





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					AutoencoderKL      ,				UpperCamelCase      :					CLIPTextModel      ,				UpperCamelCase      :					CLIPTokenizer      ,				UpperCamelCase      :					UNetaDConditionModel      ,				UpperCamelCase      :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,				UpperCamelCase      :					StableDiffusionSafetyChecker      ,				UpperCamelCase      :					CLIPImageProcessor      ,				):


    '''simple docstring'''





    super().__init__()
    self.register_modules(
        vae=UpperCamelCase      ,				text_encoder=UpperCamelCase      ,				tokenizer=UpperCamelCase      ,				unet=UpperCamelCase      ,				scheduler=UpperCamelCase      ,				safety_checker=UpperCamelCase      ,				feature_extractor=UpperCamelCase      ,				)







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Optional[Union[str, int]] = "auto"							):


    '''simple docstring'''





    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      _snake_case   :				int														=	self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    self.enable_attention_slicing(UpperCamelCase							)







  @torch.no_grad()
  def __call__(      self      :					Optional[int]      ,				UpperCamelCase      :					Union[str, List[str]]      ,				UpperCamelCase      :					int = 5_12      ,				UpperCamelCase      :					int = 5_12      ,				UpperCamelCase      :					int = 50      ,				UpperCamelCase      :					float = 7.5      ,				UpperCamelCase      :					Optional[Union[str, List[str]]] = None      ,				UpperCamelCase      :					Optional[int] = 1      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					Optional[torch.Generator] = None      ,				UpperCamelCase      :					Optional[torch.FloatTensor] = None      ,				UpperCamelCase      :					Optional[str] = "pil"      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,				UpperCamelCase      :					int = 1      ,				UpperCamelCase      :					Optional[torch.FloatTensor] = None      ,				**UpperCamelCase      :					Union[str, Any]      ,				):


    '''simple docstring'''





    if isinstance(UpperCamelCase      ,				UpperCamelCase							):
      _snake_case   :				int														=	1
    elif isinstance(UpperCamelCase      ,				UpperCamelCase							):
      _snake_case   :				str														=	len(UpperCamelCase							)
    else:
      raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase							)}"""							)

    if height % 8 != 0 or width % 8 != 0:
      raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""							)

    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(UpperCamelCase      ,				UpperCamelCase							) or callback_steps <= 0)
    ):
      raise ValueError(
          f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
          f""" {type(UpperCamelCase							)}."""							)

    # get prompt text embeddings
    _snake_case   :				List[Any]														=	self.tokenizer(
        UpperCamelCase      ,				padding='max_length'      ,				max_length=self.tokenizer.model_max_length      ,				return_tensors='pt'      ,				)
    _snake_case   :				Tuple														=	text_inputs.input_ids

    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
      _snake_case   :				List[Any]														=	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
      logger.warning(
          'The following part of your input was truncated because CLIP can only handle sequences up to'
          f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
      _snake_case   :				str														=	text_input_ids[:, : self.tokenizer.model_max_length]

    if text_embeddings is None:
      _snake_case   :				Tuple														=	self.text_encoder(text_input_ids.to(self.device							)							)[0]

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _snake_case							,					_snake_case							,					_snake_case   :				str														=	text_embeddings.shape
    _snake_case   :				List[Any]														=	text_embeddings.repeat(1      ,				UpperCamelCase      ,				1							)
    _snake_case   :				int														=	text_embeddings.view(bs_embed * num_images_per_prompt      ,				UpperCamelCase      ,				-1							)

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    _snake_case   :				Dict														=	guidance_scale > 1.0
    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance:
      _snake_case   :				List[str]
      if negative_prompt is None:
        _snake_case   :				str														=	['']
      elif type(UpperCamelCase							) is not type(UpperCamelCase							):
        raise TypeError(
            f"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase							)} !="""
            f""" {type(UpperCamelCase							)}."""							)
      elif isinstance(UpperCamelCase      ,				UpperCamelCase							):
        _snake_case   :				Optional[int]														=	[negative_prompt]
      elif batch_size != len(UpperCamelCase							):
        raise ValueError(
            f"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase							)}, but `prompt`:"""
            f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
            ' the batch size of `prompt`.'							)
      else:
        _snake_case   :				str														=	negative_prompt

      _snake_case   :				List[Any]														=	text_input_ids.shape[-1]
      _snake_case   :				List[Any]														=	self.tokenizer(
          UpperCamelCase      ,				padding='max_length'      ,				max_length=UpperCamelCase      ,				truncation=UpperCamelCase      ,				return_tensors='pt'      ,				)
      _snake_case   :				Optional[int]														=	self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

      # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
      _snake_case   :				Optional[Any]														=	uncond_embeddings.shape[1]
      _snake_case   :				List[str]														=	uncond_embeddings.repeat(UpperCamelCase      ,				UpperCamelCase      ,				1							)
      _snake_case   :				str														=	uncond_embeddings.view(batch_size * num_images_per_prompt      ,				UpperCamelCase      ,				-1							)

      # For classifier free guidance, we need to do two forward passes.
      # Here we concatenate the unconditional and text embeddings into a single batch
      # to avoid doing two forward passes
      _snake_case   :				Any														=	torch.cat([uncond_embeddings, text_embeddings]							)

