





'''simple docstring'''

def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    list    )						->Union[str, Any]:
    _enforce_args(__lowerCamelCase					,       __lowerCamelCase    )
    if n == 0:
        return 0
    _SCREAMING_SNAKE_CASE									=			float("""-inf"""    )
    for i in range(1					,       n + 1    ):
        _SCREAMING_SNAKE_CASE									=			max(
            __lowerCamelCase					,       prices[i - 1] + naive_cut_rod_recursive(n - i					,       __lowerCamelCase    )    )

    return max_revue





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    list    )						->Optional[Any]:
    _enforce_args(__lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			[float("""-inf"""    ) for _ in range(n + 1    )]
    return _top_down_cut_rod_recursive(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    list					,       __lowerCamelCase :    list    )						->Tuple:
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        _SCREAMING_SNAKE_CASE									=			float("""-inf"""    )
        for i in range(1					,       n + 1    ):
            _SCREAMING_SNAKE_CASE									=			max(
                __lowerCamelCase					,       prices[i - 1] + _top_down_cut_rod_recursive(n - i					,       __lowerCamelCase					,       __lowerCamelCase    )					,       )

        _SCREAMING_SNAKE_CASE									=			max_revenue

    return max_rev[n]





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    list    )						->Union[str, Any]:
    _enforce_args(__lowerCamelCase					,       __lowerCamelCase    )

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

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

        _SCREAMING_SNAKE_CASE									=			max_revenue_i

    return max_rev[n]





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    list    )						->int:
    if n < 0:
        _SCREAMING_SNAKE_CASE									=			F'n must be greater than or equal to 0. Got n = {n}'
        raise ValueError(__lowerCamelCase    )

    if n > len(__lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			(
            """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						(     )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			[6, 10, 12, 15, 20, 23]
    _SCREAMING_SNAKE_CASE									=			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.
    _SCREAMING_SNAKE_CASE									=			36

    _SCREAMING_SNAKE_CASE									=			top_down_cut_rod(__lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			bottom_up_cut_rod(__lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			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()











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_tensorflow_text_available():
	from transformers.models.bert import TFBertTokenizer


UpperCAmelCase_ :						Optional[Any]      =      ['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase_ :						List[str]      =      '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

	class     _SCREAMING_SNAKE_CASE       (			tf.keras.Model      ):





		def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any]	):
			super().__init__()
			UpperCamelCase     :Any          =	tokenizer
			UpperCamelCase     :List[str]          =	AutoConfig.from_pretrained(__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	TFAutoModel.from_config(__lowerCamelCase	)





		def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str	):
			UpperCamelCase     :str          =	self.tokenizer(__lowerCamelCase	)
			UpperCamelCase     :Any          =	self.bert(**__lowerCamelCase	)
			return out["pooler_output"]

@require_tf
@require_tensorflow_text
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					Dict	):
		super().setUp()

		UpperCamelCase     :int          =	[
		    BertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
		]  # repeat for when fast_bert_tokenizer=false
		UpperCamelCase     :Any          =	[TFBertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in TOKENIZER_CHECKPOINTS] + [
		    TFBertTokenizer.from_pretrained(__lowerCamelCase ,  use_fast_bert_tokenizer=__lowerCamelCase	)
		    for checkpoint in TOKENIZER_CHECKPOINTS
		]
		assert len(self.tokenizers	) == len(self.tf_tokenizers	)

		UpperCamelCase     :Any          =	[
		    """This is a straightforward English test sentence.""",
		    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
		    """Now we're going to add some Chinese: 一 二 三 一二三""",
		    """And some much more rare Chinese: 齉 堃 齉堃""",
		    """Je vais aussi écrire en français pour tester les accents""",
		    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
		]
		UpperCamelCase     :Union[str, Any]          =	list(zip(self.test_sentences ,  self.test_sentences[::-1]	)	)





	def 						_A (		self				:					Optional[int]	):
		for tokenizer, tf_tokenizer in zip(self.tokenizers ,  self.tf_tokenizers	):
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tokenizer(__lowerCamelCase ,  return_tensors="""tf""" ,  padding="""longest"""	)
				UpperCamelCase     :str          =	tf_tokenizer(__lowerCamelCase	)

				for key in python_outputs.keys():
					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape	)	)
					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,  tf.intaa	) == tf_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Dict	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :str          =	tf_tokenizer(self.paired_sentences	)
			UpperCamelCase     :Any          =	tf_tokenizer(
			    text=[sentence[0] for sentence in self.paired_sentences] ,  text_pair=[sentence[1] for sentence in self.paired_sentences] ,  )
			for key in merged_outputs.keys():
				self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,  tf.intaa	) == separated_outputs[key]	)	)





	@slow
	def 						_A (		self				:					List[str]	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[Any]          =	tf.function(__lowerCamelCase	)
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tf.constant(__lowerCamelCase	)
				UpperCamelCase     :List[str]          =	compiled_tokenizer(__lowerCamelCase	)
				UpperCamelCase     :Optional[Any]          =	tf_tokenizer(__lowerCamelCase	)

				for key in eager_outputs.keys():
					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[str]          =	ModelToSave(tokenizer=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	tf.convert_to_tensor(self.test_sentences	)
			UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)  # Build model with some sample inputs
			with TemporaryDirectory() as tempdir:
				UpperCamelCase     :List[str]          =	Path(__lowerCamelCase	) / """saved.model"""
				model.save(__lowerCamelCase	)
				UpperCamelCase     :List[Any]          =	tf.keras.models.load_model(__lowerCamelCase	)
			UpperCamelCase     :Dict          =	loaded_model(__lowerCamelCase	)
			# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
			self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output	)	) ,  1E-5	)
