





"""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  __UpperCamelCase       :






									@staticmethod
									def 					__a     (     *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				List[str]:
										pass


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






							@require_torch
							def 					__a     (     self   )			->				Optional[int]:
								a   :	Optional[int]			=       pipeline(
								    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"   ,      )
								a   :	Optional[int]			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
								a   :	Optional[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": 0.333, "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"}],
								    ]   ,      )

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






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

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

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






							@slow
							@require_torch
							def 					__a     (     self   )			->				Any:
								a   :	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
								a   :	List[str]			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
								a   :	Any			=       image_classifier(lowerCAmelCase_   ,      candidate_labels=["cat", "plane", "remote"]   )

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

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






							@slow
							@require_tf
							def 					__a     (     self   )			->				Optional[int]:
								a   :	Optional[int]			=       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
								a   :	str			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
								a   :	int			=       image_classifier(lowerCAmelCase_   ,      candidate_labels=["cat", "plane", "remote"]   )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase_   )   ,      [
								        {"score": 0.511, "label": "remote"},
								        {"score": 0.485, "label": "cat"},
								        {"score": 0.004, "label": "plane"},
								    ]   ,      )

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









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


