


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,
      )


_snake_case				=			logging.get_logger(__name__)


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




@add_end_docstrings(__A  )
class  A_ (			__A  ):







 """simple docstring"""





 def __init__(      self       :			List[str]							,     *_a       :			Optional[int]							,     **_a       :			int					)     ->      List[Any]:
      super().__init__(*__snake_case							,     **__snake_case					)

      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       :			List[Any]							,     **_a       :			Any					)     ->      Union[str, Any]:
      UpperCAmelCase    :					str			=      {}
      UpperCAmelCase    :					int			=      {}
      if "subtask" in kwargs:
           UpperCAmelCase    :					Any			=      kwargs["subtask"]
           UpperCAmelCase    :					List[Any]			=      kwargs["subtask"]
      if "threshold" in kwargs:
           UpperCAmelCase    :					Optional[Any]			=      kwargs["threshold"]
      if "mask_threshold" in kwargs:
           UpperCAmelCase    :					Any			=      kwargs["mask_threshold"]
      if "overlap_mask_area_threshold" in kwargs:
           UpperCAmelCase    :					List[Any]			=      kwargs["overlap_mask_area_threshold"]

      return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(      self       :			int							,     _a       :			Optional[Any]							,     **_a       :			Tuple					)     ->      Union[Predictions, List[Prediction]]:
      return super().__call__(__snake_case							,     **__snake_case					)
 def 	_UpperCamelCase     (      self       :			Optional[int]							,     _a       :			Optional[Any]							,     _a       :			Union[str, Any]=None					)     ->      List[str]:
      UpperCAmelCase    :					Any			=      load_image(__snake_case					)
      UpperCAmelCase    :					Tuple			=      [(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
           if subtask is None:
                UpperCAmelCase    :					List[Any]			=      {}
           else:
                UpperCAmelCase    :					int			=      {"task_inputs": [subtask]}
           UpperCAmelCase    :					Tuple			=      self.image_processor(images=[image]							,     return_tensors="pt"							,     **__snake_case					)
           UpperCAmelCase    :					Union[str, Any]			=      self.tokenizer(
               inputs["task_inputs"]							,     padding="max_length"							,     max_length=self.model.config.task_seq_len							,     return_tensors=self.framework							,     )["input_ids"]
      else:
           UpperCAmelCase    :					str			=      self.image_processor(images=[image]							,     return_tensors="pt"					)
      UpperCAmelCase    :					Optional[int]			=      target_size
      return inputs
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			List[str]					)     ->      Optional[Any]:
      UpperCAmelCase    :					Dict			=      model_inputs.pop("target_size"					)
      UpperCAmelCase    :					Union[str, Any]			=      self.model(**__snake_case					)
      UpperCAmelCase    :					Optional[Any]			=      target_size
      return model_outputs

 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			Optional[int]							,     _a       :			Tuple=None							,     _a       :			List[str]=0.9							,     _a       :			int=0.5							,     _a       :			Optional[int]=0.5					)     ->      Optional[int]:
      UpperCAmelCase    :					List[Any]			=      None
      if subtask in {"panoptic", None} and hasattr(self.image_processor							,     "post_process_panoptic_segmentation"					):
           UpperCAmelCase    :					str			=      self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor							,     "post_process_instance_segmentation"					):
           UpperCAmelCase    :					str			=      self.image_processor.post_process_instance_segmentation

      if fn is not None:
           UpperCAmelCase    :					Optional[Any]			=      fn(
               __snake_case							,     threshold=__snake_case							,     mask_threshold=__snake_case							,     overlap_mask_area_threshold=__snake_case							,     target_sizes=model_outputs["target_size"]							,     )[0]

           UpperCAmelCase    :					Optional[int]			=      []
           UpperCAmelCase    :					int			=      outputs["segmentation"]

           for segment in outputs["segments_info"]:
                UpperCAmelCase    :					Dict			=      (segmentation == segment["id"]) * 2_5_5
                UpperCAmelCase    :					int			=      Image.fromarray(mask.numpy().astype(np.uinta					)							,     mode="L"					)
                UpperCAmelCase    :					Optional[Any]			=      self.model.config.idalabel[segment["label_id"]]
                UpperCAmelCase    :					Optional[int]			=      segment["score"]
                annotation.append({"score": score, "label": label, "mask": mask}					)

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

           UpperCAmelCase    :					Tuple			=      []
           UpperCAmelCase    :					Any			=      outputs.numpy()
           UpperCAmelCase    :					Tuple			=      np.unique(__snake_case					)

           for label in labels:
                UpperCAmelCase    :					str			=      (segmentation == label) * 2_5_5
                UpperCAmelCase    :					str			=      Image.fromarray(mask.astype(np.uinta					)							,     mode="L"					)
                UpperCAmelCase    :					List[str]			=      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					(				UpperCAmelCase	,	UpperCAmelCase					):
					# "Simpson Rule"
					# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
					_snake_case							:       List[str]												= (boundary[1] - boundary[0]) / steps
					_snake_case							:       List[Any]												= boundary[0]
					_snake_case							:       Tuple												= boundary[1]
					_snake_case							:       str												= make_points(UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase					)
					_snake_case							:       str												= 0.0
					y += (h / 3.0) * f(UpperCAmelCase					)
					_snake_case							:       Tuple												= 2
					for i in x_i:
										y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase					)
										cnt += 1
					y += (h / 3.0) * f(UpperCAmelCase					)
					return y







def  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase					):
					_snake_case							:       Union[str, Any]												= a + h
					while x < (b - h):
										yield x
										_snake_case							:       Optional[Any]												= x + h







def  A					(				UpperCAmelCase					):  # enter your function here
					_snake_case							:       Dict												= (x - 0) * (x - 0)
					return y







def  A					(				):
					_snake_case							:       int												= 0.0  # Lower bound of integration
					_snake_case							:       Union[str, Any]												= 1.0  # Upper bound of integration
					_snake_case							:       List[str]												= 1_0.0  # define number of steps or resolution
					_snake_case							:       Optional[int]												= [a, b]  # define boundary of integration
					_snake_case							:       List[str]												= method_a(UpperCAmelCase	,	UpperCAmelCase					)
					print(F"""y = {y}"""					)


if __name__ == "__main__":
							main()