






"""simple docstring"""



import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 8    ):





  '''simple docstring'''





  lowercase							    =       ascii_letters + digits + punctuation
  return "".join(secrets.choice(__snake_case    ) for _ in range(__snake_case    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  int    ):





  '''simple docstring'''





  i -= len(__snake_case    )
  lowercase							    =       i // 3
  lowercase							    =       i % 3
  # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
  #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
  lowercase							    =       (
      chars_incl
      + random(__snake_case		,					quotient + remainder    )
      + random(__snake_case		,					__snake_case    )
      + random(__snake_case		,					__snake_case    )
  )
  lowercase							    =       list(__snake_case    )
  shuffle(__snake_case    )
  return "".join(__snake_case    )

  # random is a generalised function for letters, characters and numbers
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  int    ):





  '''simple docstring'''





  return "".join(secrets.choice(__snake_case    ) for _ in range(__snake_case    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  str    ):





  '''simple docstring'''





  pass  # Put your code here...
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  pass  # Put your code here...
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Dict    ):





  '''simple docstring'''





  pass  # Put your code here...
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  int = 8    ):





  '''simple docstring'''





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

  lowercase							    =       any(char in ascii_uppercase for char in password    )
  lowercase							    =       any(char in ascii_lowercase for char in password    )
  lowercase							    =       any(char in digits for char in password    )
  lowercase							    =       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 (    ):





  '''simple docstring'''





  lowercase							    =       int(input('Please indicate the max length of your password: '    ).strip()    )
  lowercase							    =       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()










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


