



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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






def     a(      A       :    List[str] ,     A       :    Optional[Any] ,     A       :    Dict       )		->       Any:
					"""simple docstring"""





					if isinstance(A ,     torch.Tensor       ):
										return image
					elif isinstance(A ,     PIL.Image.Image       ):
										a					     =   [image]

					if isinstance(image[0] ,     PIL.Image.Image       ):
										a					     =   [np.array(i.resize((w, h) ,     resample=PIL_INTERPOLATION["lanczos"]       )       )[None, :] for i in image]
										a					     =   np.concatenate(A ,     axis=0       )
										a					     =   np.array(A       ).astype(np.floataa       ) / 255.0
										a					     =   image.transpose(0 ,     3 ,     1 ,     2       )
										a					     =   2.0 * image - 1.0
										a					     =   torch.from_numpy(A       )
					elif isinstance(image[0] ,     torch.Tensor       ):
										a					     =   torch.cat(A ,     dim=0       )
					return image






def     a(      A       :    Tuple ,     A       :    int ,     A       :    Optional[int] ,     A       :    str=0.9_995       )		->       Any:
					"""simple docstring"""





					if not isinstance(A ,     np.ndarray       ):
										a					     =   True
										a					     =   va.device
										a					     =   va.cpu().numpy()
										a					     =   va.cpu().numpy()

					a					     =   np.sum(va * va / (np.linalg.norm(A       ) * np.linalg.norm(A       ))       )
					if np.abs(A       ) > DOT_THRESHOLD:
										a					     =   (1 - t) * va + t * va
					else:
										a					     =   np.arccos(A       )
										a					     =   np.sin(A       )
										a					     =   theta_a * t
										a					     =   np.sin(A       )
										a					     =   np.sin(theta_a - theta_t       ) / sin_theta_a
										a					     =   sin_theta_t / sin_theta_a
										a					     =   sa * va + sa * va

					if inputs_are_torch:
										a					     =   torch.from_numpy(A       ).to(A       )

					return va






def     a(      A       :    Optional[int] ,     A       :    Union[str, Any]       )		->       Union[str, Any]:
					"""simple docstring"""





					a					     =   F.normalize(A ,     dim=-1       )
					a					     =   F.normalize(A ,     dim=-1       )
					return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )






def     a(      A       :    List[str] ,     A       :    int       )		->       Optional[int]:
					"""simple docstring"""





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







class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       ):
						"""simple docstring"""

						super().__init__()
						self.register_modules(
						    vae=lowerCamelCase_		,       text_encoder=lowerCamelCase_		,       clip_model=lowerCamelCase_		,       tokenizer=lowerCamelCase_		,       unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		,       feature_extractor=lowerCamelCase_		,       coca_model=lowerCamelCase_		,       coca_tokenizer=lowerCamelCase_		,       coca_transform=lowerCamelCase_		,       )
						a					     =   (
						    feature_extractor.size
						    if isinstance(feature_extractor.size		,       lowerCamelCase_		)
						    else feature_extractor.size["shortest_edge"]
						)
						a					     =   transforms.Normalize(mean=feature_extractor.image_mean		,       std=feature_extractor.image_std		)
						set_requires_grad(self.text_encoder		,       lowerCamelCase_		)
						set_requires_grad(self.clip_model		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_ = "auto"		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.enable_attention_slicing(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						set_requires_grad(self.vae		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						set_requires_grad(self.vae		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						set_requires_grad(self.unet		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						set_requires_grad(self.unet		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   min(int(num_inference_steps * strength		)		,       lowerCamelCase_		)

						a					     =   max(num_inference_steps - init_timestep		,       0		)
						a					     =   self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if not isinstance(lowerCamelCase_		,       torch.Tensor		):
											raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase_		)}'''		)

						a					     =   image.to(device=lowerCamelCase_		,       dtype=lowerCamelCase_		)

						if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   [
											    self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(lowerCamelCase_		)
											]
											a					     =   torch.cat(lowerCamelCase_		,       dim=0		)
						else:
											a					     =   self.vae.encode(lowerCamelCase_		).latent_dist.sample(lowerCamelCase_		)

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						a					     =   0.1_8215 * init_latents
						a					     =   init_latents.repeat_interleave(lowerCamelCase_		,       dim=0		)

						a					     =   randn_tensor(init_latents.shape		,       generator=lowerCamelCase_		,       device=lowerCamelCase_		,       dtype=lowerCamelCase_		)

						# get latents
						a					     =   self.scheduler.add_noise(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   init_latents

						return latents




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.coca_transform(lowerCamelCase_		).unsqueeze(0		)
						with torch.no_grad(), torch.cuda.amp.autocast():
											a					     =   self.coca_model.generate(transformed_image.to(device=self.device		,       dtype=self.coca_model.dtype		)		)
						a					     =   self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
						return generated.split("<end_of_text>"		)[0].replace("<start_of_text>"		,       ""		).rstrip(" .,"		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.feature_extractor.preprocess(lowerCamelCase_		)
						a					     =   torch.from_numpy(clip_image_input["pixel_values"][0]		).unsqueeze(0		).to(self.device		).half()
						a					     =   self.clip_model.get_image_features(lowerCamelCase_		)
						a					     =   image_embeddings_clip / image_embeddings_clip.norm(p=2		,       dim=-1		,       keepdim=lowerCamelCase_		)
						a					     =   image_embeddings_clip.repeat_interleave(lowerCamelCase_		,       dim=0		)
						return image_embeddings_clip




	@torch.enable_grad()
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   latents.detach().requires_grad_()

						a					     =   self.scheduler.scale_model_input(lowerCamelCase_		,       lowerCamelCase_		)

						# predict the noise residual
						a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		,       encoder_hidden_states=lowerCamelCase_		).sample

