
"""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,
    )


_a   :      Union[str, Any]       =				logging.get_logger(__name__)


_a   :      List[Any]       =				Dict[str, Any]
_a   :      List[str]       =				List[Prediction]






@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class   _UpperCAmelCase					(       _SCREAMING_SNAKE_CASE):
    def __init__(     self    ,     *snake_case_    ,     **snake_case_  ):
      super().__init__(*_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_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        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Any            = {}
      _snake_case  :    int            = {}
      if "subtask" in kwargs:
        _snake_case  :    str            = kwargs["subtask"]
        _snake_case  :    List[str]            = kwargs["subtask"]
      if "threshold" in kwargs:
        _snake_case  :    int            = kwargs["threshold"]
      if "mask_threshold" in kwargs:
        _snake_case  :    Any            = kwargs["mask_threshold"]
      if "overlap_mask_area_threshold" in kwargs:
        _snake_case  :    Any            = kwargs["overlap_mask_area_threshold"]

      return preprocess_kwargs, {}, postprocess_kwargs
    def __call__(     self    ,     snake_case_    ,     **snake_case_  ):
      return super().__call__(_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None  ):
      _snake_case  :    Any            = load_image(_SCREAMING_SNAKE_CASE  )
      _snake_case  :    Dict            = [(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
        if subtask is None:
          _snake_case  :    Dict            = {}
        else:
          _snake_case  :    str            = {"task_inputs": [subtask]}
        _snake_case  :    str            = self.image_processor(images=[image]    ,     return_tensors="pt"    ,     **_SCREAMING_SNAKE_CASE  )
        _snake_case  :    Optional[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  :    int            = self.image_processor(images=[image]    ,     return_tensors="pt"  )
      _snake_case  :    Dict            = target_size
      return inputs
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Any            = model_inputs.pop("target_size"  )
      _snake_case  :    Optional[int]            = self.model(**_SCREAMING_SNAKE_CASE  )
      _snake_case  :    Optional[Any]            = target_size
      return model_outputs


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None    ,     snake_case_=0.9    ,     snake_case_=0.5    ,     snake_case_=0.5  ):
      _snake_case  :    Tuple            = None
      if subtask in {"panoptic", None} and hasattr(self.image_processor    ,     "post_process_panoptic_segmentation"  ):
        _snake_case  :    str            = self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor    ,     "post_process_instance_segmentation"  ):
        _snake_case  :    Tuple            = self.image_processor.post_process_instance_segmentation

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

        _snake_case  :    Optional[int]            = []
        _snake_case  :    List[str]            = outputs["segmentation"]

        for segment in outputs["segments_info"]:
          _snake_case  :    Any            = (segmentation == segment["id"]) * 2_55
          _snake_case  :    int            = Image.fromarray(mask.numpy().astype(np.uinta  )    ,     mode="L"  )
          _snake_case  :    List[Any]            = self.model.config.idalabel[segment["label_id"]]
          _snake_case  :    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"  ):
        _snake_case  :    Optional[Any]            = self.image_processor.post_process_semantic_segmentation(
            _SCREAMING_SNAKE_CASE    ,     target_sizes=model_outputs["target_size"]  )[0]

        _snake_case  :    Any            = []
        _snake_case  :    int            = outputs.numpy()
        _snake_case  :    Optional[Any]            = np.unique(_SCREAMING_SNAKE_CASE  )

        for label in labels:
          _snake_case  :    Optional[int]            = (segmentation == label) * 2_55
          _snake_case  :    Dict            = Image.fromarray(mask.astype(np.uinta  )    ,     mode="L"  )
          _snake_case  :    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





class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(_SCREAMING_SNAKE_CASE    ):
										self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
								return self.sequence[:index]







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	print(
	    """Fibonacci Series Using Dynamic Programming\n"""     ,					"""Enter the index of the Fibonacci number you want to calculate """     ,					"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""     ,					sep=""""""     ,					)
	a_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()