





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 				SCREAMING_SNAKE_CASE   (nn.Module      ):


   def __init__(    self			:				Dict			,     a			:				Optional[Any]			,     a			:				Dict=1.0       )->   Optional[Any]:





     """simple docstring"""







     super().__init__()

     lowercase__				=  cut_size
     lowercase__				=  cut_power


   def        SCREAMING_SNAKE_CASE_							(    self			:				Union[str, Any]			,     a			:				Union[str, Any]			,     a			:				str       )->   Union[str, Any]:





     """simple docstring"""







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






def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)				->   List[str]:
  lowercase__				=  F.normalize(lowerCamelCase_		,						dim=-1		)
  lowercase__				=  F.normalize(lowerCamelCase_		,						dim=-1		)
  return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)




def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)				->   Dict:
  for param in model.parameters():
    lowercase__				=  value


class 				SCREAMING_SNAKE_CASE   (lowerCAmelCase__      ):


   def __init__(    self			:				Any			,     a			:				Any			,     a			:				Optional[int]			,     a			:				List[str]			,     a			:				Tuple			,     a			:				List[Any]			,     a			:				List[str]			,     a			:				Optional[int]			,     )->   Union[str, Any]:





     """simple docstring"""







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

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

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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Tuple			,     a			:				Union[str, Any] = "auto"       )->   Optional[int]:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[Any]       )->   str:





     """simple docstring"""







     self.enable_attention_slicing(_lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Union[str, Any]       )->   Dict:





     """simple docstring"""







     set_requires_grad(self.vae			,     _lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]       )->   int:





     """simple docstring"""







     set_requires_grad(self.vae			,     _lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				int       )->   List[str]:





     """simple docstring"""







     set_requires_grad(self.unet			,     _lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[Any]       )->   List[Any]:





     """simple docstring"""







     set_requires_grad(self.unet			,     _lowerCamelCase       )


   @torch.enable_grad()
   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict			,     a			:				int			,     a			:				Optional[int]			,     a			:				List[str]			,     a			:				int			,     a			:				Any			,     a			:				List[str]			,     a			:				Union[str, Any]			,     a			:				List[str]			,     a			:				Optional[int]=True			,     )->   Optional[int]:





     """simple docstring"""







     lowercase__				=  latents.detach().requires_grad_()

     lowercase__				=  self.scheduler.scale_model_input(_lowerCamelCase			,     _lowerCamelCase       )

     # predict the noise residual
     lowercase__				=  self.unet(_lowerCamelCase			,     _lowerCamelCase			,     encoder_hidden_states=_lowerCamelCase       ).sample

     if isinstance(self.scheduler			,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)       ):
       lowercase__				=  self.scheduler.alphas_cumprod[timestep]
       lowercase__				=  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
       lowercase__				=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

     lowercase__				=  -torch.autograd.grad(_lowerCamelCase			,     _lowerCamelCase       )[0]

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


   @torch.no_grad()
   def __call__(    self			:				int			,     a			:				List[str]			,     a			:				str = 512			,     a			:				int = 512			,     a			:				Optional[int] = 50			,     a			:				Tuple = 7.5			,     a			:				int = 1			,     a			:				int = 0.0			,     a			:				List[str] = 100			,     a			:				Optional[Any] = None			,     a			:				str = 4			,     a			:				List[str] = True			,     a			:				Optional[Any] = None			,     a			:				List[Any] = None			,     a			:				Union[str, Any] = "pil"			,     a			:				Dict = True			,     )->   int:





     """simple docstring"""







     if isinstance(_lowerCamelCase			,     _lowerCamelCase       ):
       lowercase__				=  1
     elif isinstance(_lowerCamelCase			,     _lowerCamelCase       ):
       lowercase__				=  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}."""       )

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

     if clip_guidance_scale > 0:
       if clip_prompt is not None:
         lowercase__				=  self.tokenizer(
             _lowerCamelCase			,     padding='max_length'			,     max_length=self.tokenizer.model_max_length			,     truncation=_lowerCamelCase			,     return_tensors='pt'			,     ).input_ids.to(self.device       )
       else:
         lowercase__				=  text_input.input_ids.to(self.device       )
       lowercase__				=  self.clip_model.get_text_features(_lowerCamelCase       )
       lowercase__				=  text_embeddings_clip / text_embeddings_clip.norm(p=2			,     dim=-1			,     keepdim=_lowerCamelCase       )
       # duplicate text embeddings clip for each generation per prompt
       lowercase__				=  text_embeddings_clip.repeat_interleave(_lowerCamelCase			,     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.
     lowercase__				=  guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
       lowercase__				=  text_input.input_ids.shape[-1]
       lowercase__				=  self.tokenizer(['']			,     padding='max_length'			,     max_length=_lowerCamelCase			,     return_tensors='pt'       )
       lowercase__				=  self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
       # duplicate unconditional embeddings for each generation per prompt
       lowercase__				=  uncond_embeddings.repeat_interleave(_lowerCamelCase			,     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
       lowercase__				=  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`.
     lowercase__				=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     lowercase__				=  text_embeddings.dtype
     if latents is None:
       if self.device.type == "mps":
         # randn does not work reproducibly on mps
         lowercase__				=  torch.randn(_lowerCamelCase			,     generator=_lowerCamelCase			,     device='cpu'			,     dtype=_lowerCamelCase       ).to(
             self.device       )
       else:
         lowercase__				=  torch.randn(_lowerCamelCase			,     generator=_lowerCamelCase			,     device=self.device			,     dtype=_lowerCamelCase       )
     else:
       if latents.shape != latents_shape:
         raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
       lowercase__				=  latents.to(self.device       )

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

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

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

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

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

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

       # predict the noise residual
       lowercase__				=  self.unet(_lowerCamelCase			,     _lowerCamelCase			,     encoder_hidden_states=_lowerCamelCase       ).sample

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

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

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

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

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

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

     if not return_dict:
       return (image, None)

     return StableDiffusionPipelineOutput(images=_lowerCamelCase			,     nsfw_content_detected=_lowerCamelCase       )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE    =			{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrFeatureExtractor''']
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			[
	    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DeformableDetrForObjectDetection''',
	    '''DeformableDetrModel''',
	    '''DeformableDetrPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE    =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





