       device
	UpperCAmelCase_		:   List[Any]													=       shape[0]

	UpperCAmelCase_		:   int													=       layout or torch.strided
	UpperCAmelCase_		:   Any													=       device or torch.device('cpu'	)

	if generator is not None:
		UpperCAmelCase_		:   Tuple													=       generator.device.type if not isinstance(UpperCamelCase ,UpperCamelCase	) else generator[0].device.type
		if gen_device_type != device.type and gen_device_type == "cpu":
			UpperCAmelCase_		:   Optional[int]													=       'cpu'
			if device != "mps":
				logger.info(
				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""	)
		elif gen_device_type != device.type and gen_device_type == "cuda":
			raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""	)

	if isinstance(UpperCamelCase ,UpperCamelCase	):
		UpperCAmelCase_		:   Dict													=       (1,) + shape[1:]
		UpperCAmelCase_		:   List[str]													=       [
		    torch.randn(UpperCamelCase ,generator=generator[i] ,device=UpperCamelCase ,dtype=UpperCamelCase ,layout=UpperCamelCase	)
		    for i in range(UpperCamelCase	)
		]
		UpperCAmelCase_		:   Optional[int]													=       torch.cat(UpperCamelCase ,dim=0	).to(UpperCamelCase	)
	else:
		UpperCAmelCase_		:   str													=       torch.randn(UpperCamelCase ,generator=UpperCamelCase ,device=UpperCamelCase ,dtype=UpperCamelCase ,layout=UpperCamelCase	).to(UpperCamelCase	)

	return latents







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if is_torch_version('<' ,'2.0.0'	) or not hasattr(UpperCamelCase ,'_dynamo'	):
		return False
	return isinstance(UpperCamelCase ,torch._dynamo.eval_frame.OptimizedModule	)









'''simple docstring'''


def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('recursive_lucas_number accepts only integer arguments.'	)
	if n_th_number == 0:
		return 2
	if n_th_number == 1:
		return 1

	return recursive_lucas_number(n_th_number - 1	) + recursive_lucas_number(
	    n_th_number - 2	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('dynamic_lucas_number accepts only integer arguments.'	)
	UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       2, 1
	for _ in range(UpperCamelCase	):
		UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       b, a + b
	return a


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCAmelCase__      				=			int(input("Enter the number of terms in lucas series:\n").strip())
						print("Using recursive function to calculate lucas series:")
						print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
						print("\nUsing dynamic function to calculate lucas series:")
						print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






