
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput







class  _lowerCamelCase			(nn.Module					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=1.0    ):
            super().__init__()

            __snake_case						       =   cut_size
            __snake_case						       =   cut_power

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   pixel_values.shape[2:4]
            __snake_case						       =   min(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   min(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       self.cut_size    )
            __snake_case						       =   []
            for _ in range(SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   int(torch.rand([]    ) ** self.cut_power * (max_size - min_size) + min_size    )
                   __snake_case						       =   torch.randint(0							,       sideX - size + 1							,       ()    )
                   __snake_case						       =   torch.randint(0							,       sideY - size + 1							,       ()    )
                   __snake_case						       =   pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                   cutouts.append(F.adaptive_avg_poolad(SCREAMING_SNAKE_CASE_							,       self.cut_size    )    )
            return torch.cat(SCREAMING_SNAKE_CASE_    )





def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]					)       ->   List[Any]:
       __snake_case						       =   F.normalize(lowerCAmelCase__    ,dim=-1					)
       __snake_case						       =   F.normalize(lowerCAmelCase__    ,dim=-1					)
       return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)
def        __lowercase(							__snake_case				:		str    ,__snake_case				:		int					)       ->   Union[str, Any]:




       for param in model.parameters():
              __snake_case						       =   value







class  _lowerCamelCase			(lowercase_					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       ):
            super().__init__()
            self.register_modules(
                vae=SCREAMING_SNAKE_CASE_							,       text_encoder=SCREAMING_SNAKE_CASE_							,       clip_model=SCREAMING_SNAKE_CASE_							,       tokenizer=SCREAMING_SNAKE_CASE_							,       unet=SCREAMING_SNAKE_CASE_							,       scheduler=SCREAMING_SNAKE_CASE_							,       feature_extractor=SCREAMING_SNAKE_CASE_							,       )

            __snake_case						       =   transforms.Normalize(mean=feature_extractor.image_mean							,       std=feature_extractor.image_std    )
            __snake_case						       =   (
                feature_extractor.size
                if isinstance(feature_extractor.size							,       SCREAMING_SNAKE_CASE_    )
                else feature_extractor.size['shortest_edge']
            )
            __snake_case						       =   MakeCutouts(self.cut_out_size    )

            set_requires_grad(self.text_encoder							,       SCREAMING_SNAKE_CASE_    )
            set_requires_grad(self.clip_model							,       SCREAMING_SNAKE_CASE_    )

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

     def 						__lowerCamelCase				(  self    ):
            self.enable_attention_slicing(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            set_requires_grad(self.vae							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            set_requires_grad(self.vae							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            set_requires_grad(self.unet							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            set_requires_grad(self.unet							,       SCREAMING_SNAKE_CASE_    )

     @torch.enable_grad()
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=True							,       ):
            __snake_case						       =   latents.detach().requires_grad_()

            __snake_case						       =   self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            # predict the noise residual
            __snake_case						       =   self.unet(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       encoder_hidden_states=SCREAMING_SNAKE_CASE_    ).sample

            if isinstance(self.scheduler							,       (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
                   __snake_case						       =   self.scheduler.alphas_cumprod[timestep]
                   __snake_case						       =   1 - alpha_prod_t
                   # compute predicted original sample from predicted noise also called
                   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                   __snake_case						       =   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                   __snake_case						       =   torch.sqrt(SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   self.scheduler.sigmas[index]
                   __snake_case						       =   latents - sigma * noise_pred
            else:
                   raise ValueError(f'''scheduler type {type(self.scheduler    )} not supported'''    )

            __snake_case						       =   1 / self.vae.config.scaling_factor * sample
            __snake_case						       =   self.vae.decode(SCREAMING_SNAKE_CASE_    ).sample
            __snake_case						       =   (image / 2 + 0.5).clamp(0							,       1    )

            if use_cutouts:
                   __snake_case						       =   self.make_cutouts(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            else:
                   __snake_case						       =   transforms.Resize(self.cut_out_size    )(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   self.normalize(SCREAMING_SNAKE_CASE_    ).to(latents.dtype    )

            __snake_case						       =   self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   image_embeddings_clip / image_embeddings_clip.norm(p=2							,       dim=-1							,       keepdim=SCREAMING_SNAKE_CASE_    )

            if use_cutouts:
                   __snake_case						       =   spherical_dist_loss(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   dists.view([num_cutouts, sample.shape[0], -1]    )
                   __snake_case						       =   dists.sum(2    ).mean(0    ).sum() * clip_guidance_scale
            else:
                   __snake_case						       =   spherical_dist_loss(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ).mean() * clip_guidance_scale

            __snake_case						       =   -torch.autograd.grad(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )[0]

            if isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   latents.detach() + grads * (sigma**2)
                   __snake_case						       =   noise_pred_original
            else:
                   __snake_case						       =   noise_pred_original - torch.sqrt(SCREAMING_SNAKE_CASE_    ) * grads
            return noise_pred, latents

     @torch.no_grad()
     def __call__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 512							,       SCREAMING_SNAKE_CASE_ = 512							,       SCREAMING_SNAKE_CASE_ = 50							,       SCREAMING_SNAKE_CASE_ = 7.5							,       SCREAMING_SNAKE_CASE_ = 1							,       SCREAMING_SNAKE_CASE_ = 0.0							,       SCREAMING_SNAKE_CASE_ = 100							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 4							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = "pil"							,       SCREAMING_SNAKE_CASE_ = True							,       ):
            if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   1
            elif isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   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}.'''    )

