





'''simple docstring'''



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 							__lowercase				(					nn.Module  ):







	'''simple docstring'''

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







				'''simple docstring'''





				super().__init__()

				__lowercase   =							cut_size
				__lowercase   =							cut_power


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							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       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Dict ):
			__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       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			for param in model.parameters():
						__lowercase   =							value






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,)							->							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   _UpperCAmelCase   (self   ,_lowerCamelCase = "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   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				self.enable_attention_slicing(_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				set_requires_grad(self.vae   ,_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				set_requires_grad(self.vae   ,_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				set_requires_grad(self.unet   ,_lowerCamelCase       )


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







				'''simple docstring'''





				set_requires_grad(self.unet   ,_lowerCamelCase       )


	@torch.enable_grad()
	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=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   ,_lowerCamelCase   ,_lowerCamelCase = 512   ,_lowerCamelCase = 512   ,_lowerCamelCase = 50   ,_lowerCamelCase = 7.5   ,_lowerCamelCase = 1   ,_lowerCamelCase = 0.0   ,_lowerCamelCase = 100   ,_lowerCamelCase = None   ,_lowerCamelCase = 4   ,_lowerCamelCase = True   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = "pil"   ,_lowerCamelCase = 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__)





