


'''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 		lowerCAmelCase__	:




									@staticmethod
									def 							lowerCAmelCase__			(						*lowerCamelCase__     :   int		,     **lowerCamelCase__     :   Union[str, Any]       )      ->str:



											'''simple docstring'''







											pass






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




			@require_torch
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					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						:				List[Any]	    =					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						:				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 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					pipeline(
					    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"		,     framework="tf"       )
					_UpperCAmelCase						:				List[str]	    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
					_UpperCAmelCase						:				Dict	    =					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						:				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__       )},
					        ],
					    ]		,     )




			@slow
			@require_torch
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					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						:				Optional[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[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		,     )






			@slow
			@require_tf
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[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
					_UpperCAmelCase						:				List[Any]	    =					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						:				Optional[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'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')