


'''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				:  List[str]      					=							logging.get_logger(__name__)  # pylint: disable=invalid-name





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def __init__(	self				:   Tuple       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   __snake_case				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       super().__init__()
       self.register_modules(
           vae=_SCREAMING_SNAKE_CASE       ,   text_encoder=_SCREAMING_SNAKE_CASE       ,   tokenizer=_SCREAMING_SNAKE_CASE       ,   unet=_SCREAMING_SNAKE_CASE       ,   scheduler=_SCREAMING_SNAKE_CASE       ,   safety_checker=_SCREAMING_SNAKE_CASE       ,   feature_extractor=_SCREAMING_SNAKE_CASE       ,   )





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   Optional[Any] = "auto"	):





       '''simple docstring'''






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





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       self.enable_attention_slicing(_SCREAMING_SNAKE_CASE	)





    @torch.no_grad()
    def __call__(	self				:   Union[str, Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[str] = 512       ,   __snake_case				:   Tuple = 512       ,   __snake_case				:   Union[str, Any] = 50       ,   __snake_case				:   Any = 7.5       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   int = 1       ,   __snake_case				:   int = 0.0       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   List[Any] = None       ,   __snake_case				:   Union[str, Any] = "pil"       ,   __snake_case				:   int = True       ,   __snake_case				:   Dict = None       ,   __snake_case				:   Tuple = 1       ,   __snake_case				:   Union[str, Any] = None       ,   **__snake_case				:   Union[str, Any]       ,   ):





       '''simple docstring'''






       if isinstance(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE	):
          UpperCAmelCase_							:      Union[str, Any]						=   1
       elif isinstance(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE	):
          UpperCAmelCase_							:      Union[str, Any]						=   len(_SCREAMING_SNAKE_CASE	)
       else:
          raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE	)}'''	)

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

       # get prompt text embeddings
       UpperCAmelCase_							:      Tuple						=   self.tokenizer(
           _SCREAMING_SNAKE_CASE       ,   padding='''max_length'''       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors='''pt'''       ,   )
       UpperCAmelCase_							:      int						=   text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          UpperCAmelCase_							:      str						=   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}'''	)
          UpperCAmelCase_							:      Dict						=   text_input_ids[:, : self.tokenizer.model_max_length]

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      List[str]						=   text_embeddings.shape
       UpperCAmelCase_							:      Optional[int]						=   text_embeddings.repeat(1       ,   _SCREAMING_SNAKE_CASE       ,   1	)
       UpperCAmelCase_							:      Optional[int]						=   text_embeddings.view(bs_embed * num_images_per_prompt       ,   _SCREAMING_SNAKE_CASE       ,   -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.
       UpperCAmelCase_							:      Union[str, Any]						=   guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
          UpperCAmelCase_							:      str						=   42
          if negative_prompt is None:
             UpperCAmelCase_							:      Tuple						=   ['''''']
          elif type(_SCREAMING_SNAKE_CASE	) is not type(_SCREAMING_SNAKE_CASE	):
             raise TypeError(
                 f'''`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE	)} !='''
                 f''' {type(_SCREAMING_SNAKE_CASE	)}.'''	)
          elif isinstance(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE	):
             UpperCAmelCase_							:      int						=   [negative_prompt]
          elif batch_size != len(_SCREAMING_SNAKE_CASE	):
             raise ValueError(
                 f'''`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE	)}, but `prompt`:'''
                 f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                 ''' the batch size of `prompt`.'''	)
          else:
             UpperCAmelCase_							:      Dict						=   negative_prompt

