
'''simple docstring'''






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


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

class 			UpperCAmelCase__    (							A			):



    def __init__(						self   :							Dict,__A   :							AutoencoderKL,__A   :							CLIPTextModel,__A   :							CLIPTokenizer,__A   :							UNetaDConditionModel,__A   :							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],__A   :							StableDiffusionSafetyChecker,__A   :							CLIPImageProcessor,):
          super().__init__()
          self.register_modules(
              vae=__A,text_encoder=__A,tokenizer=__A,unet=__A,scheduler=__A,safety_checker=__A,feature_extractor=__A,)



    def 	lowerCamelCase_	(						self   :							str,__A   :							Optional[Union[str, int]] = "auto"  ):
          if slice_size == "auto":
                # half the attention head size is usually a good trade-off between
                # speed and memory
                _lowerCamelCase					: Tuple		    = self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          self.enable_attention_slicing(__A  )



    @torch.no_grad()
    def __call__(						self   :							List[Any],__A   :							Union[str, List[str]],__A   :							int = 5_1_2,__A   :							int = 5_1_2,__A   :							int = 5_0,__A   :							float = 7.5,__A   :							Optional[Union[str, List[str]]] = None,__A   :							Optional[int] = 1,__A   :							float = 0.0,__A   :							Optional[torch.Generator] = None,__A   :							Optional[torch.FloatTensor] = None,__A   :							Optional[str] = "pil",__A   :							bool = True,__A   :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None,__A   :							int = 1,__A   :							Optional[torch.FloatTensor] = None,**__A   :							str,):

          if isinstance(__A,__A  ):
                _lowerCamelCase					: Optional[int]		    = 1
          elif isinstance(__A,__A  ):
                _lowerCamelCase					: Optional[int]		    = len(__A  )
          else:
                raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__A  )}'  )

          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(__A,__A  ) or callback_steps <= 0)
          ):
                raise ValueError(
                    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                    f' {type(__A  )}.'  )

          # get prompt text embeddings
          _lowerCamelCase					: Optional[Any]		    = self.tokenizer(
              __A,padding="max_length",max_length=self.tokenizer.model_max_length,return_tensors="pt",)
          _lowerCamelCase					: int		    = text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                _lowerCamelCase					: Optional[int]		    = 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}'  )
                _lowerCamelCase					: Union[str, Any]		    = text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
                _lowerCamelCase					: List[Any]		    = self.text_encoder(text_input_ids.to(self.device  )  )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: Union[str, Any]		    = text_embeddings.shape
          _lowerCamelCase					: Optional[Any]		    = text_embeddings.repeat(1,__A,1  )
          _lowerCamelCase					: Any		    = text_embeddings.view(bs_embed * num_images_per_prompt,__A,-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.
          _lowerCamelCase					: str		    = guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
                _lowerCamelCase					: List[str]
                if negative_prompt is None:
                      _lowerCamelCase					: Any		    = [""]
                elif type(__A  ) is not type(__A  ):
                      raise TypeError(
                          f'`negative_prompt` should be the same type to `prompt`, but got {type(__A  )} !='
                          f' {type(__A  )}.'  )
                elif isinstance(__A,__A  ):
                      _lowerCamelCase					: Optional[Any]		    = [negative_prompt]
                elif batch_size != len(__A  ):
                      raise ValueError(
                          f'`negative_prompt`: {negative_prompt} has batch size {len(__A  )}, but `prompt`:'
                          f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                          " the batch size of `prompt`."  )
                else:
                      _lowerCamelCase					: List[Any]		    = negative_prompt

