


"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Union[str, Any]=3		,       UpperCAmelCase_							:      List[str]=30		,       UpperCAmelCase_							:      Union[str, Any]=400		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      Tuple=None		,       UpperCAmelCase_							:      Optional[int]=0.9		,       UpperCAmelCase_							:      int=None		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      Dict=[0.5, 0.5, 0.5]		,       UpperCAmelCase_							:      int=[0.5, 0.5, 0.5]		,       )					->int:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""shortest_edge""": 30}
														snake_case_           =		crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize_and_center_crop
														snake_case_           =		size
														snake_case_           =		crop_pct
														snake_case_           =		crop_size
														snake_case_           =		do_normalize
														snake_case_           =		image_mean
														snake_case_           =		image_std



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														return {
														    "size": self.size,
														    "do_resize_and_center_crop": self.do_resize_and_center_crop,
														    "crop_pct": self.crop_pct,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Dict							      =    PoolFormerImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		PoolFormerImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Any:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize_and_center_crop"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """crop_pct"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """image_mean"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """image_std"""       )       )



							def 		lowerCAmelCase       (				self							:      Any       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""shortest_edge""": 30}       )
														self.assertEqual(image_processor.crop_size		,       {"""height""": 30, """width""": 30}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42		,       crop_size=84       )
														self.assertEqual(image_processor.size		,       {"""shortest_edge""": 42}       )
														self.assertEqual(image_processor.crop_size		,       {"""height""": 84, """width""": 84}       )



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



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random PIL images
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       Image.Image       )

														# Test not batched input
														snake_case_           =		image_processing(image_inputs[0]		,       return_tensors="""pt"""       ).pixel_values

														self.assertEqual(
														    encoded_images.shape		,       (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )

														# Test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       ).pixel_values
														self.assertEqual(
														    encoded_images.shape		,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random numpy tensors
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_		,       numpify=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       np.ndarray       )

														# Test not batched input
														snake_case_           =		image_processing(image_inputs[0]		,       return_tensors="""pt"""       ).pixel_values
														self.assertEqual(
														    encoded_images.shape		,       (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )

														# Test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       ).pixel_values
														self.assertEqual(
														    encoded_images.shape		,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )



							def 		lowerCAmelCase       (				self							:      int       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random PyTorch tensors
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_		,       torchify=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       torch.Tensor       )

														# Test not batched input
														snake_case_           =		image_processing(image_inputs[0]		,       return_tensors="""pt"""       ).pixel_values
														self.assertEqual(
														    encoded_images.shape		,       (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )

														# Test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       ).pixel_values
														self.assertEqual(
														    encoded_images.shape		,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["""height"""],
														        self.image_processor_tester.crop_size["""width"""],
														    )		,       )







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







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







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

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

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

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		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 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



