




"""simple docstring"""

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a         =      logging.get_logger(__name__)  # pylint: disable=invalid-name

a         =      "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[Any]    ,  _snake_case							: str=8							)   -> Dict:






      '''simple docstring'''


      _A        =       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A        =       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class     lowercase_	(					_UpperCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

       self.register_modules(
           unet=lowercase_   ,							scheduler=lowercase_   ,							movq=lowercase_   ,							)
       _A        =       2 ** (len(self.movq.config.block_out_channels		) - 1)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Tuple		):
       if latents is None:
             _A        =       randn_tensor(lowercase_   ,							generator=lowercase_   ,							device=lowercase_   ,							dtype=lowercase_		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(lowercase_		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	List[str]=0		):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('Please install accelerate via `pip install accelerate`'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

       _A        =       [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(lowercase_   ,							lowercase_		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Dict=0		):
       if is_accelerate_available() and is_accelerate_version('>='   ,							'0.17.0.dev0'		):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

       if self.device.type != "cpu":
             self.to('cpu'   ,							silence_dtype_warnings=lowercase_		)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _A        =       None
       for cpu_offloaded_model in [self.unet, self.movq]:
             _A        =       cpu_offload_with_hook(lowercase_   ,							lowercase_   ,							prev_module_hook=lowercase_		)

       # We'll offload the last model manually.
       _A        =       hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	Dict		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(lowercase_   ,							'_hf_hook'		)
                 and hasattr(module._hf_hook   ,							'execution_device'		)
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device		)
       return self.device



 @torch.no_grad()
 @replace_example_docstring(lowercase_		)
 def __call__(			self	:	int   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

       if isinstance(lowercase_   ,							lowercase_		):
             _A        =       torch.cat(lowercase_   ,							dim=0		)
       _A        =       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(lowercase_   ,							lowercase_		):
             _A        =       torch.cat(lowercase_   ,							dim=0		)

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(lowercase_   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(lowercase_   ,							dim=0		)

       _A        =       torch.cat([negative_image_embeds, image_embeds]   ,							dim=0		).to(dtype=self.unet.dtype   ,							device=lowercase_		)

       self.scheduler.set_timesteps(lowercase_   ,							device=lowercase_		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A        =       downscale_height_and_width(lowercase_   ,							lowercase_   ,							self.movq_scale_factor		)

       # create initial latent
       _A        =       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)   ,							image_embeds.dtype   ,							lowercase_   ,							lowercase_   ,							lowercase_   ,							self.scheduler   ,							)

       for i, t in enumerate(self.progress_bar(lowercase_		)		):
             # expand the latents if we are doing classifier free guidance
             _A        =       torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

             _A        =       {"""image_embeds""": image_embeds}
             _A        =       self.unet(
                 sample=lowercase_   ,							timestep=lowercase_   ,							encoder_hidden_states=lowercase_   ,							added_cond_kwargs=lowercase_   ,							return_dict=lowercase_   ,							)[0]

             if do_classifier_free_guidance:
                   _A        =       noise_pred.split(latents.shape[1]   ,							dim=1		)
                   _A        =       noise_pred.chunk(2		)
                   _A        =       variance_pred.chunk(2		)
                   _A        =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _A        =       torch.cat([noise_pred, variance_pred_text]   ,							dim=1		)

             if not (
                 hasattr(self.scheduler.config   ,							'variance_type'		)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   _A        =       noise_pred.split(latents.shape[1]   ,							dim=1		)

             # compute the previous noisy sample x_t -> x_t-1
             _A        =       self.scheduler.step(
                 lowercase_   ,							lowercase_   ,							lowercase_   ,							generator=lowercase_   ,							)[0]
       # post-processing
       _A        =       self.movq.decode(lowercase_   ,							force_not_quantize=lowercase_		)["""sample"""]

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

       if output_type in ["np", "pil"]:
             _A        =       image * 0.5 + 0.5
             _A        =       image.clamp(0   ,							1		)
             _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		).float().numpy()

       if output_type == "pil":
             _A        =       self.numpy_to_pil(lowercase_		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=lowercase_		)







"""simple docstring"""

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






def 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


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

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






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






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






      '''simple docstring'''


      pass  # Put your code here...






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






      '''simple docstring'''


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

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       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 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


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