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

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

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						a					     =   1 / 0.1_8215 * sample
						a					     =   self.vae.decode(lowerCamelCase_		).sample
						a					     =   (image / 2 + 0.5).clamp(0		,       1		)

						a					     =   transforms.Resize(self.feature_extractor_size		)(lowerCamelCase_		)
						a					     =   self.normalize(lowerCamelCase_		).to(latents.dtype		)

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

						a					     =   spherical_dist_loss(lowerCamelCase_		,       lowerCamelCase_		).mean() * clip_guidance_scale

						a					     =   -torch.autograd.grad(lowerCamelCase_		,       lowerCamelCase_		)[0]

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




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 512		,       lowerCamelCase_ = 512		,       lowerCamelCase_ = 0.6		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = 7.5		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = 100		,       lowerCamelCase_ = None		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0.8		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 0.1		,       ):
						"""simple docstring"""

						if isinstance(lowerCamelCase_		,       lowerCamelCase_		) and len(lowerCamelCase_		) != batch_size:
											raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase_		)} generators.'''		)

						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 isinstance(lowerCamelCase_		,       torch.Generator		) and batch_size > 1:
											a					     =   [generator] + [None] * (batch_size - 1)

						a					     =   [
						    ("model", self.coca_model is None),
						    ("tokenizer", self.coca_tokenizer is None),
						    ("transform", self.coca_transform is None),
						]
						a					     =   [x[0] for x in coca_is_none if x[1]]
						a					     =   ", ".join(lowerCamelCase_		)
						# generate prompts with coca model if prompt is None
						if content_prompt is None:
											if len(lowerCamelCase_		):
																raise ValueError(
																    F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
																    F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''		)
											a					     =   self.get_image_description(lowerCamelCase_		)
						if style_prompt is None:
											if len(lowerCamelCase_		):
																raise ValueError(
																    F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
																    F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''		)
											a					     =   self.get_image_description(lowerCamelCase_		)

						# get prompt text embeddings for content and style
						a					     =   self.tokenizer(
						    lowerCamelCase_		,       padding="max_length"		,       max_length=self.tokenizer.model_max_length		,       truncation=lowerCamelCase_		,       return_tensors="pt"		,       )
						a					     =   self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

						a					     =   self.tokenizer(
						    lowerCamelCase_		,       padding="max_length"		,       max_length=self.tokenizer.model_max_length		,       truncation=lowerCamelCase_		,       return_tensors="pt"		,       )
						a					     =   self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

						a					     =   slerp(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						# duplicate text embeddings for each generation per prompt
						a					     =   text_embeddings.repeat_interleave(lowerCamelCase_		,       dim=0		)

						# set timesteps
						a					     =   "offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
						a					     =   {}
						if accepts_offset:
											a					     =   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
						self.scheduler.timesteps.to(self.device		)

						a     ,				a					     =   self.get_timesteps(lowerCamelCase_		,       lowerCamelCase_		,       self.device		)
						a					     =   timesteps[:1].repeat(lowerCamelCase_		)

						# Preprocess image
						a					     =   preprocess(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.prepare_latents(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       text_embeddings.dtype		,       self.device		,       lowerCamelCase_		)

						a					     =   preprocess(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.prepare_latents(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       text_embeddings.dtype		,       self.device		,       lowerCamelCase_		)

						a					     =   slerp(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						if clip_guidance_scale > 0:
											a					     =   self.get_clip_image_embeddings(lowerCamelCase_		,       lowerCamelCase_		)
											a					     =   self.get_clip_image_embeddings(lowerCamelCase_		,       lowerCamelCase_		)
											a					     =   slerp(
											    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						# 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.
						a					     =   guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
											a					     =   content_text_input.input_ids.shape[-1]
											a					     =   self.tokenizer([""]		,       padding="max_length"		,       max_length=lowerCamelCase_		,       return_tensors="pt"		)
											a					     =   self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
											# duplicate unconditional embeddings for each generation per prompt
											a					     =   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
											a					     =   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`.
						a					     =   (batch_size, self.unet.config.in_channels, height // 8, width // 8)
						a					     =   text_embeddings.dtype
						if latents is None:
											if self.device.type == "mps":
																# randn does not work reproducibly on mps
																a					     =   torch.randn(lowerCamelCase_		,       generator=lowerCamelCase_		,       device="cpu"		,       dtype=lowerCamelCase_		).to(
																    self.device		)
											else:
																a					     =   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}'''		)
											a					     =   latents.to(self.device		)

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

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

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

																# predict the noise residual
																a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		,       encoder_hidden_states=lowerCamelCase_		).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						a					     =   1 / 0.1_8215 * latents
						a					     =   self.vae.decode(lowerCamelCase_		).sample

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

						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image, None)

						return StableDiffusionPipelineOutput(images=lowerCamelCase_		,       nsfw_content_detected=lowerCamelCase_		)





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						requires_backends(self		,       "torch"		)

						if self.framework != "pt":
											raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

						self.check_model_type(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   points_per_batch if points_per_batch is not None else n_points

						if points_per_batch <= 0:
											raise ValueError(
											    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
											    "To return all points at once, set points_per_batch to None"		)

						for i in range(0		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   self.image_processor.filter_masks(
						    masks[0]		,       iou_scores[0]		,       original_sizes[0]		,       input_boxes[0]		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )
						return {
						    "masks": masks,
						    "is_last": is_last,
						    "boxes": boxes,
						    "iou_scores": iou_scores,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						for model_output in model_outputs:
											all_scores.append(model_output.pop("iou_scores"		)		)
											all_masks.extend(model_output.pop("masks"		)		)
											all_boxes.append(model_output.pop("boxes"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

						return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
