


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_		:     Dict        =     logging.get_logger(__name__)



@add_end_docstrings(__UpperCAmelCase	)
class 		_lowercase		(       __UpperCAmelCase	):

   def __init__(      self			:       Dict							,   **__lowerCAmelCase			:       Any							)       ->				List[str]:


    """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 		A							(      self			:       str							,   **__lowerCAmelCase			:       str							)       ->				str:


    """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			:       Union[str, Any]							,   __lowerCAmelCase			:       List[str]							,   *__lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       List[str]=None							,   **__lowerCAmelCase			:       str							)       ->				List[str]:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[Any]=64							,   __lowerCAmelCase			:       int = 0							,   __lowerCAmelCase			:       float = 512 / 1500							,   __lowerCAmelCase			:       Optional[int] = 32							,   __lowerCAmelCase			:       Optional[int] = 1							,   )       ->				Union[str, Any]:


    """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 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       Any=0.8_8							,   __lowerCAmelCase			:       Tuple=0.9_5							,   __lowerCAmelCase			:       str=0							,   __lowerCAmelCase			:       Dict=1							,   )       ->				List[Any]:


    """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 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Any=False							,   __lowerCAmelCase			:       List[Any]=False							,   __lowerCAmelCase			:       Tuple=0.7							,   )       ->				str:


    """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}






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(UniPCMultistepScheduler,)
   _UpperCAmelCase     	=			(('''num_inference_steps''', 25),)

   def 		A							(      self			:       List[Any]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "solver_order": 2,
        "solver_type": "bh2",
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]=0							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)
     # copy over dummy past residuals
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      new_scheduler.set_timesteps(__lowerCAmelCase							)
      # copy over dummy past residuals
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a		,  a						=		sample, sample
     for t in range(__lowerCAmelCase							,   time_step + scheduler.config.solver_order + 1							):
      a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
      a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

      assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]=0							,   **__lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)

     # copy over dummy past residuals (must be after setting timesteps)
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      # copy over dummy past residuals
      new_scheduler.set_timesteps(__lowerCAmelCase							)

      # copy over dummy past residual (must be after setting timesteps)
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any=None							,   **__lowerCAmelCase			:       List[str]							)       ->				Any:


    """simple docstring"""






    if scheduler is None:
     a						=		self.scheduler_classes[0]
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(**__lowerCAmelCase							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    return sample

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)

    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)

     a						=		self.dummy_sample
     a						=		0.1 * sample

     if num_inference_steps is not None and hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      scheduler.set_timesteps(__lowerCAmelCase							)
     elif num_inference_steps is not None and not hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      a						=		num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     a						=		scheduler.timesteps[5]
     a						=		scheduler.timesteps[6]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     self.assertEqual(output_a.shape							,   sample.shape							)
     self.assertEqual(output_a.shape							,   output_a.shape							)

   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		UniPCMultistepScheduler(**self.get_scheduler_config()							)
    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

    a						=		DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    a						=		DEISMultistepScheduler.from_config(scheduler.config							)
    a						=		DPMSolverMultistepScheduler.from_config(scheduler.config							)
    a						=		UniPCMultistepScheduler.from_config(scheduler.config							)

    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """simple docstring"""






    for timesteps in [25, 50, 100, 999, 1000]:
     self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    self.check_over_configs(thresholding=__lowerCAmelCase							)
    for order in [1, 2, 3]:
     for solver_type in ["bh1", "bh2"]:
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            thresholding=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   sample_max_value=__lowerCAmelCase							,   solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   )

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for solver_type in ["bh1", "bh2"]:
     for order in [1, 2, 3]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       a						=		self.full_loop(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       assert not torch.isnan(__lowerCAmelCase							).any(), "Samples have nan numbers"

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    self.check_over_configs(lower_order_final=__lowerCAmelCase							)
    self.check_over_configs(lower_order_final=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
     self.check_over_forward(num_inference_steps=__lowerCAmelCase							,   time_step=0							)

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop()
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(thresholding=__lowerCAmelCase							,   dynamic_thresholding_ratio=0							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    assert sample.dtype == torch.floataa




   def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

     scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
     assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps

