



"""simple docstring"""





from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import (
      MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
      MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
      MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
  )


SCREAMING_SNAKE_CASE				:     Optional[Any]          =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE				:     List[str]          =	Dict[str, Any]
SCREAMING_SNAKE_CASE				:     int          =	List[Prediction]


@add_end_docstrings(__lowercase    )
class       __lowerCamelCase	(   __lowercase    ):







   def __init__(self       ,	*lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




      super().__init__(*lowerCamelCase       ,	**lowerCamelCase				)

      if self.framework == "tf":
         raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

      requires_backends(self       ,	"""vision"""				)
      self.check_model_type(
          dict(
              MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()				)				)







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{}
      _lowerCAmelCase					     =		{}
      if "subtask" in kwargs:
         _lowerCAmelCase					     =		kwargs["""subtask"""]
         _lowerCAmelCase					     =		kwargs["""subtask"""]
      if "threshold" in kwargs:
         _lowerCAmelCase					     =		kwargs["""threshold"""]
      if "mask_threshold" in kwargs:
         _lowerCAmelCase					     =		kwargs["""mask_threshold"""]
      if "overlap_mask_area_threshold" in kwargs:
         _lowerCAmelCase					     =		kwargs["""overlap_mask_area_threshold"""]

      return preprocess_kwargs, {}, postprocess_kwargs







   def __call__(self       ,	lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




      return super().__call__(lowerCamelCase       ,	**lowerCamelCase				)







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase=None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		load_image(lowerCamelCase				)
      _lowerCAmelCase					     =		[(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
         if subtask is None:
            _lowerCAmelCase					     =		{}
         else:
            _lowerCAmelCase					     =		{"""task_inputs""": [subtask]}
         _lowerCAmelCase					     =		self.image_processor(images=[image]       ,	return_tensors="""pt"""       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		self.tokenizer(
             inputs["""task_inputs"""]       ,	padding="""max_length"""       ,	max_length=self.model.config.task_seq_len       ,	return_tensors=self.framework       ,	)["""input_ids"""]
      else:
         _lowerCAmelCase					     =		self.image_processor(images=[image]       ,	return_tensors="""pt"""				)
      _lowerCAmelCase					     =		target_size
      return inputs







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		model_inputs.pop("""target_size"""				)
      _lowerCAmelCase					     =		self.model(**lowerCamelCase				)
      _lowerCAmelCase					     =		target_size
      return model_outputs







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase=None       ,	lowerCamelCase=0.9       ,	lowerCamelCase=0.5       ,	lowerCamelCase=0.5				):




      '''simple docstring'''




      _lowerCAmelCase					     =		None
      if subtask in {"panoptic", None} and hasattr(self.image_processor       ,	"""post_process_panoptic_segmentation"""				):
         _lowerCAmelCase					     =		self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor       ,	"""post_process_instance_segmentation"""				):
         _lowerCAmelCase					     =		self.image_processor.post_process_instance_segmentation

      if fn is not None:
         _lowerCAmelCase					     =		fn(
             lowerCamelCase       ,	threshold=lowerCamelCase       ,	mask_threshold=lowerCamelCase       ,	overlap_mask_area_threshold=lowerCamelCase       ,	target_sizes=model_outputs["""target_size"""]       ,	)[0]

         _lowerCAmelCase					     =		[]
         _lowerCAmelCase					     =		outputs["""segmentation"""]

         for segment in outputs["segments_info"]:
            _lowerCAmelCase					     =		(segmentation == segment["""id"""]) * 255
            _lowerCAmelCase					     =		Image.fromarray(mask.numpy().astype(np.uinta				)       ,	mode="""L"""				)
            _lowerCAmelCase					     =		self.model.config.idalabel[segment["""label_id"""]]
            _lowerCAmelCase					     =		segment["""score"""]
            annotation.append({"""score""": score, """label""": label, """mask""": mask}				)

      elif subtask in {"semantic", None} and hasattr(self.image_processor       ,	"""post_process_semantic_segmentation"""				):
         _lowerCAmelCase					     =		self.image_processor.post_process_semantic_segmentation(
             lowerCamelCase       ,	target_sizes=model_outputs["""target_size"""]				)[0]

         _lowerCAmelCase					     =		[]
         _lowerCAmelCase					     =		outputs.numpy()
         _lowerCAmelCase					     =		np.unique(lowerCamelCase				)

         for label in labels:
            _lowerCAmelCase					     =		(segmentation == label) * 255
            _lowerCAmelCase					     =		Image.fromarray(mask.astype(np.uinta				)       ,	mode="""L"""				)
            _lowerCAmelCase					     =		self.model.config.idalabel[label]
            annotation.append({"""score""": None, """label""": label, """mask""": mask}				)
      else:
         raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model				)}"""				)
      return annotation




"""simple docstring"""





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   (HeunDiscreteScheduler,)
   __UpperCamelCase									=   10







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**lowerCamelCase				)
      return config







   def 							A__						(self				):




      '''simple docstring'''




      for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,	[0.0002, 0.002, 0.02]				):
         self.check_over_configs(beta_start=lowerCamelCase       ,	beta_end=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for schedule in ["linear", "scaled_linear", "exp"]:
         self.check_over_configs(beta_schedule=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for clip_sample_range in [1.0, 2.0, 3.0]:
         self.check_over_configs(clip_sample_range=lowerCamelCase       ,	clip_sample=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config(prediction_type="""v_prediction"""				)
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07				) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 4.693428650170972e-07				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if str(lowerCamelCase				).startswith("""cpu"""				):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      elif str(lowerCamelCase				).startswith("""mps"""				):
         # Larger tolerance on mps
         assert abs(result_mean.item() - 0.0002				) < 1e-2
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase       ,	use_karras_sigmas=lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      assert abs(result_sum.item() - 0.0_0015				) < 1e-2
      assert abs(result_mean.item() - 1.9869554535034695e-07				) < 1e-2