





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


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

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   AutoencoderKL						,lowerCamelCase__						:   CLIPTextModel						,lowerCamelCase__						:   CLIPTokenizer						,lowerCamelCase__						:   UNetaDConditionModel						,lowerCamelCase__						:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,lowerCamelCase__						:   StableDiffusionSafetyChecker						,lowerCamelCase__						:   CLIPImageProcessor						,)					->		List[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   Optional[Union[str, int]] = "auto"	)					->		Optional[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Optional[Any]:




           '''simple docstring'''


           self.enable_attention_slicing(lowerCamelCase__	)






     @torch.no_grad()
     def __call__(							self						:   Dict						,lowerCamelCase__						:   Union[str, List[str]]						,lowerCamelCase__						:   int = 512						,lowerCamelCase__						:   int = 512						,lowerCamelCase__						:   int = 50						,lowerCamelCase__						:   float = 7.5						,lowerCamelCase__						:   Optional[Union[str, List[str]]] = None						,lowerCamelCase__						:   Optional[int] = 1						,lowerCamelCase__						:   float = 0.0						,lowerCamelCase__						:   Optional[torch.Generator] = None						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,lowerCamelCase__						:   Optional[str] = "pil"						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,**lowerCamelCase__						:   Optional[Any]						,)					->		Optional[int]:




           '''simple docstring'''


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

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

           # get prompt text embeddings
           SCREAMING_SNAKE_CASE		=						self.tokenizer(
               lowerCamelCase__						,padding="""max_length"""						,max_length=self.tokenizer.model_max_length						,return_tensors="""pt"""						,)
           SCREAMING_SNAKE_CASE		=						text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 SCREAMING_SNAKE_CASE		=						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}"""	)
                 SCREAMING_SNAKE_CASE		=						text_input_ids[:, : self.tokenizer.model_max_length]

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

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

                 SCREAMING_SNAKE_CASE		=						text_input_ids.shape[-1]
                 SCREAMING_SNAKE_CASE		=						self.tokenizer(
                     lowerCamelCase__						,padding="""max_length"""						,max_length=lowerCamelCase__						,truncation=lowerCamelCase__						,return_tensors="""pt"""						,)
                 SCREAMING_SNAKE_CASE		=						self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

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

           # set timesteps
           self.scheduler.set_timesteps(lowerCamelCase__	)

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

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

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

                 # predict the noise residual
                 SCREAMING_SNAKE_CASE		=						self.unet(lowerCamelCase__						,lowerCamelCase__						,encoder_hidden_states=lowerCamelCase__	).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 SCREAMING_SNAKE_CASE		=						self.scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	).prev_sample

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

           SCREAMING_SNAKE_CASE		=						1 / 0.18215 * latents
           SCREAMING_SNAKE_CASE		=						self.vae.decode(lowerCamelCase__	).sample

           SCREAMING_SNAKE_CASE		=						(image / 2 + 0.5).clamp(0						,1	)

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

           if self.safety_checker is not None:
                 SCREAMING_SNAKE_CASE		=						self.feature_extractor(self.numpy_to_pil(lowerCamelCase__	)						,return_tensors="""pt"""	).to(
                     self.device	)
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.safety_checker(
                     images=lowerCamelCase__						,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
           else:
                 SCREAMING_SNAKE_CASE		=						None

           if output_type == "pil":
                 SCREAMING_SNAKE_CASE		=						self.numpy_to_pil(lowerCamelCase__	)

           if not return_dict:
                 return (image, has_nsfw_concept)

           return StableDiffusionPipelineOutput(images=lowerCamelCase__						,nsfw_content_detected=lowerCamelCase__	)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