            # get prompt text embeddings
            __snake_case						       =   self.tokenizer(
                SCREAMING_SNAKE_CASE_							,       padding='max_length'							,       max_length=self.tokenizer.model_max_length							,       truncation=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'							,       )
            __snake_case						       =   self.text_encoder(text_input.input_ids.to(self.device    )    )[0]
            # duplicate text embeddings for each generation per prompt
            __snake_case						       =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_							,       dim=0    )

            if clip_guidance_scale > 0:
                   if clip_prompt is not None:
                          __snake_case						       =   self.tokenizer(
                              SCREAMING_SNAKE_CASE_							,       padding='max_length'							,       max_length=self.tokenizer.model_max_length							,       truncation=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'							,       ).input_ids.to(self.device    )
                   else:
                          __snake_case						       =   text_input.input_ids.to(self.device    )
                   __snake_case						       =   self.clip_model.get_text_features(SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   text_embeddings_clip / text_embeddings_clip.norm(p=2							,       dim=-1							,       keepdim=SCREAMING_SNAKE_CASE_    )
                   # duplicate text embeddings clip for each generation per prompt
                   __snake_case						       =   text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE_							,       dim=0    )

            # 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						       =   guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                   __snake_case						       =   text_input.input_ids.shape[-1]
                   __snake_case						       =   self.tokenizer(['']							,       padding='max_length'							,       max_length=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   __snake_case						       =   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
                   # duplicate unconditional embeddings for each generation per prompt
                   __snake_case						       =   uncond_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_							,       dim=0    )

                   # 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						       =   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						       =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            __snake_case						       =   text_embeddings.dtype
            if latents is None:
                   if self.device.type == "mps":
                          # randn does not work reproducibly on mps
                          __snake_case						       =   torch.randn(SCREAMING_SNAKE_CASE_							,       generator=SCREAMING_SNAKE_CASE_							,       device='cpu'							,       dtype=SCREAMING_SNAKE_CASE_    ).to(
                              self.device    )
                   else:
                          __snake_case						       =   torch.randn(SCREAMING_SNAKE_CASE_							,       generator=SCREAMING_SNAKE_CASE_							,       device=self.device							,       dtype=SCREAMING_SNAKE_CASE_    )
            else:
                   if latents.shape != latents_shape:
                          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
                   __snake_case						       =   latents.to(self.device    )

            # set timesteps
            __snake_case						       =   'offset' in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
            __snake_case						       =   {}
            if accepts_offset:
                   __snake_case						       =   1

            self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

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

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

            # check if the scheduler accepts generator
            __snake_case						       =   'generator' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
            if accepts_generator:
                   __snake_case						       =   generator

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

                   # predict the noise residual
                   __snake_case						       =   self.unet(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       encoder_hidden_states=SCREAMING_SNAKE_CASE_    ).sample

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                          __snake_case						       =   noise_pred.chunk(2    )
                          __snake_case						       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                          __snake_case						       =   (
                              text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
                          )
                          __snake_case						       =   self.cond_fn(
                              SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       )

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

            # scale and decode the image latents with vae
            __snake_case						       =   1 / self.vae.config.scaling_factor * latents
            __snake_case						       =   self.vae.decode(SCREAMING_SNAKE_CASE_    ).sample

            __snake_case						       =   (image / 2 + 0.5).clamp(0							,       1    )
            __snake_case						       =   image.cpu().permute(0							,       2							,       3							,       1    ).numpy()

            if output_type == "pil":
                   __snake_case						       =   self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

            if not return_dict:
                   return (image, None)

            return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_							,       nsfw_content_detected=SCREAMING_SNAKE_CASE_    )





"""simple docstring"""







import unittest
from timeit import timeit

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					first_num if first_num >= second_num else second_num
  lowerCAmelCase_  :							Union[str, Any]								=					max_num
  while (common_mult % first_num > 0) or (common_mult % second_num > 0):
    common_mult += max_num
  return common_mult

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return b if a == 0 else greatest_common_divisor(b % a       ,  lowerCAmelCase__		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return first_num // greatest_common_divisor(lowerCAmelCase__       ,  lowerCAmelCase__		) * second_num

def 							UpperCamelCase_       (       )		->  Dict:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					(
      'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
  )
  print(
      'least_common_multiple_slow():'       ,  timeit('least_common_multiple_slow(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )
  print(
      'least_common_multiple_fast():'       ,  timeit('least_common_multiple_fast(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )







class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	(
          (10, 20),
          (13, 15),
          (4, 31),
          (10, 42),
          (43, 34),
          (5, 12),
          (12, 25),
          (10, 25),
          (6, 9),
      )
      _SCREAMING_SNAKE_CASE    							=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        for i, (first_num, second_num) in enumerate(self.test_inputs			):
          lowerCAmelCase_  :							Union[str, Any]								=					least_common_multiple_slow(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Any								=					least_common_multiple_fast(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          with self.subTest(i=SCREAMING_SNAKE_CASE_			):
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)


if __name__ == "__main__":
     benchmark()
     unittest.main()





