






"""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,
  )


UpperCamelCase					=     logging.get_logger(__name__)


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







@add_end_docstrings(lowercase_       )
class 						_a  (		lowercase_       ):





   '''simple docstring'''






   def __init__(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          super().__init__(*UpperCAmelCase_					, **UpperCAmelCase_)

          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 __lowercase							(       self					, **UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							int         =     {}
          lowercase__:							Dict         =     {}
          if "subtask" in kwargs:
                 lowercase__:							Union[str, Any]         =     kwargs["subtask"]
                 lowercase__:							Optional[int]         =     kwargs["subtask"]
          if "threshold" in kwargs:
                 lowercase__:							Tuple         =     kwargs["threshold"]
          if "mask_threshold" in kwargs:
                 lowercase__:							List[Any]         =     kwargs["mask_threshold"]
          if "overlap_mask_area_threshold" in kwargs:
                 lowercase__:							Union[str, Any]         =     kwargs["overlap_mask_area_threshold"]

          return preprocess_kwargs, {}, postprocess_kwargs





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




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=None)			->   Any:




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     model_inputs.pop("target_size")
          lowercase__:							Tuple         =     self.model(**UpperCAmelCase_)
          lowercase__:							str         =     target_size
          return model_outputs





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=None					, UpperCAmelCase_=0.9					, UpperCAmelCase_=0.5					, UpperCAmelCase_=0.5)			->   Optional[Any]:




          '''simple docstring'''




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

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

                 lowercase__:							List[str]         =     []
                 lowercase__:							str         =     outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                        lowercase__:							str         =     (segmentation == segment["id"]) * 255
                        lowercase__:							List[Any]         =     Image.fromarray(mask.numpy().astype(np.uinta)					, mode="L")
                        lowercase__:							Union[str, Any]         =     self.model.config.idalabel[segment["label_id"]]
                        lowercase__:							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"):
                 lowercase__:							Any         =     self.image_processor.post_process_semantic_segmentation(
                     UpperCAmelCase_					, target_sizes=model_outputs["target_size"])[0]

                 lowercase__:							str         =     []
                 lowercase__:							Tuple         =     outputs.numpy()
                 lowercase__:							Dict         =     np.unique(UpperCAmelCase_)

                 for label in labels:
                        lowercase__:							Optional[Any]         =     (segmentation == label) * 255
                        lowercase__:							int         =     Image.fromarray(mask.astype(np.uinta)					, mode="L")
                        lowercase__:							Dict         =     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'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





