'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     list[list[int]]      ,  lowerCAmelCase       :     list[list[int]]			):
 __UpperCAmelCase         =					[]
 __UpperCAmelCase         =					len(lowerCAmelCase			)
 for i in range(lowerCAmelCase			):
  __UpperCAmelCase         =					[]
  for j in range(lowerCAmelCase			):
   __UpperCAmelCase         =					0
   for k in range(lowerCAmelCase			):
    __UpperCAmelCase         =					val + matrix_a[i][k] * matrix_b[k][j]
   list_a.append(lowerCAmelCase			)
  matrix_c.append(lowerCAmelCase			)
 return matrix_c



def 						__snake_case (       lowerCAmelCase       :     int			):
 return [[int(row == column			) for column in range(lowerCAmelCase			)] for row in range(lowerCAmelCase			)]



def 						__snake_case (       lowerCAmelCase       :     int			):
 if n <= 1:
  return n
 __UpperCAmelCase         =					identity(2			)
 __UpperCAmelCase         =					[[1, 1], [1, 0]]
 __UpperCAmelCase         =					n - 1
 while n > 0:
  if n % 2 == 1:
   __UpperCAmelCase         =					multiply(lowerCAmelCase      ,  lowerCAmelCase			)
  __UpperCAmelCase         =					multiply(lowerCAmelCase      ,  lowerCAmelCase			)
  __UpperCAmelCase         =					int(n / 2			)
 return res_matrix[0][0]



def 						__snake_case (       lowerCAmelCase       :     int			):
 if n <= 1:
  return n
 __UpperCAmelCase         =					0
 __UpperCAmelCase         =					1
 for _ in range(2      ,  n + 1			):
  __UpperCAmelCase  ,			__UpperCAmelCase         =					fiba, fiba + fiba
 return fiba



def 						__snake_case (       ):
 for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
  __UpperCAmelCase         =					int(''.join(c for c in ordinal if c in '0123456789'			)			)  # 1000th --> 1000
  print(
      F"""{ordinal} fibonacci number using matrix exponentiation is """
      F"""{nth_fibonacci_matrix(lowerCAmelCase			)} and using bruteforce is """
      F"""{nth_fibonacci_bruteforce(lowerCAmelCase			)}\n"""			)
 # from timeit import timeit
 # print(timeit("nth_fibonacci_matrix(1000000)",
 #              "from main import nth_fibonacci_matrix", number=5))
 # print(timeit("nth_fibonacci_bruteforce(1000000)",
 #              "from main import nth_fibonacci_bruteforce", number=5))
 # 2.3342058970001744
 # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCamelCase				:		List[Any]       			=		get_logger(__name__)



def 						__snake_case (       lowerCAmelCase       :     List[str]			):
 if version.parse(lowerCAmelCase			) < version.parse(lowerCAmelCase			):
  if "dev" in min_version:
   __UpperCAmelCase         =					(
       'This example requires a source install from HuggingFace diffusers (see '
       '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
   )
  else:
   __UpperCAmelCase         =					F"""This example requires a minimum version of {min_version},"""
  error_message += F""" but the version found is {__version__}.\n"""
  raise ImportError(lowerCAmelCase			)



