



'''simple docstring'''





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


A					:       Optional[int]									=     logging.get_logger(__name__)  # pylint: disable=invalid-name





class 				lowerCAmelCase_						(			__a		):





    def __init__(							self							:							Any,							_snake_case							:							AutoencoderKL,							_snake_case							:							CLIPTextModel,							_snake_case							:							CLIPTokenizer,							_snake_case							:							UNetaDConditionModel,							_snake_case							:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],							_snake_case							:							StableDiffusionSafetyChecker,							_snake_case							:							CLIPImageProcessor,							):



       '''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 	__snake_case  (							self							:							Optional[int],							_snake_case							:							Optional[Union[str, int]] = "auto"      ):



       '''simple docstring'''







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





    def 	__snake_case  (							self							:							List[str]      ):



       '''simple docstring'''







       self.enable_attention_slicing(lowerCAmelCase__      )






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



       '''simple docstring'''







       if isinstance(lowerCAmelCase__,							lowerCAmelCase__      ):
          snake_case       :				Union[str, Any]		    =1
       elif isinstance(lowerCAmelCase__,							lowerCAmelCase__      ):
          snake_case       :				Tuple		    =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
       snake_case       :				Any		    =self.tokenizer(
           lowerCAmelCase__,							padding='''max_length''',							max_length=self.tokenizer.model_max_length,							return_tensors='''pt''',							)
       snake_case       :				Optional[int]		    =text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case       :				str		    =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}'''      )
          snake_case       :				Any		    =text_input_ids[:, : self.tokenizer.model_max_length]

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       snake_case    ,							snake_case    ,							snake_case       :				int		    =text_embeddings.shape
       snake_case       :				Optional[int]		    =text_embeddings.repeat(1,							lowerCAmelCase__,							1      )
       snake_case       :				Union[str, Any]		    =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.
       snake_case       :				List[str]		    =guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
          snake_case       :				Optional[Any]		    =42
          if negative_prompt is None:
             snake_case       :				List[str]		    =['''''']
          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__      ):
             snake_case       :				int		    =[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:
             snake_case       :				int		    =negative_prompt

          snake_case       :				List[Any]		    =text_input_ids.shape[-1]
          snake_case       :				Union[str, Any]		    =self.tokenizer(
              lowerCAmelCase__,							padding='''max_length''',							max_length=lowerCAmelCase__,							truncation=lowerCAmelCase__,							return_tensors='''pt''',							)
          snake_case       :				Any		    =self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          snake_case       :				Tuple		    =uncond_embeddings.shape[1]
          snake_case       :				List[str]		    =uncond_embeddings.repeat(lowerCAmelCase__,							lowerCAmelCase__,							1      )
          snake_case       :				Dict		    =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
          snake_case       :				Union[str, Any]		    =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       :				int		    =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       snake_case       :				str		    =(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       snake_case       :				Union[str, Any]		    =text_embeddings.dtype
       if latents is None:
          if self.device.type == "mps":
             # randn does not exist on mps
             snake_case       :				Any		    =torch.randn(
                 lowerCAmelCase__,							generator=lowerCAmelCase__,							device='''cpu''',							dtype=lowerCAmelCase__      ).to(self.device      )
             snake_case       :				Dict		    =torch.randn(lowerCAmelCase__,							generator=lowerCAmelCase__,							device='''cpu''',							dtype=lowerCAmelCase__      ).to(
                 self.device      )
          else:
             snake_case       :				Union[str, Any]		    =torch.randn(
                 lowerCAmelCase__,							generator=lowerCAmelCase__,							device=self.device,							dtype=lowerCAmelCase__      )
             snake_case       :				str		    =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}'''      )
          snake_case       :				Any		    =latents_reference.to(self.device      )
          snake_case       :				Optional[int]		    =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
       snake_case       :				int		    =(latents_shape[3] - latents_shape_reference[3]) // 2
       snake_case       :				Optional[int]		    =(latents_shape[2] - latents_shape_reference[2]) // 2
       snake_case       :				Optional[int]		    =latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       snake_case       :				Optional[int]		    =latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       snake_case       :				Dict		    =0 if dx < 0 else dx
       snake_case       :				List[Any]		    =0 if dy < 0 else dy
       snake_case       :				int		    =max(-dx,							0      )
       snake_case       :				Optional[Any]		    =max(-dy,							0      )
       # import pdb
       # pdb.set_trace()
       snake_case       :				int		    =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
       snake_case       :				Dict		    =self.scheduler.timesteps.to(self.device      )

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

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

          # predict the noise residual
          snake_case       :				str		    =self.unet(lowerCAmelCase__,							lowerCAmelCase__,							encoder_hidden_states=lowerCAmelCase__      ).sample

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

          # compute the previous noisy sample x_t -> x_t-1
          snake_case       :				Dict		    =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__      )

       snake_case       :				int		    =1 / 0.1_8215 * latents
       snake_case       :				str		    =self.vae.decode(lowerCAmelCase__      ).sample

       snake_case       :				Dict		    =(image / 2 + 0.5).clamp(0,							1      )

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

       if self.safety_checker is not None:
          snake_case       :				Dict		    =self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__      ),							return_tensors='''pt'''      ).to(
              self.device      )
          snake_case    ,							snake_case       :				List[Any]		    =self.safety_checker(
              images=lowerCAmelCase__,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
       else:
          snake_case       :				Tuple		    =None

       if output_type == "pil":
          snake_case       :				Optional[Any]		    =self.numpy_to_pil(lowerCAmelCase__      )

       if not return_dict:
          return (image, has_nsfw_concept)

       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

_snake_case      :							List[Any]     	=    logging.get_logger(__name__)





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    **lowerCAmelCase__							:Dict     )  ->int:
										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        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      {}
										lowercase         =      {}
										lowercase         =      {}
										# preprocess args
										if "points_per_batch" in kwargs:
														lowercase         =      kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
														lowercase         =      kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
														lowercase         =      kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
														lowercase         =      kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
														lowercase         =      kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
														lowercase         =      kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
														lowercase         =      kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
														lowercase         =      kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
														lowercase         =      kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
														lowercase         =      kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
														lowercase         =      kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
														lowercase         =      kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs





						def __call__(			self							:List[str]		,    lowerCAmelCase__							:int		,    *lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Any=None		,    **lowerCAmelCase__							:List[str]     )  ->Optional[int]:
										return super().__call__(lowerCAmelCase__		,    *lowerCAmelCase__		,    num_workers=lowerCAmelCase__		,    batch_size=lowerCAmelCase__		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      grid_points.shape[1]
										lowercase         =      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__     ):
														lowercase         =      grid_points[:, i : i + points_per_batch, :, :]
														lowercase         =      input_labels[:, i : i + points_per_batch]
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										lowercase         =      model_inputs.pop("input_boxes"     )
										lowercase         =      model_inputs.pop("is_last"     )
										lowercase         =      model_inputs.pop("original_sizes"     ).tolist()
										lowercase         =      model_inputs.pop("reshaped_input_sizes"     ).tolist()

										lowercase         =      self.model(**lowerCAmelCase__     )

										# post processing happens here in order to avoid CPU GPU copies of ALL the masks
										lowercase         =      model_outputs["pred_masks"]
										lowercase         =      self.image_processor.post_process_masks(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    binarize=lowerCAmelCase__     )
										lowercase         =      model_outputs["iou_scores"]
										lowercase     ,	lowercase     ,	lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										lowercase         =      []
										lowercase         =      []
										lowercase         =      []
										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"     )     )

										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

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