




"""simple docstring"""






def 	_snake_case     (							lowercase__				,       lowercase__						):
				_enforce_args(lowercase__				,       lowercase__						)
				if n == 0:
								return 0
				_lowerCamelCase	:     Optional[Any]       =       float('-inf'						)
				for i in range(1				,       n + 1						):
								_lowerCamelCase	:     Dict       =       max(
								    lowercase__				,       prices[i - 1] + naive_cut_rod_recursive(n - i				,       lowercase__						)						)

				return max_revue







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_enforce_args(lowercase__				,       lowercase__						)
				_lowerCamelCase	:     Optional[int]       =       [float('-inf'						) for _ in range(n + 1						)]
				return _top_down_cut_rod_recursive(lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__						):
				if max_rev[n] >= 0:
								return max_rev[n]
				elif n == 0:
								return 0
				else:
								_lowerCamelCase	:     Tuple       =       float('-inf'						)
								for i in range(1				,       n + 1						):
												_lowerCamelCase	:     Optional[int]       =       max(
												    lowercase__				,       prices[i - 1] + _top_down_cut_rod_recursive(n - i				,       lowercase__				,       lowercase__						)				,       )

								_lowerCamelCase	:     List[Any]       =       max_revenue

				return max_rev[n]







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_enforce_args(lowercase__				,       lowercase__						)

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

				for i in range(1				,       n + 1						):
								_lowerCamelCase	:     Union[str, Any]       =       max_rev[i]
								for j in range(1				,       i + 1						):
												_lowerCamelCase	:     int       =       max(lowercase__				,       prices[j - 1] + max_rev[i - j]						)

								_lowerCamelCase	:     Dict       =       max_revenue_i

				return max_rev[n]







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if n < 0:
								_lowerCamelCase	:     Optional[Any]       =       f'''n must be greater than or equal to 0. Got n = {n}'''
								raise ValueError(lowercase__						)

				if n > len(lowercase__						):
								_lowerCamelCase	:     List[str]       =       (
								    'Each integral piece of rod must have a corresponding price. '
								    f'''Got n = {n} but length of prices = {len(lowercase__						)}'''
								)
								raise ValueError(lowercase__						)







def 	_snake_case     (							):
				_lowerCamelCase	:     str       =       [6, 10, 12, 15, 20, 23]
				_lowerCamelCase	:     Tuple       =       len(lowercase__						)

				# the best revenue comes from cutting the rod into 6 pieces, each
				# of length 1 resulting in a revenue of 6 * 6 = 36.
				_lowerCamelCase	:     List[str]       =       36

				_lowerCamelCase	:     Optional[int]       =       top_down_cut_rod(lowercase__				,       lowercase__						)
				_lowerCamelCase	:     Union[str, Any]       =       bottom_up_cut_rod(lowercase__				,       lowercase__						)
				_lowerCamelCase	:     Tuple       =       naive_cut_rod_recursive(lowercase__				,       lowercase__						)

				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"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}