






'''simple docstring'''
from __future__ import annotations

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

a_					= TypeVar('T')






class      __SCREAMING_SNAKE_CASE      (			Generic[T]				):




 def __init__(      self	:							str					,      __lowercase	:							list[T]					,      __lowercase	:							Callable[[T, T], T]      )	->						None:
       SCREAMING_SNAKE_CASE__     :				Any | T									=None

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




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




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							int					,      __lowercase	:							T      )	->						None:
       p += self.N
       SCREAMING_SNAKE_CASE__     :				Dict									=v
       while p > 1:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=p // 2
             SCREAMING_SNAKE_CASE__     :				int									=self.fn(self.st[p * 2]					,      self.st[p * 2 + 1]      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							int					,      __lowercase	:							int      )	->						T | None:  # noqa: E741
       SCREAMING_SNAKE_CASE__     :				Tuple									=l + self.N, r + self.N

       SCREAMING_SNAKE_CASE__     :				T | None									=None
       while l <= r:
             if l % 2 == 1:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=self.st[l] if res is None else self.fn(__lowercase					,      self.st[l]      )
             if r % 2 == 0:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=self.st[r] if res is None else self.fn(__lowercase					,      self.st[r]      )
             SCREAMING_SNAKE_CASE__     :				str									=(l + 1) // 2, (r - 1) // 2
       return res


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

     a_					= [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

     a_					= {
         0: 7,
         1: 2,
         2: 6,
         3: -1_4,
         4: 5,
         5: 4,
         6: 7,
         7: -1_0,
         8: 9,
         9: 1_0,
         1_0: 1_2,
         1_1: 1,
     }

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





     def  _a(  ):




           '''simple docstring'''






           for i in range(len(UpperCamelCase__							)							):
                 for j in range(UpperCamelCase__,	len(UpperCamelCase__							)							):
                       SCREAMING_SNAKE_CASE__     :				Optional[int]									=reduce(UpperCamelCase__,	test_array[i : j + 1]							)
                       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=reduce(UpperCamelCase__,	test_array[i : j + 1]							)
                       SCREAMING_SNAKE_CASE__     :				Any									=reduce(lambda UpperCamelCase__,	UpperCamelCase__							: a + b,	test_array[i : j + 1]							)
                       assert min_range == min_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)
                       assert max_range == max_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)
                       assert sum_range == sum_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)

     test_all_segments()

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







'''simple docstring'''
import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:






  class      __SCREAMING_SNAKE_CASE      :




   @staticmethod
   def      __magic_name__	(      *__lowercase	:							int					,      **__lowercase	:							Optional[Any]      )	->						Optional[Any]:
         pass






@is_pipeline_test
@require_vision
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
       # python and torch versions.
       self.assertIn(
           nested_simplify(__lowercase      )					,      [
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				int									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      framework='''tf'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @slow
 @require_torch
 def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Any									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )




 @slow
 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				str									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      framework='''tf'''      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )