



'''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 				lowercase__      :





						@staticmethod
						def       UpperCamelCase_    (					*lowerCamelCase__   :							Dict					,**lowerCamelCase__   :							Optional[Any]					):







												'''simple docstring'''
												pass






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





	@require_torch
	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'					,)
							_UpperCamelCase   :						Union[str, Any]												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							_UpperCamelCase   :						int												=  image_classifier(lowerCamelCase__					,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(lowerCamelCase__					)					,[
							        [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}],
							        [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'c'}, {'score': 0.3_3_3, 'label': 'b'}],
							    ]					,)

							_UpperCamelCase   :						Optional[int]												=  image_classifier([image] * 5					,candidate_labels=['A', 'B', 'C']					,batch_size=2					)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							    ]					,)





	@require_tf
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'					,framework='tf'					)
							_UpperCamelCase   :						Tuple												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							_UpperCamelCase   :						List[Any]												=  image_classifier(lowerCamelCase__					,candidate_labels=['a', 'b', 'c']					)

							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}]					,)

							_UpperCamelCase   :						Union[str, Any]												=  image_classifier([image] * 5					,candidate_labels=['A', 'B', 'C']					,batch_size=2					)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							        [
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							            {'score': 0.3_3_3, 'label': ANY(lowerCamelCase__					)},
							        ],
							    ]					,)





	@slow
	@require_torch
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  pipeline(
							    task='zero-shot-image-classification'					,model='openai/clip-vit-base-patch32'					,)
							# This is an image of 2 cats with remotes and no planes
							_UpperCamelCase   :						int												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							_UpperCamelCase   :						Union[str, Any]												=  image_classifier(lowerCamelCase__					,candidate_labels=['cat', 'plane', 'remote']					)

							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        {'score': 0.5_1_1, 'label': 'remote'},
							        {'score': 0.4_8_5, 'label': 'cat'},
							        {'score': 0.0_0_4, 'label': 'plane'},
							    ]					,)

							_UpperCamelCase   :						List[str]												=  image_classifier([image] * 5					,candidate_labels=['cat', 'plane', 'remote']					,batch_size=2					)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        [
							            {'score': 0.5_1_1, 'label': 'remote'},
							            {'score': 0.4_8_5, 'label': 'cat'},
							            {'score': 0.0_0_4, 'label': 'plane'},
							        ],
							    ]
							    * 5					,)





	@slow
	@require_tf
	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  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
							_UpperCamelCase   :						int												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							_UpperCamelCase   :						Optional[int]												=  image_classifier(lowerCamelCase__					,candidate_labels=['cat', 'plane', 'remote']					)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        {'score': 0.5_1_1, 'label': 'remote'},
							        {'score': 0.4_8_5, 'label': 'cat'},
							        {'score': 0.0_0_4, 'label': 'plane'},
							    ]					,)

							_UpperCamelCase   :						List[Any]												=  image_classifier([image] * 5					,candidate_labels=['cat', 'plane', 'remote']					,batch_size=2					)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__					)					,[
							        [
							            {'score': 0.5_1_1, 'label': 'remote'},
							            {'score': 0.4_8_5, 'label': 'cat'},
							            {'score': 0.0_0_4, 'label': 'plane'},
							        ],
							    ]
							    * 5					,)







'''simple docstring'''
from math import ceil






def A__       (	UpperCAmelCase_ = 1_0_0_1				):
						_UpperCamelCase   :						int												=  1

						for i in range(1    ,    int(ceil(n / 2.0				)				)				):
												_UpperCamelCase   :						Dict												=  2 * i + 1
												_UpperCamelCase   :						Tuple												=  2 * i
												_UpperCamelCase   :						Any												=  total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															snake_case_					:      Tuple    			=    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')