    # get the initial random noise unless the user supplied it

    # Unlike in other pipelines, latents need to be generated in the target device
    # for 1-to-1 results reproducibility with the CompVis implementation.
    # However this currently doesn't work in `mps`.
    _snake_case   :				Optional[Any]														=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
    _snake_case   :				List[Any]														=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
    _snake_case   :				List[Any]														=	text_embeddings.dtype
    if latents is None:
      if self.device.type == "mps":
        # randn does not exist on mps
        _snake_case   :				Any														=	torch.randn(
            UpperCamelCase      ,				generator=UpperCamelCase      ,				device='cpu'      ,				dtype=UpperCamelCase							).to(self.device							)
        _snake_case   :				List[str]														=	torch.randn(UpperCamelCase      ,				generator=UpperCamelCase      ,				device='cpu'      ,				dtype=UpperCamelCase							).to(
            self.device							)
      else:
        _snake_case   :				str														=	torch.randn(
            UpperCamelCase      ,				generator=UpperCamelCase      ,				device=self.device      ,				dtype=UpperCamelCase							)
        _snake_case   :				List[str]														=	torch.randn(UpperCamelCase      ,				generator=UpperCamelCase      ,				device=self.device      ,				dtype=UpperCamelCase							)
    else:
      if latents_reference.shape != latents_shape:
        raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
      _snake_case   :				Optional[Any]														=	latents_reference.to(self.device							)
      _snake_case   :				Optional[Any]														=	latents.to(self.device							)

    # This is the key part of the pipeline where we
    # try to ensure that the generated images w/ the same seed
    # but different sizes actually result in similar images
    _snake_case   :				int														=	(latents_shape[3] - latents_shape_reference[3]) // 2
    _snake_case   :				List[str]														=	(latents_shape[2] - latents_shape_reference[2]) // 2
    _snake_case   :				List[Any]														=	latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
    _snake_case   :				Tuple														=	latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
    _snake_case   :				Tuple														=	0 if dx < 0 else dx
    _snake_case   :				Optional[int]														=	0 if dy < 0 else dy
    _snake_case   :				Union[str, Any]														=	max(-dx      ,				0							)
    _snake_case   :				List[str]														=	max(-dy      ,				0							)
    # import pdb
    # pdb.set_trace()
    _snake_case   :				Tuple														=	latents_reference[:, :, dy : dy + h, dx : dx + w]

    # set timesteps
    self.scheduler.set_timesteps(UpperCamelCase							)

    # Some schedulers like PNDM have timesteps as arrays
    # It's more optimized to move all timesteps to correct device beforehand
    _snake_case   :				Union[str, Any]														=	self.scheduler.timesteps.to(self.device							)

    # scale the initial noise by the standard deviation required by the scheduler
    _snake_case   :				Optional[Any]														=	latents * self.scheduler.init_noise_sigma

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    _snake_case   :				List[str]														=	'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
    _snake_case   :				str														=	{}
    if accepts_eta:
      _snake_case   :				List[str]														=	eta

    for i, t in enumerate(self.progress_bar(UpperCamelCase							)							):
      # expand the latents if we are doing classifier free guidance
      _snake_case   :				List[str]														=	torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
      _snake_case   :				Union[str, Any]														=	self.scheduler.scale_model_input(UpperCamelCase      ,				UpperCamelCase							)

      # predict the noise residual
      _snake_case   :				Optional[int]														=	self.unet(UpperCamelCase      ,				UpperCamelCase      ,				encoder_hidden_states=UpperCamelCase							).sample

      # perform guidance
      if do_classifier_free_guidance:
        _snake_case							,					_snake_case   :				Union[str, Any]														=	noise_pred.chunk(2							)
        _snake_case   :				str														=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

      # compute the previous noisy sample x_t -> x_t-1
      _snake_case   :				Optional[int]														=	self.scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample

      # call the callback, if provided
      if callback is not None and i % callback_steps == 0:
        callback(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				List[Any]														=	1 / 0.1_82_15 * latents
    _snake_case   :				Any														=	self.vae.decode(UpperCamelCase							).sample

    _snake_case   :				Any														=	(image / 2 + 0.5).clamp(0      ,				1							)

    # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
    _snake_case   :				Dict														=	image.cpu().permute(0      ,				2      ,				3      ,				1							).float().numpy()

    if self.safety_checker is not None:
      _snake_case   :				Tuple														=	self.feature_extractor(self.numpy_to_pil(UpperCamelCase							)      ,				return_tensors='pt'							).to(
          self.device							)
      _snake_case							,					_snake_case   :				Optional[int]														=	self.safety_checker(
          images=UpperCamelCase      ,				clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
    else:
      _snake_case   :				Union[str, Any]														=	None

    if output_type == "pil":
      _snake_case   :				Any														=	self.numpy_to_pil(UpperCamelCase							)

    if not return_dict:
      return (image, has_nsfw_concept)

    return StableDiffusionPipelineOutput(images=UpperCamelCase      ,				nsfw_content_detected=UpperCamelCase							)











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




