


"""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,
					)


lowercase  		=			logging.get_logger(__name__)


lowercase  		=			Dict[str, Any]
lowercase  		=			List[Prediction]


@add_end_docstrings(_lowercase)
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''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        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      {}
								snake_case__  :						Optional[Any]		=      {}
								if "subtask" in kwargs:
									snake_case__  :						Optional[int]		=      kwargs["subtask"]
									snake_case__  :						List[str]		=      kwargs["subtask"]
								if "threshold" in kwargs:
									snake_case__  :						Any		=      kwargs["threshold"]
								if "mask_threshold" in kwargs:
									snake_case__  :						Optional[int]		=      kwargs["mask_threshold"]
								if "overlap_mask_area_threshold" in kwargs:
									snake_case__  :						List[Any]		=      kwargs["overlap_mask_area_threshold"]

								return preprocess_kwargs, {}, postprocess_kwargs




							def __call__(						self	,					lowerCamelCase__	,					**lowerCamelCase__)   ->	Union[Predictions, List[Prediction]]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None)   ->	Any:







								'''simple docstring'''




								snake_case__  :						int		=      load_image(lowerCamelCase__)
								snake_case__  :						List[str]		=      [(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
										snake_case__  :						Optional[Any]		=      {}
									else:
										snake_case__  :						Union[str, Any]		=      {"task_inputs": [subtask]}
									snake_case__  :						Any		=      self.image_processor(images=[image]	,					return_tensors="pt"	,					**lowerCamelCase__)
									snake_case__  :						int		=      self.tokenizer(
									    inputs["task_inputs"]	,					padding="max_length"	,					max_length=self.model.config.task_seq_len	,					return_tensors=self.framework	,					)["input_ids"]
								else:
									snake_case__  :						Dict		=      self.image_processor(images=[image]	,					return_tensors="pt")
								snake_case__  :						Optional[int]		=      target_size
								return inputs




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      model_inputs.pop("target_size")
								snake_case__  :						Tuple		=      self.model(**lowerCamelCase__)
								snake_case__  :						Tuple		=      target_size
								return model_outputs







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__=0.9	,					lowerCamelCase__=0.5	,					lowerCamelCase__=0.5)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      None
								if subtask in {"panoptic", None} and hasattr(self.image_processor	,					"post_process_panoptic_segmentation"):
									snake_case__  :						Union[str, Any]		=      self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor	,					"post_process_instance_segmentation"):
									snake_case__  :						Optional[Any]		=      self.image_processor.post_process_instance_segmentation

								if fn is not None:
									snake_case__  :						Union[str, Any]		=      fn(
									    lowerCamelCase__	,					threshold=lowerCamelCase__	,					mask_threshold=lowerCamelCase__	,					overlap_mask_area_threshold=lowerCamelCase__	,					target_sizes=model_outputs["target_size"]	,					)[0]

									snake_case__  :						Tuple		=      []
									snake_case__  :						Optional[Any]		=      outputs["segmentation"]

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

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

									snake_case__  :						Optional[Any]		=      []
									snake_case__  :						Optional[Any]		=      outputs.numpy()
									snake_case__  :						List[str]		=      np.unique(lowerCamelCase__)

									for label in labels:
										snake_case__  :						List[Any]		=      (segmentation == label) * 255
										snake_case__  :						Tuple		=      Image.fromarray(mask.astype(np.uinta)	,					mode="L")
										snake_case__  :						int		=      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







def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Dict			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      (boundary[1] - boundary[0]) / steps
						__UpperCamelCase  	=      boundary[0]
						__UpperCamelCase  	=      boundary[1]
						__UpperCamelCase  	=      make_points(snake_case			,   snake_case			,   snake_case			)
						__UpperCamelCase  	=      0.0
						y += (h / 3.0) * f(snake_case			)
						__UpperCamelCase  	=      2
						for i in x_i:
												y += (h / 3) * (4 - 2 * (cnt % 2)) * f(snake_case			)
												cnt += 1
						y += (h / 3.0) * f(snake_case			)
						return y




def 						A_			(				snake_case				: str			,   snake_case				: Optional[int]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      a + h
						while x < (b - h):
												yield x
												__UpperCamelCase  	=      x + h




def 						A_			(				snake_case				: Union[str, Any]			)   ->     str:  # enter your function here



						'''simple docstring'''

						__UpperCamelCase  	=      (x - 0) * (x - 0)
						return y




def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      0.0  # Lower bound of integration
						__UpperCamelCase  	=      1.0  # Upper bound of integration
						__UpperCamelCase  	=      10.0  # define number of steps or resolution
						__UpperCamelCase  	=      [a, b]  # define boundary of integration
						__UpperCamelCase  	=      method_a(snake_case			,   snake_case			)
						print(f"y = {y}"			)


if __name__ == "__main__":
				main()




