
"""simple docstring"""


import functools
from math import sqrt
from time import time



def        _lowerCamelCase(__UpperCamelCase       ,					*__UpperCamelCase       ,					**__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase								=time()
	_lowerCAmelCase								=func(*__UpperCamelCase       ,					**__UpperCamelCase			)
	_lowerCAmelCase								=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        _lowerCamelCase(__UpperCamelCase			)							->					list[int]:
	if n < 0:
		raise Exception("""n is negative"""			)
	if n == 0:
		return [0]
	_lowerCAmelCase								=[0, 1]
	for _ in range(n - 1			):
		fib.append(fib[-1] + fib[-2]			)
	return fib



def        _lowerCamelCase(__UpperCamelCase			)							->					list[int]:

	def fib_recursive_term(__UpperCamelCase			) -> 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(__UpperCamelCase			) for i in range(n + 1			)]



def        _lowerCamelCase(__UpperCamelCase			)							->					list[int]:

	@functools.cache
	def fib_recursive_term(__UpperCamelCase			) -> 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(__UpperCamelCase			) for i in range(n + 1			)]



def        _lowerCamelCase(__UpperCamelCase			)							->					list[int]:
	if n < 0:
		raise Exception("""n is negative"""			)
	# Cache must be outside recursuive function
	# other it will reset every time it calls itself.
	_lowerCAmelCase								={0: 0, 1: 1, 2: 1}  # Prefilled cache

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

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

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



def        _lowerCamelCase(__UpperCamelCase			)							->					list[int]:
	if n < 0:
		raise Exception("""n is negative"""			)
	if n >= 1475:
		raise Exception("""n is too large"""			)
	_lowerCAmelCase								=sqrt(5			)
	_lowerCAmelCase								=(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)





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			[
			    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DonutSwinModel',
			    'DonutSwinPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			['DonutFeatureExtractor']
			__A					=			['DonutImageProcessor']


if TYPE_CHECKING:
			from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
			from .processing_donut import DonutProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_donut_swin import (
						    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DonutSwinModel,
						    DonutSwinPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_donut import DonutFeatureExtractor
						from .image_processing_donut import DonutImageProcessor

else:
			import sys

			__A					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



