
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      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      Optional[Any]:
										pass





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




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

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

							lowerCAmelCase_						=	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     )				->      Union[str, Any]:
							lowerCAmelCase_						=	pipeline(
							    task="zero-shot-image-classification"				,				model="openai/clip-vit-base-patch32"				,				)
							# This is an image of 2 cats with remotes and no planes
							lowerCAmelCase_						=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							lowerCAmelCase_						=	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"},
							    ]				,				)

							lowerCAmelCase_						=	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[Any]:
							lowerCAmelCase_						=	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
							lowerCAmelCase_						=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							lowerCAmelCase_						=	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"},
							    ]				,				)

							lowerCAmelCase_						=	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"""




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int = 8					) ->str:






	'''simple docstring'''






	a      :		List[Any]	=				ascii_letters + digits + punctuation
	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	i -= len(_lowercase					)
	a      :		List[str]	=				i // 3
	a      :		Any	=				i % 3
	# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
	#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
	a      :		int	=				(
	    chars_incl
	    + random(_lowercase    ,       quotient + remainder					)
	    + random(_lowercase    ,       _lowercase					)
	    + random(_lowercase    ,       _lowercase					)
	)
	a      :		List[str]	=				list(_lowercase					)
	shuffle(_lowercase					)
	return "".join(_lowercase					)

	# random is a generalised function for letters, characters and numbers






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






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






	'''simple docstring'''






	pass  # Put your code here...






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






	'''simple docstring'''






	pass  # Put your code here...






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






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int = 8					) ->bool:






	'''simple docstring'''






	if len(_lowercase					) < min_length:
		# Your Password must be at least 8 characters long
		return False

	a      :		List[str]	=				any(char in ascii_uppercase for char in password					)
	a      :		Optional[int]	=				any(char in ascii_lowercase for char in password					)
	a      :		List[str]	=				any(char in digits for char in password					)
	a      :		int	=				any(char in punctuation for char in password					)

	return upper and lower and num and spec_char
	# Passwords should contain UPPERCASE, lowerase
	# numbers, and special characters






def       _SCREAMING_SNAKE_CASE		(	) ->Union[str, Any]:






	'''simple docstring'''






	a      :		Dict	=				int(input("Please indicate the max length of your password: "					).strip()					)
	a      :		str	=				input(
	    "Please indicate the characters that must be in your password: "					).strip()
	print("Password generated:"    ,       password_generator(_lowercase					)					)
	print(
	    "Alternative Password generated:"    ,       alternative_password_generator(_lowercase    ,       _lowercase					)    ,       )
	print("[If you are thinking of using this passsword, You better save it.]"					)


if __name__ == "__main__":
		main()


