'''simple docstring'''





from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE_:	Any    =logging.get_logger(__name__)


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




@add_end_docstrings(UpperCamelCase__      )
class 			__A (					UpperCamelCase__      ):



    def __init__(self       :       str  ,     *__a       :       Union[str, Any]  ,     **__a       :       Dict					):
         super().__init__(*__a  ,     **__a					)

         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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()					)					)



    def    _lowercase		(self       :       List[str]  ,     **__a       :       List[str]					):
         UpperCAmelCase_			       =  {}
         if "threshold" in kwargs:
              UpperCAmelCase_			       =  kwargs["threshold"]
         return {}, {}, postprocess_kwargs



    def __call__(self       :       int  ,     *__a       :       Optional[Any]  ,     **__a       :       int					):
         return super().__call__(*__a  ,     **__a					)



    def    _lowercase		(self       :       str  ,     __a       :       str					):
         UpperCAmelCase_			       =  load_image(__a					)
         UpperCAmelCase_			       =  torch.IntTensor([[image.height, image.width]]					)
         UpperCAmelCase_			       =  self.image_processor(images=[image]  ,     return_tensors="pt"					)
         if self.tokenizer is not None:
              UpperCAmelCase_			       =  self.tokenizer(text=inputs["words"]  ,     boxes=inputs["boxes"]  ,     return_tensors="pt"					)
         UpperCAmelCase_			       =  target_size
         return inputs



    def    _lowercase		(self       :       str  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  model_inputs.pop("target_size"					)
         UpperCAmelCase_			       =  self.model(**__a					)
         UpperCAmelCase_			       =  outputs.__class__({"target_size": target_size, **outputs}					)
         if self.tokenizer is not None:
              UpperCAmelCase_			       =  model_inputs["bbox"]
         return model_outputs



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       Tuple  ,     __a       :       Optional[Any]=0.9					):
         UpperCAmelCase_			       =  model_outputs["target_size"]
         if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              UpperCAmelCase_       ,					UpperCAmelCase_			       =  target_size[0].tolist()

              def unnormalize(__a       :       str					):
                   return self._get_bounding_box(
                       torch.Tensor(
                           [
                               (width * bbox[0] / 1000),
                               (height * bbox[1] / 1000),
                               (width * bbox[2] / 1000),
                               (height * bbox[3] / 1000),
                           ]					)					)

              UpperCAmelCase_       ,					UpperCAmelCase_			       =  model_outputs["logits"].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
              UpperCAmelCase_			       =  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              UpperCAmelCase_			       =  [unnormalize(__a					) for bbox in model_outputs["bbox"].squeeze(0					)]
              UpperCAmelCase_			       =  ["score", "label", "box"]
              UpperCAmelCase_			       =  [dict(zip(__a  ,     __a					)					) for vals in zip(scores.tolist()  ,     __a  ,     __a					) if vals[0] > threshold]
         else:
              # This is a regular ForObjectDetectionModel
              UpperCAmelCase_			       =  self.image_processor.post_process_object_detection(__a  ,     __a  ,     __a					)
              UpperCAmelCase_			       =  raw_annotations[0]
              UpperCAmelCase_			       =  raw_annotation["scores"]
              UpperCAmelCase_			       =  raw_annotation["labels"]
              UpperCAmelCase_			       =  raw_annotation["boxes"]

              UpperCAmelCase_			       =  scores.tolist()
              UpperCAmelCase_			       =  [self.model.config.idalabel[label.item()] for label in labels]
              UpperCAmelCase_			       =  [self._get_bounding_box(__a					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              UpperCAmelCase_			       =  ["score", "label", "box"]
              UpperCAmelCase_			       =  [
                  dict(zip(__a  ,     __a					)					)
                  for vals in zip(raw_annotation["scores"]  ,     raw_annotation["labels"]  ,     raw_annotation["boxes"]					)
              ]

         return annotation






    def    _lowercase		(self       :       Dict  ,     __a       :       "torch.Tensor"					):
         if self.framework != "pt":
              raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."					)
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  box.int().tolist()
         UpperCAmelCase_			       =  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox










import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# Validation
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ) or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for day in days ):
				raise ValueError('The parameter days should be a list of integers' )

		if len(lowerCAmelCase ) != 3 or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for cost in costs ):
				raise ValueError('The parameter costs should be a list of three integers' )

		if len(lowerCAmelCase ) == 0:
				return 0

		if min(lowerCAmelCase ) <= 0:
				raise ValueError('All days elements should be greater than 0' )

		if max(lowerCAmelCase ) >= 366:
				raise ValueError('All days elements should be less than 366' )

		SCREAMING_SNAKE_CASE_					      =							set(lowerCAmelCase )

		@functools.cache
		def dynamic_programming(lowerCAmelCase ) -> int:
				if index > 365:
						return 0

				if index not in days_set:
						return dynamic_programming(index + 1 )

				return min(
				    costs[0] + dynamic_programming(index + 1 )    ,						costs[1] + dynamic_programming(index + 7 )    ,						costs[2] + dynamic_programming(index + 30 )    ,						)

		return dynamic_programming(1 )


if __name__ == "__main__":
						import doctest

						doctest.testmod()