



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

SCREAMING_SNAKE_CASE :   Tuple							      =				logging.get_logger(__name__)






@add_end_docstrings(_a     )
class _lowerCamelCase(		_a     ):
       def __init__(      self,   **lowerCamelCase) ->     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     UpperCamelCase  (      self,   **lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	{}
           _lowercase :							List[Any]   	=	{}
           _lowercase :							str   	=	{}
           # preprocess args
           if "points_per_batch" in kwargs:
               _lowercase :							Any   	=	kwargs['points_per_batch']
           if "points_per_crop" in kwargs:
               _lowercase :							Tuple   	=	kwargs['points_per_crop']
           if "crops_n_layers" in kwargs:
               _lowercase :							int   	=	kwargs['crops_n_layers']
           if "crop_overlap_ratio" in kwargs:
               _lowercase :							int   	=	kwargs['crop_overlap_ratio']
           if "crop_n_points_downscale_factor" in kwargs:
               _lowercase :							int   	=	kwargs['crop_n_points_downscale_factor']
           # postprocess args
           if "pred_iou_thresh" in kwargs:
               _lowercase :							Dict   	=	kwargs['pred_iou_thresh']
           if "stability_score_offset" in kwargs:
               _lowercase :							Dict   	=	kwargs['stability_score_offset']
           if "mask_threshold" in kwargs:
               _lowercase :							Optional[int]   	=	kwargs['mask_threshold']
           if "stability_score_thresh" in kwargs:
               _lowercase :							List[Any]   	=	kwargs['stability_score_thresh']
           if "crops_nms_thresh" in kwargs:
               _lowercase :							Dict   	=	kwargs['crops_nms_thresh']
           if "output_rle_mask" in kwargs:
               _lowercase :							Tuple   	=	kwargs['output_rle_mask']
           if "output_bboxes_mask" in kwargs:
               _lowercase :							List[str]   	=	kwargs['output_bboxes_mask']
           return preprocess_kwargs, forward_params, postprocess_kwargs
       def __call__(      self,   lowerCamelCase,   *lowerCamelCase,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           return super().__call__(lowerCamelCase,   *lowerCamelCase,   num_workers=lowerCamelCase,   batch_size=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=64,   lowerCamelCase = 0,   lowerCamelCase = 5_12 / 15_00,   lowerCamelCase = 32,   lowerCamelCase = 1,   ) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	load_image(lowerCamelCase)
           _lowercase :							Any   	=	self.image_processor.size['longest_edge']
           _lowercase :							List[Any]   	=	self.image_processor.generate_crop_boxes(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	self.image_processor(images=lowerCamelCase,   return_tensors='pt')

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

           _lowercase :							Union[str, Any]   	=	grid_points.shape[1]
           _lowercase :							Dict   	=	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 :							Any   	=	grid_points[:, i : i + points_per_batch, :, :]
               _lowercase :							Union[str, Any]   	=	input_labels[:, i : i + points_per_batch]
               _lowercase :							List[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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=0.8_8,   lowerCamelCase=0.9_5,   lowerCamelCase=0,   lowerCamelCase=1,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	model_inputs.pop('input_boxes')
           _lowercase :							Dict   	=	model_inputs.pop('is_last')
           _lowercase :							List[Any]   	=	model_inputs.pop('original_sizes').tolist()
           _lowercase :							List[Any]   	=	model_inputs.pop('reshaped_input_sizes').tolist()

           _lowercase :							Tuple   	=	self.model(**lowerCamelCase)

           # post processing happens here in order to avoid CPU GPU copies of ALL the masks
           _lowercase :							Dict   	=	model_outputs['pred_masks']
           _lowercase :							Optional[int]   	=	self.image_processor.post_process_masks(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   binarize=lowerCamelCase)
           _lowercase :							Tuple   	=	model_outputs['iou_scores']
           _lowercase :							str   	=	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,   ) ->     Any:




           """simple docstring"""

