
import functools
from math import sqrt
from time import time



def 		lowercase_     (  A__					,						*A__					,						**A__						)				->				List[str]:
    """simple docstring"""

    snake_case						=   time()
    snake_case						=   func(*A__					,						**A__						)
    snake_case						=   time()
    if int(end - start						) > 0:
        print(F'{func.__name__} runtime: {(end - start):0.4f} s'						)
    else:
        print(F'{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'						)
    return output



def 		lowercase_     (  A__						)				->				list[int]:
    """simple docstring"""

    if n < 0:
        raise Exception("n is negative"						)
    if n == 0:
        return [0]
    snake_case						=   [0, 1]
    for _ in range(n - 1						):
        fib.append(fib[-1] + fib[-2]						)
    return fib



def 		lowercase_     (  A__						)				->				list[int]:
    """simple docstring"""

    def fib_recursive_term(A__						) -> int:
        if i < 0:
            raise Exception("n is negative"						)
        if i < 2:
            return i
        return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

    if n < 0:
        raise Exception("n is negative"						)
    return [fib_recursive_term(A__						) for i in range(n + 1						)]



def 		lowercase_     (  A__						)				->				list[int]:
    """simple docstring"""

    @functools.cache
    def fib_recursive_term(A__						) -> int:
        if i < 0:
            raise Exception("n is negative"						)
        if i < 2:
            return i
        return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

    if n < 0:
        raise Exception("n is negative"						)
    return [fib_recursive_term(A__						) for i in range(n + 1						)]



def 		lowercase_     (  A__						)				->				list[int]:
    """simple docstring"""

    if n < 0:
        raise Exception("n is negative"						)
    # Cache must be outside recursuive function
    # other it will reset every time it calls itself.
    snake_case						=   {0: 0, 1: 1, 2: 1}  # Prefilled cache

    def rec_fn_memoized(A__						) -> int:
        if num in cache:
            return cache[num]

        snake_case						=   rec_fn_memoized(num - 1						) + rec_fn_memoized(num - 2						)
        snake_case						=   value
        return value

    return [rec_fn_memoized(A__						) for i in range(n + 1						)]



def 		lowercase_     (  A__						)				->				list[int]:
    """simple docstring"""

    if n < 0:
        raise Exception("n is negative"						)
    if n >= 1475:
        raise Exception("n is too large"						)
    snake_case						=   sqrt(5						)
    snake_case						=   (1 + sqrt_a) / 2
    return [round(phi**i / sqrt_a						) for i in range(n + 1						)]


if __name__ == "__main__":
   _A       =					30
   time_func(fib_iterative, num)
   time_func(fib_recursive, num)  # Around 3s runtime
   time_func(fib_recursive_cached, num)  # Around 0ms runtime
   time_func(fib_memoization, num)
   time_func(fib_binet, num)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

