



"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Tuple:
							_enforce_args(lowerCamelCase__				,      lowerCamelCase__			)
							if n == 0:
														return 0
							A         =					float('-inf'			)
							for i in range(1				,      n + 1			):
														A         =					max(
														    lowerCamelCase__				,      prices[i - 1] + naive_cut_rod_recursive(n - i				,      lowerCamelCase__			)			)

							return max_revue

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Any:
							_enforce_args(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					[float('-inf'			) for _ in range(n + 1			)]
							return _top_down_cut_rod_recursive(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Dict:
							if max_rev[n] >= 0:
														return max_rev[n]
							elif n == 0:
														return 0
							else:
														A         =					float('-inf'			)
														for i in range(1				,      n + 1			):
																					A         =					max(
																					    lowerCamelCase__				,      prices[i - 1] + _top_down_cut_rod_recursive(n - i				,      lowerCamelCase__				,      lowerCamelCase__			)				,      )

														A         =					max_revenue

							return max_rev[n]

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							_enforce_args(lowerCamelCase__				,      lowerCamelCase__			)

							# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
							# length 0.
							A         =					[float('-inf'			) for _ in range(n + 1			)]
							A         =					0

							for i in range(1				,      n + 1			):
														A         =					max_rev[i]
														for j in range(1				,      i + 1			):
																					A         =					max(lowerCamelCase__				,      prices[j - 1] + max_rev[i - j]			)

														A         =					max_revenue_i

							return max_rev[n]

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Dict:
							if n < 0:
														A         =					f"""n must be greater than or equal to 0. Got n = {n}"""
														raise ValueError(lowerCamelCase__			)

							if n > len(lowerCamelCase__			):
														A         =					(
														    'Each integral piece of rod must have a corresponding price. '
														    f"""Got n = {n} but length of prices = {len(lowerCamelCase__			)}"""
														)
														raise ValueError(lowerCamelCase__			)

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							A         =					[6, 10, 12, 15, 20, 23]
							A         =					len(lowerCamelCase__			)

							# the best revenue comes from cutting the rod into 6 pieces, each
							# of length 1 resulting in a revenue of 6 * 6 = 36.
							A         =					36

							A         =					top_down_cut_rod(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					bottom_up_cut_rod(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					naive_cut_rod_recursive(lowerCamelCase__				,      lowerCamelCase__			)

							assert expected_max_revenue == max_rev_top_down
							assert max_rev_top_down == max_rev_bottom_up
							assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
							main()











"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

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



