





'''simple docstring'''




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






def 			_lowerCAmelCase     (    __snake_case    :					int = 8     )	->	str:
     __A		:					Tuple						=			ascii_letters + digits + punctuation
     return "".join(secrets.choice(_snake_case     ) for _ in range(_snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					int     )	->	str:
     # Password Generator = full boot with random_number, random_letters, and
     # random_character FUNCTIONS
     # Put your code here...
     i -= len(_snake_case     )
     __A		:					int						=			i // 3
     __A		:					int						=			i % 3
     # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
     #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
     __A		:					List[str]						=			(
         chars_incl
         + random(_snake_case					,				quotient + remainder     )
         + random(_snake_case					,				_snake_case     )
         + random(_snake_case					,				_snake_case     )
     )
     __A		:					List[str]						=			list(_snake_case     )
     shuffle(_snake_case     )
     return "".join(_snake_case     )

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






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					int     )	->	str:
     return "".join(secrets.choice(_snake_case     ) for _ in range(_snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					Union[str, Any]     )	->	str:
     pass  # Put your code here...






def 			_lowerCAmelCase     (    __snake_case    :					Tuple					,				__snake_case    :					Union[str, Any]     )	->	str:
     pass  # Put your code here...






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Dict     )	->	str:
     pass  # Put your code here...






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					int = 8     )	->	bool:

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

     __A		:					List[Any]						=			any(char in ascii_uppercase for char in password     )
     __A		:					Tuple						=			any(char in ascii_lowercase for char in password     )
     __A		:					int						=			any(char in digits for char in password     )
     __A		:					Any						=			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 			_lowerCAmelCase     (    )	->	Any:
     __A		:					Tuple						=			int(input('Please indicate the max length of your password: '     ).strip()     )
     __A		:					Optional[int]						=			input(
         'Please indicate the characters that must be in your password: '     ).strip()
     print('Password generated:'					,				password_generator(_snake_case     )     )
     print(
         'Alternative Password generated:'					,				alternative_password_generator(_snake_case					,				_snake_case     )					,				)
     print('[If you are thinking of using this passsword, You better save it.]'     )


if __name__ == "__main__":
  main()




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__	:



    """simple docstring"""

    @staticmethod
    def     snake_case_      (    *__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Any      )   ->	Any:
       pass


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



 """simple docstring"""

 @require_torch
 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						)
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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      =       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					:					int      )   ->	Optional[int]:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						framework='''tf'''      )
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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      =       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					:					Optional[int]      )   ->	int:
    _A      =       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      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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      =       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					:					Optional[int]      )   ->	Dict:
    _A      =       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      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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      =       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			,						)



