

from __future__ import annotations

import math







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:

		if depth < 0:
				raise ValueError("Depth cannot be less than 0" )

		if not scores:
				raise ValueError("Scores cannot be empty" )

		if depth == height:
				return scores[node_index]

		return (
		    max(
		        minimax(depth + 1			,     node_index * 2			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase )			,     minimax(depth + 1			,     node_index * 2 + 1			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase )			,     )
		    if is_max
		    else min(
		        minimax(depth + 1			,     node_index * 2			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase )			,     minimax(depth + 1			,     node_index * 2 + 1			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase )			,     )
		)







def snake_case__  (			)		->							Tuple:
		UpperCAmelCase_						=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
		UpperCAmelCase_						=				math.log(len(_lowerCamelCase )			,     2 )
		print(f'''Optimal value : {minimax(0			,     0			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase )}''' )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

