from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__lowercase							 =					TypeVar('''T''')


class 	lowerCamelCase_     (   Generic[T] ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase)       ->	None:
        __UpperCamelCase     :Any | T			  =		None

        __UpperCamelCase     :int			  =		len(__lowercase)
        __UpperCamelCase     :list[T]			  =		[any_type for _ in range(self.N)] + arr
        __UpperCamelCase     :int			  =		fnc
        self.build()







    def 			UpperCamelCase__   (	self)       ->	None:
        for p in range(self.N - 1 , 0 , -1):
            __UpperCamelCase     :List[str]			  =		self.fn(self.st[p * 2] , self.st[p * 2 + 1])







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	None:
        p += self.N
        __UpperCamelCase     :int			  =		v
        while p > 1:
            __UpperCamelCase     :Optional[int]			  =		p // 2
            __UpperCamelCase     :Optional[int]			  =		self.fn(self.st[p * 2] , self.st[p * 2 + 1])







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	T | None:  # noqa: E741
        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		l + self.N, r + self.N

        __UpperCamelCase     :T | None			  =		None
        while l <= r:
            if l % 2 == 1:
                __UpperCamelCase     :Optional[int]			  =		self.st[l] if res is None else self.fn(__lowercase , self.st[l])
            if r % 2 == 0:
                __UpperCamelCase     :Dict			  =		self.st[r] if res is None else self.fn(__lowercase , self.st[r])
            __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
    from functools import reduce

    __lowercase							 =					[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

    __lowercase							 =					{
        0: 7,
        1: 2,
        2: 6,
        3: -14,
        4: 5,
        5: 4,
        6: 7,
        7: -10,
        8: 9,
        9: 10,
        10: 12,
        11: 1,
    }

    __lowercase							 =					SegmentTree(test_array, min)
    __lowercase							 =					SegmentTree(test_array, max)
    __lowercase							 =					SegmentTree(test_array, lambda a, b: a + b)



    def lowerCamelCase  (   ):
        '''simple docstring'''







        for i in range(len(SCREAMING_SNAKE_CASE	)	):
            for j in range(SCREAMING_SNAKE_CASE  ,			len(SCREAMING_SNAKE_CASE	)	):
                __UpperCamelCase     :Union[str, Any]			  =		reduce(SCREAMING_SNAKE_CASE  ,			test_array[i : j + 1]	)
                __UpperCamelCase     :Dict			  =		reduce(SCREAMING_SNAKE_CASE  ,			test_array[i : j + 1]	)
                __UpperCamelCase     :str			  =		reduce(lambda SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	: a + b  ,			test_array[i : j + 1]	)
                assert min_range == min_segment_tree.query(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                assert max_range == max_segment_tree.query(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                assert sum_range == sum_segment_tree.query(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    test_all_segments()

    for index, value in test_updates.items():
        __lowercase							 =					value
        min_segment_tree.update(index, value)
        max_segment_tree.update(index, value)
        sum_segment_tree.update(index, value)
        test_all_segments()






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
        """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
        """It returns the most likely label in the list of provided `labels` for the input text."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