           _lowercase :							Tuple   	=	[]
           _lowercase :							Optional[int]   	=	[]
           _lowercase :							str   	=	[]
           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 :							Dict   	=	torch.cat(lowerCamelCase)
           _lowercase :							Dict   	=	torch.cat(lowerCamelCase)
           _lowercase :							List[Any]   	=	self.image_processor.post_process_for_mask_generation(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)

           _lowercase :							Optional[Any]   	=	defaultdict(lowerCamelCase)
           for output in model_outputs:
               for k, v in output.items():
                   extra[k].append(lowerCamelCase)

           _lowercase :							Optional[int]   	=	{}
           if output_rle_mask:
               _lowercase :							Any   	=	rle_mask

           if output_bboxes_mask:
               _lowercase :							Dict   	=	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 _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    (DPMSolverSinglestepScheduler,)
       lowercase_				:			List[str]									=    (("""num_inference_steps""", 25),)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	{
               'num_train_timesteps': 10_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lambda_min_clipped': -float('inf'),
               'variance_type': None,
           }

           config.update(**lowerCamelCase)
           return config
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	dict(self.forward_default_kwargs)
           _lowercase :							Union[str, Any]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.dummy_sample
           _lowercase :							Optional[int]   	=	0.1 * sample
           _lowercase :							Optional[int]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							List[Any]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)
               # copy over dummy past residuals
               _lowercase :							List[str]   	=	dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							Optional[Any]   	=	scheduler_class.from_pretrained(lowerCamelCase)
                   new_scheduler.set_timesteps(lowerCamelCase)
                   # copy over dummy past residuals
                   _lowercase :							List[Any]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase	,       _lowercase :							List[Any]   	=	sample, sample
               for t in range(lowerCamelCase,   time_step + scheduler.config.solver_order + 1):
                   _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
                   _lowercase :							int   	=	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     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	dict(self.forward_default_kwargs)
           _lowercase :							List[str]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							List[str]   	=	self.dummy_sample
           _lowercase :							str   	=	0.1 * sample
           _lowercase :							Optional[Any]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config()
               _lowercase :							List[str]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							List[Any]   	=	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)
                   _lowercase :							List[str]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
               _lowercase :							List[Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase=None,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           if scheduler is None:
               _lowercase :							str   	=	self.scheduler_classes[0]
               _lowercase :							int   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Dict   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Optional[int]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           return sample
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							Optional[int]   	=	50
           _lowercase :							Union[str, Any]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:]):
               _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							int   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_5_7_4) < 1E-3
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							List[str]   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3

           _lowercase :							str   	=	DEISMultistepScheduler.from_config(scheduler.config)
           _lowercase :							List[str]   	=	DPMSolverMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Tuple   	=	UniPCMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Any   	=	DPMSolverSinglestepScheduler.from_config(scheduler.config)

           _lowercase :							Any   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(thresholding=lowerCamelCase)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   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,   algorithm_type='dpmsolver++',   solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   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,   algorithm_type=lowerCamelCase,   )
                           _lowercase :							Optional[Any]   	=	self.full_loop(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           assert not torch.isnan(lowerCamelCase).any(), "Samples have nan numbers"
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           self.check_over_configs(lower_order_final=lowerCamelCase)
           self.check_over_configs(lower_order_final=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(lambda_min_clipped=-float('inf'))
           self.check_over_configs(lambda_min_clipped=-5.1)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.check_over_configs(variance_type=lowerCamelCase)
           self.check_over_configs(variance_type='learned_range')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
               self.check_over_forward(num_inference_steps=lowerCamelCase,   time_step=0)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.full_loop()
           _lowercase :							Union[str, Any]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(use_karras_sigmas=lowerCamelCase)
           _lowercase :							List[str]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_2_4_8) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction')
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.1_4_5_3) < 1E-3
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction',   use_karras_sigmas=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.0_6_4_9) < 1E-3




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(thresholding=lowerCamelCase,   dynamic_thresholding_ratio=0)
           _lowercase :							Any   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							str   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           assert sample.dtype == torch.floataa


