



from copy import deepcopy






class SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = None,		lowerCamelCase__ = None		):

        if arr is None and size is not None:
            A :       Optional[int]          =   size
            A :       Optional[Any]          =   [0] * size
        elif arr is not None:
            self.init(lowerCamelCase__		)
        else:
            raise ValueError("""Either arr or size must be specified"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       int          =   len(lowerCamelCase__		)
        A :       int          =   deepcopy(lowerCamelCase__		)
        for i in range(1,		self.size		):
            A :       List[Any]          =   self.next_(lowerCamelCase__		)
            if j < self.size:
                self.tree[j] += self.tree[i]






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.tree[:]
        for i in range(self.size - 1,		0,		-1		):
            A :       Union[str, Any]          =   self.next_(lowerCamelCase__		)
            if j < self.size:
                arr[j] -= arr[i]
        return arr






    @staticmethod
    def 			_lowerCAmelCase					(       lowerCamelCase__		):
        return index + (index & (-index))






    @staticmethod
    def 			_lowerCAmelCase					(       lowerCamelCase__		):
        return index - (index & (-index))






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if index == 0:
            self.tree[0] += value
            return
        while index < self.size:
            self.tree[index] += value
            A :       Union[str, Any]          =   self.next_(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        self.add(lowerCamelCase__,		value - self.get(lowerCamelCase__		)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if right == 0:
            return 0
        A :       List[str]          =   self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
            result += self.tree[right]
            A :       Optional[int]          =   self.prev(lowerCamelCase__		)
        return result






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        return self.prefix(lowerCamelCase__		) - self.prefix(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return self.query(lowerCamelCase__,		index + 1		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        value -= self.tree[0]
        if value < 0:
            return -1

        A :       Optional[int]          =   1  # Largest power of 2 <= size
        while j * 2 < self.size:
            j *= 2

        A :       List[Any]          =   0

        while j > 0:
            if i + j < self.size and self.tree[i + j] <= value:
                value -= self.tree[i + j]
                i += j
            j //= 2
        return i


if __name__ == "__main__":
     import doctest

     doctest.testmod()







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


