


'''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 		_snake_case					:




							@staticmethod
							def 				snake_case__						(      *_lowerCamelCase							,							**_lowerCamelCase):
										pass


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




					@require_torch
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     pipeline(
								    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""							,							)
								UpperCAmelCase__  :					str        =     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""": 0.333, """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"""}],
								    ]							,							)

								UpperCAmelCase__  :					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 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     pipeline(
								    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""							,							framework="""tf""")
								UpperCAmelCase__  :					Union[str, Any]        =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
								UpperCAmelCase__  :					Union[str, Any]        =     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"""}]							,							)

								UpperCAmelCase__  :					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 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[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
								UpperCAmelCase__  :					str        =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
								UpperCAmelCase__  :					Dict        =     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"""},
								    ]							,							)

								UpperCAmelCase__  :					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							,							)




					@slow
					@require_tf
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					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
								UpperCAmelCase__  :					Union[str, Any]        =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
								UpperCAmelCase__  :					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"""},
								    ]							,							)

								UpperCAmelCase__  :					List[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							,							)



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__					):
			# if the collection is empty, returns empty
			if collection == []:
						return []

			# get some information about the collection
			UpperCAmelCase__  :					List[str]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     max(UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[Any]        =     min(UpperCamelCase__					)

			# create the counting array
			UpperCAmelCase__  :					Dict        =     coll_max + 1 - coll_min
			UpperCAmelCase__  :					Optional[int]        =     [0] * counting_arr_length

			# count how much a number appears in the collection
			for number in collection:
						counting_arr[number - coll_min] += 1

			# sum each position with it's predecessors. now, counting_arr[i] tells
			# us how many elements <= i has in the collection
			for i in range(1 ,      UpperCamelCase__					):
						UpperCAmelCase__  :					List[str]        =     counting_arr[i] + counting_arr[i - 1]

			# create the output collection
			UpperCAmelCase__  :					Tuple        =     [0] * coll_len

			# place the elements in the output, respecting the original order (stable
			# sort) from end to begin, updating counting_arr
			for i in reversed(range(0 ,      UpperCamelCase__					)					):
						UpperCAmelCase__  :					Any        =     collection[i]
						counting_arr[collection[i] - coll_min] -= 1

			return ordered







def     _UpperCamelCase      (		UpperCamelCase__					):
			return "".join([chr(UpperCamelCase__					) for i in counting_sort([ord(UpperCamelCase__					) for c in string]					)]					)


if __name__ == "__main__":
		# Test string sort
		assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

		__A	=input('Enter numbers separated by a comma:\n').strip()
		__A	=[int(item) for item in user_input.split(',')]
		print(counting_sort(unsorted))