




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (DDIMParallelScheduler,)
    UpperCAmelCase__							  =      (('''eta''', 0.0), ('''num_inference_steps''', 50))


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, **UpperCAmelCase__      :	str)						->List[str]:





           '''simple docstring'''

           A__       =    {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**UpperCAmelCase__)
           return config


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, **UpperCAmelCase__      :	List[Any])						->Tuple:





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config(**UpperCAmelCase__)
           A__       =    scheduler_class(**UpperCAmelCase__)

           A__ ,							A__       =    10, 0.0

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter

           scheduler.set_timesteps(UpperCAmelCase__)

           for t in scheduler.timesteps:
                  A__       =    model(UpperCAmelCase__					, UpperCAmelCase__)
                  A__       =    scheduler.step(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__).prev_sample

           return sample


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[Any]:





           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Tuple:





           '''simple docstring'''

           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=UpperCAmelCase__)

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config(steps_offset=1)
           A__       =    scheduler_class(**UpperCAmelCase__)
           scheduler.set_timesteps(5)
           assert torch.equal(scheduler.timesteps					, torch.LongTensor([801, 601, 401, 201, 1]))


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->int:





           '''simple docstring'''

           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					, [0.002, 0.02, 0.2, 2]):
                  self.check_over_configs(beta_start=UpperCAmelCase__					, beta_end=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCAmelCase__)


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





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->List[Any]:





           '''simple docstring'''

           for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=UpperCAmelCase__)


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





           '''simple docstring'''

           for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase__)


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





           '''simple docstring'''

           self.check_over_configs(thresholding=UpperCAmelCase__)
           for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                         self.check_over_configs(
                             thresholding=UpperCAmelCase__					, prediction_type=UpperCAmelCase__					, sample_max_value=UpperCAmelCase__					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[int]:





           '''simple docstring'''

           for t in [1, 10, 49]:
                  self.check_over_forward(time_step=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[str]:





           '''simple docstring'''

           for t, num_inference_steps in zip([1, 10, 50]					, [10, 50, 500]):
                  self.check_over_forward(time_step=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__)


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





           '''simple docstring'''

           for t, eta in zip([1, 10, 49]					, [0.0, 0.5, 1.0]):
                  self.check_over_forward(time_step=UpperCAmelCase__					, eta=UpperCAmelCase__)


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





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**UpperCAmelCase__)

           assert torch.sum(torch.abs(scheduler._get_variance(0					, 0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(420					, 400) - 0.14771)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(980					, 960) - 0.32460)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0					, 0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487					, 486) - 0.00979)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999					, 998) - 0.02)) < 1e-5


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





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**UpperCAmelCase__)

           A__ ,							A__       =    10, 0.0
           scheduler.set_timesteps(UpperCAmelCase__)

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter
           A__       =    self.dummy_sample_deter + 0.1
           A__       =    self.dummy_sample_deter - 0.1

           A__       =    samplea.shape[0]
           A__       =    torch.stack([samplea, samplea, samplea]					, dim=0)
           A__       =    torch.arange(UpperCAmelCase__)[0:3, None].repeat(1					, UpperCAmelCase__)

           A__       =    model(samples.flatten(0					, 1)					, timesteps.flatten(0					, 1))
           A__       =    scheduler.batch_step_no_noise(UpperCAmelCase__					, timesteps.flatten(0					, 1)					, samples.flatten(0					, 1)					, UpperCAmelCase__)

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 1147.7904) < 1e-2
           assert abs(result_mean.item() - 0.4982) < 1e-3


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Optional[Any]:





           '''simple docstring'''

           A__       =    self.full_loop()

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 172.0067) < 1e-2
           assert abs(result_mean.item() - 0.223967) < 1e-3


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    self.full_loop(prediction_type='''v_prediction''')

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 52.5302) < 1e-2
           assert abs(result_mean.item() - 0.0684) < 1e-3


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

           A__       =    self.full_loop(set_alpha_to_one=UpperCAmelCase__					, beta_start=0.01)
           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.8295) < 1e-2
           assert abs(result_mean.item() - 0.1951) < 1e-3





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





           '''simple docstring'''

           A__       =    self.full_loop(set_alpha_to_one=UpperCAmelCase__					, beta_start=0.01)
           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.0784) < 1e-2
           assert abs(result_mean.item() - 0.1941) < 1e-3







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(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           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(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''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]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     int = 0     ,		__SCREAMING_SNAKE_CASE							:     float = 512 / 1_500     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 32     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 1     ,		):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor.size['''longest_edge''']
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.generate_crop_boxes(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		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(__SCREAMING_SNAKE_CASE     ,		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     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __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 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=0.88     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.95     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		__SCREAMING_SNAKE_CASE							:     int=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(**__SCREAMING_SNAKE_CASE)

           # 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(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		binarize=__SCREAMING_SNAKE_CASE)
           __a						       =				model_outputs['''iou_scores''']
           __a       ,   __a       ,   __a						       =				self.image_processor.filter_masks(
               masks[0]     ,		iou_scores[0]     ,		original_sizes[0]     ,		input_boxes[0]     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		)
           return {
               "masks": masks,
               "is_last": is_last,
               "boxes": boxes,
               "iou_scores": iou_scores,
           }





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Dict=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     int=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(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.post_process_for_mask_generation(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

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

           __a						       =				{}
           if output_rle_mask:
               __a						       =				rle_mask

           if output_bboxes_mask:
               __a						       =				bounding_boxes

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