          UpperCAmelCase_							:      Dict						=   text_input_ids.shape[-1]
          UpperCAmelCase_							:      Optional[Any]						=   self.tokenizer(
              _SCREAMING_SNAKE_CASE       ,   padding='''max_length'''       ,   max_length=_SCREAMING_SNAKE_CASE       ,   truncation=_SCREAMING_SNAKE_CASE       ,   return_tensors='''pt'''       ,   )
          UpperCAmelCase_							:      str						=   self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          UpperCAmelCase_							:      List[str]						=   uncond_embeddings.shape[1]
          UpperCAmelCase_							:      Dict						=   uncond_embeddings.repeat(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   1	)
          UpperCAmelCase_							:      List[Any]						=   uncond_embeddings.view(batch_size * num_images_per_prompt       ,   _SCREAMING_SNAKE_CASE       ,   -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
          UpperCAmelCase_							:      Optional[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`.
       UpperCAmelCase_							:      Optional[int]						=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCAmelCase_							:      str						=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       UpperCAmelCase_							:      str						=   text_embeddings.dtype
       if latents is None:
          if self.device.type == "mps":
             # randn does not exist on mps
             UpperCAmelCase_							:      List[str]						=   torch.randn(
                 _SCREAMING_SNAKE_CASE       ,   generator=_SCREAMING_SNAKE_CASE       ,   device='''cpu'''       ,   dtype=_SCREAMING_SNAKE_CASE	).to(self.device	)
             UpperCAmelCase_							:      Union[str, Any]						=   torch.randn(_SCREAMING_SNAKE_CASE       ,   generator=_SCREAMING_SNAKE_CASE       ,   device='''cpu'''       ,   dtype=_SCREAMING_SNAKE_CASE	).to(
                 self.device	)
          else:
             UpperCAmelCase_							:      str						=   torch.randn(
                 _SCREAMING_SNAKE_CASE       ,   generator=_SCREAMING_SNAKE_CASE       ,   device=self.device       ,   dtype=_SCREAMING_SNAKE_CASE	)
             UpperCAmelCase_							:      str						=   torch.randn(_SCREAMING_SNAKE_CASE       ,   generator=_SCREAMING_SNAKE_CASE       ,   device=self.device       ,   dtype=_SCREAMING_SNAKE_CASE	)
       else:
          if latents_reference.shape != latents_shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
          UpperCAmelCase_							:      Optional[int]						=   latents_reference.to(self.device	)
          UpperCAmelCase_							:      List[str]						=   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
       UpperCAmelCase_							:      Optional[Any]						=   (latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCAmelCase_							:      Dict						=   (latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCAmelCase_							:      Dict						=   latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       UpperCAmelCase_							:      List[str]						=   latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       UpperCAmelCase_							:      Tuple						=   0 if dx < 0 else dx
       UpperCAmelCase_							:      Tuple						=   0 if dy < 0 else dy
       UpperCAmelCase_							:      List[Any]						=   max(-dx       ,   0	)
       UpperCAmelCase_							:      List[Any]						=   max(-dy       ,   0	)
       # import pdb
       # pdb.set_trace()
       UpperCAmelCase_							:      Optional[Any]						=   latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCAmelCase_							:      Dict						=   self.scheduler.timesteps.to(self.device	)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_							:      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]
       UpperCAmelCase_							:      Dict						=   '''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
       UpperCAmelCase_							:      Dict						=   {}
       if accepts_eta:
          UpperCAmelCase_							:      Optional[Any]						=   eta

       for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE	)	):
          # expand the latents if we are doing classifier free guidance
          UpperCAmelCase_							:      Optional[int]						=   torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
          UpperCAmelCase_							:      Any						=   self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE	)

          # predict the noise residual
          UpperCAmelCase_							:      List[Any]						=   self.unet(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE	).sample

          # perform guidance
          if do_classifier_free_guidance:
             UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   noise_pred.chunk(2	)
             UpperCAmelCase_							:      Dict						=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

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

       UpperCAmelCase_							:      str						=   1 / 0.18_215 * latents
       UpperCAmelCase_							:      List[str]						=   self.vae.decode(_SCREAMING_SNAKE_CASE	).sample

       UpperCAmelCase_							:      int						=   (image / 2 + 0.5).clamp(0       ,   1	)

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

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

       if output_type == "pil":
          UpperCAmelCase_							:      Any						=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE	)

       if not return_dict:
          return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE       ,   nsfw_content_detected=_SCREAMING_SNAKE_CASE	)



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))