                _lowerCamelCase					: Any		    = text_input_ids.shape[-1]
                _lowerCamelCase					: Optional[Any]		    = self.tokenizer(
                    __A,padding="max_length",max_length=__A,truncation=__A,return_tensors="pt",)
                _lowerCamelCase					: List[str]		    = self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                _lowerCamelCase					: Optional[Any]		    = uncond_embeddings.shape[1]
                _lowerCamelCase					: Union[str, Any]		    = uncond_embeddings.repeat(__A,__A,1  )
                _lowerCamelCase					: Optional[int]		    = uncond_embeddings.view(batch_size * num_images_per_prompt,__A,-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
                _lowerCamelCase					: List[str]		    = 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`.
          _lowerCamelCase					: Union[str, Any]		    = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          _lowerCamelCase					: List[Any]		    = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          _lowerCamelCase					: Optional[Any]		    = text_embeddings.dtype
          if latents is None:
                if self.device.type == "mps":
                      # randn does not exist on mps
                      _lowerCamelCase					: List[Any]		    = torch.randn(
                          __A,generator=__A,device="cpu",dtype=__A  ).to(self.device  )
                      _lowerCamelCase					: List[Any]		    = torch.randn(__A,generator=__A,device="cpu",dtype=__A  ).to(
                          self.device  )
                else:
                      _lowerCamelCase					: Any		    = torch.randn(
                          __A,generator=__A,device=self.device,dtype=__A  )
                      _lowerCamelCase					: Any		    = torch.randn(__A,generator=__A,device=self.device,dtype=__A  )
          else:
                if latents_reference.shape != latents_shape:
                      raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'  )
                _lowerCamelCase					: Tuple		    = latents_reference.to(self.device  )
                _lowerCamelCase					: Union[str, 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
          _lowerCamelCase					: str		    = (latents_shape[3] - latents_shape_reference[3]) // 2
          _lowerCamelCase					: Tuple		    = (latents_shape[2] - latents_shape_reference[2]) // 2
          _lowerCamelCase					: List[Any]		    = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          _lowerCamelCase					: Optional[Any]		    = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          _lowerCamelCase					: int		    = 0 if dx < 0 else dx
          _lowerCamelCase					: Dict		    = 0 if dy < 0 else dy
          _lowerCamelCase					: Optional[Any]		    = max(-dx,0  )
          _lowerCamelCase					: List[Any]		    = max(-dy,0  )
          # import pdb
          # pdb.set_trace()
          _lowerCamelCase					: List[Any]		    = latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(__A  )

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

          # scale the initial noise by the standard deviation required by the scheduler
          _lowerCamelCase					: 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]
          _lowerCamelCase					: List[str]		    = "eta" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
          _lowerCamelCase					: int		    = {}
          if accepts_eta:
                _lowerCamelCase					: List[str]		    = eta

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

                # predict the noise residual
                _lowerCamelCase					: Any		    = self.unet(__A,__A,encoder_hidden_states=__A  ).sample

                # perform guidance
                if do_classifier_free_guidance:
                      _lowerCamelCase    ,		_lowerCamelCase					: Dict		    = noise_pred.chunk(2  )
                      _lowerCamelCase					: str		    = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                _lowerCamelCase					: Dict		    = self.scheduler.step(__A,__A,__A,**__A  ).prev_sample

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

          _lowerCamelCase					: Any		    = 1 / 0.18215 * latents
          _lowerCamelCase					: List[str]		    = self.vae.decode(__A  ).sample

          _lowerCamelCase					: Union[str, 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
          _lowerCamelCase					: List[Any]		    = image.cpu().permute(0,2,3,1  ).float().numpy()

          if self.safety_checker is not None:
                _lowerCamelCase					: List[Any]		    = self.feature_extractor(self.numpy_to_pil(__A  ),return_tensors="pt"  ).to(
                    self.device  )
                _lowerCamelCase    ,		_lowerCamelCase					: str		    = self.safety_checker(
                    images=__A,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype  )  )
          else:
                _lowerCamelCase					: Union[str, Any]		    = None

          if output_type == "pil":
                _lowerCamelCase					: Any		    = self.numpy_to_pil(__A  )

          if not return_dict:
                return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=__A,nsfw_content_detected=__A  )

'''simple docstring'''
import numpy as np




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return 1 / (1 + np.exp(-vector			))




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return vector * sigmoid(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


