"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__A			   = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
__A			   = parser.parse_args()


__A			   = "cpu"
__A			   = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

__A			   = "path-to-your-trained-model"
__A			   = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
       __A			   = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__A			   = pipe.to(device)

# to channels last
__A			   = pipe.unet.to(memory_format=torch.channels_last)
__A			   = pipe.vae.to(memory_format=torch.channels_last)
__A			   = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
       __A			   = pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
__A			   = torch.randn(2, 4, 6_4, 6_4)
__A			   = torch.rand(1) * 9_9_9
__A			   = torch.randn(2, 7_7, 7_6_8)
__A			   = (sample, timestep, encoder_hidden_status)
try:
       __A			   = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
       __A			   = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__A			   = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__A			   = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
       __A			   = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
__A			   = 6_6_6
__A			   = torch.Generator(device).manual_seed(seed)
__A			   = {"generator": generator}
if args.steps is not None:
       __A			   = args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
       __A			   = pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")

"""simple docstring"""






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

__A			   = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase  )
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			**_UpperCAmelCase							):
       super().__init__(**_UpperCAmelCase							)
       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(_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      {}
       lowercase__:   List[Any]					=      {}
       lowercase__:   Dict					=      {}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowercase__:   Dict					=      kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
          lowercase__:   Any					=      kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
          lowercase__:   Optional[Any]					=      kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowercase__:   Any					=      kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
          lowercase__:   Tuple					=      kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
          lowercase__:   List[str]					=      kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
          lowercase__:   str					=      kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
          lowercase__:   List[str]					=      kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
          lowercase__:   Dict					=      kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
          lowercase__:   int					=      kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs






    def __call__(   self    ,			_UpperCAmelCase    ,			*_UpperCAmelCase    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase							):
       return super().__call__(_UpperCAmelCase    ,			*_UpperCAmelCase    ,			num_workers=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=64    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = 512 / 1500    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 1    ,			):
       lowercase__:   Union[str, Any]					=      load_image(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.image_processor.size['''longest_edge''']
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      self.image_processor.generate_crop_boxes(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.image_processor(images=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       with self.device_placement():
          if self.framework == "pt":
             lowercase__:   Tuple					=      self.get_inference_context()
             with inference_context():
                lowercase__:   Optional[Any]					=      self._ensure_tensor_on_device(_UpperCAmelCase    ,			device=self.device							)
                lowercase__:   Any					=      self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
                lowercase__:   Tuple					=      image_embeddings

       lowercase__:   Optional[Any]					=      grid_points.shape[1]
       lowercase__:   Tuple					=      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    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
          lowercase__:   Dict					=      grid_points[:, i : i + points_per_batch, :, :]
          lowercase__:   int					=      input_labels[:, i : i + points_per_batch]
          lowercase__:   Any					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=0.88    ,			_UpperCAmelCase=0.95    ,			_UpperCAmelCase=0    ,			_UpperCAmelCase=1    ,			):
       lowercase__:   List[Any]					=      model_inputs.pop('''input_boxes'''							)
       lowercase__:   List[Any]					=      model_inputs.pop('''is_last'''							)
       lowercase__:   Any					=      model_inputs.pop('''original_sizes'''							).tolist()
       lowercase__:   Union[str, Any]					=      model_inputs.pop('''reshaped_input_sizes'''							).tolist()

       lowercase__:   List[Any]					=      self.model(**_UpperCAmelCase							)

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






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=0.7    ,			):
       lowercase__:   int					=      []
       lowercase__:   str					=      []
       lowercase__:   List[Any]					=      []
       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__:   Any					=      torch.cat(_UpperCAmelCase							)
       lowercase__:   Dict					=      torch.cat(_UpperCAmelCase							)
       lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      self.image_processor.post_process_for_mask_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      defaultdict(_UpperCAmelCase							)
       for output in model_outputs:
          for k, v in output.items():
             extra[k].append(_UpperCAmelCase							)

       lowercase__:   Any					=      {}
       if output_rle_mask:
          lowercase__:   Optional[Any]					=      rle_mask

       if output_bboxes_mask:
          lowercase__:   Optional[int]					=      bounding_boxes

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