




'''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 							A (      *a_						:    Optional[Any]      ,		**a_						:    int							):



												"""simple docstring"""




												pass



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





			@require_torch
			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		pipeline(
						    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,		)
						__snake_case			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
						__snake_case			=		image_classifier(a_      ,		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(a_							)      ,		[
						        [{"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"}],
						    ]      ,		)

						__snake_case			=		image_classifier([image] * 5      ,		candidate_labels=["A", "B", "C"]      ,		batch_size=2							)
						self.assertEqual(
						    nested_simplify(a_							)      ,		[
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						    ]      ,		)





			@require_tf
			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		pipeline(
						    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,		framework="tf"							)
						__snake_case			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
						__snake_case			=		image_classifier(a_      ,		candidate_labels=["a", "b", "c"]							)

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

						__snake_case			=		image_classifier([image] * 5      ,		candidate_labels=["A", "B", "C"]      ,		batch_size=2							)
						self.assertEqual(
						    nested_simplify(a_							)      ,		[
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						        [
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						            {"score": 0.333, "label": ANY(a_							)},
						        ],
						    ]      ,		)





			@slow
			@require_torch
			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		pipeline(
						    task="zero-shot-image-classification"      ,		model="openai/clip-vit-base-patch32"      ,		)
						# This is an image of 2 cats with remotes and no planes
						__snake_case			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
						__snake_case			=		image_classifier(a_      ,		candidate_labels=["cat", "plane", "remote"]							)

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

						__snake_case			=		image_classifier([image] * 5      ,		candidate_labels=["cat", "plane", "remote"]      ,		batch_size=2							)
						self.assertEqual(
						    nested_simplify(a_							)      ,		[
						        [
						            {"score": 0.511, "label": "remote"},
						            {"score": 0.485, "label": "cat"},
						            {"score": 0.004, "label": "plane"},
						        ],
						    ]
						    * 5      ,		)






			@slow
			@require_tf
			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		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
						__snake_case			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
						__snake_case			=		image_classifier(a_      ,		candidate_labels=["cat", "plane", "remote"]							)
						self.assertEqual(
						    nested_simplify(a_							)      ,		[
						        {"score": 0.511, "label": "remote"},
						        {"score": 0.485, "label": "cat"},
						        {"score": 0.004, "label": "plane"},
						    ]      ,		)

						__snake_case			=		image_classifier([image] * 5      ,		candidate_labels=["cat", "plane", "remote"]      ,		batch_size=2							)
						self.assertEqual(
						    nested_simplify(a_							)      ,		[
						        [
						            {"score": 0.511, "label": "remote"},
						            {"score": 0.485, "label": "cat"},
						            {"score": 0.004, "label": "plane"},
						        ],
						    ]
						    * 5      ,		)











from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




