



"""simple docstring"""
from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_UpperCAmelCase     =			logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__       )
class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Dict       ,		**lowerCAmelCase :					int		)     ->					int:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     requires_backends(self       ,		"""vision"""		)
     requires_backends(self       ,		"""torch"""		)

     if self.framework != "pt":
        raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)

     self.check_model_type(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any       ,		**lowerCAmelCase :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     # preprocess args
     if "points_per_batch" in kwargs:
        SCREAMING_SNAKE_CASE_: Tuple     			=kwargs["""points_per_batch"""]
     if "points_per_crop" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""points_per_crop"""]
     if "crops_n_layers" in kwargs:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=kwargs["""crops_n_layers"""]
     if "crop_overlap_ratio" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""crop_overlap_ratio"""]
     if "crop_n_points_downscale_factor" in kwargs:
        SCREAMING_SNAKE_CASE_: Any     			=kwargs["""crop_n_points_downscale_factor"""]
     # postprocess args
     if "pred_iou_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: List[Any]     			=kwargs["""pred_iou_thresh"""]
     if "stability_score_offset" in kwargs:
        SCREAMING_SNAKE_CASE_: Tuple     			=kwargs["""stability_score_offset"""]
     if "mask_threshold" in kwargs:
        SCREAMING_SNAKE_CASE_: List[str]     			=kwargs["""mask_threshold"""]
     if "stability_score_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""stability_score_thresh"""]
     if "crops_nms_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: List[str]     			=kwargs["""crops_nms_thresh"""]
     if "output_rle_mask" in kwargs:
        SCREAMING_SNAKE_CASE_: Dict     			=kwargs["""output_rle_mask"""]
     if "output_bboxes_mask" in kwargs:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=kwargs["""output_bboxes_mask"""]
     return preprocess_kwargs, forward_params, postprocess_kwargs



  def __call__(						self :					List[Any]       ,		lowerCAmelCase :					Union[str, Any]       ,		*lowerCAmelCase :					int       ,		lowerCAmelCase :					Any=None       ,		lowerCAmelCase :					List[str]=None       ,		**lowerCAmelCase :					Optional[int]		)     ->					str:


     '''simple docstring'''




     return super().__call__(lowerCAmelCase       ,		*lowerCAmelCase       ,		num_workers=lowerCAmelCase       ,		batch_size=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					List[Any]=64       ,		lowerCAmelCase :					int = 0       ,		lowerCAmelCase :					float = 512 / 1500       ,		lowerCAmelCase :					Optional[int] = 32       ,		lowerCAmelCase :					Optional[int] = 1       ,		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=load_image(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.image_processor.size["""longest_edge"""]
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=self.image_processor.generate_crop_boxes(
         lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		)

     with self.device_placement():
        if self.framework == "pt":
           SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_inference_context()
           with inference_context():
              SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self._ensure_tensor_on_device(lowerCAmelCase       ,		device=self.device		)
              SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
              SCREAMING_SNAKE_CASE_: List[Any]     			=image_embeddings

     SCREAMING_SNAKE_CASE_: Any     			=grid_points.shape[1]
     SCREAMING_SNAKE_CASE_: List[str]     			=points_per_batch if points_per_batch is not None else n_points

     if points_per_batch <= 0:
        raise ValueError(
            """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
            """To return all points at once, set points_per_batch to None"""		)

     for i in range(0       ,		lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=grid_points[:, i : i + points_per_batch, :, :]
        SCREAMING_SNAKE_CASE_: Dict     			=input_labels[:, i : i + points_per_batch]
        SCREAMING_SNAKE_CASE_: Dict     			=i == n_points - points_per_batch
        yield {
            "input_points": batched_points,
            "input_labels": labels,
            "input_boxes": crop_boxes,
            "is_last": is_last,
            **model_inputs,
        }



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					List[str]=0.8_8       ,		lowerCAmelCase :					List[str]=0.9_5       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Dict=1       ,		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=model_inputs.pop("""input_boxes"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=model_inputs.pop("""is_last"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=model_inputs.pop("""original_sizes"""		).tolist()
     SCREAMING_SNAKE_CASE_: Tuple     			=model_inputs.pop("""reshaped_input_sizes"""		).tolist()

     SCREAMING_SNAKE_CASE_: List[str]     			=self.model(**lowerCAmelCase		)

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     SCREAMING_SNAKE_CASE_: Tuple     			=model_outputs["""pred_masks"""]
     SCREAMING_SNAKE_CASE_: str     			=self.image_processor.post_process_masks(
         lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		binarize=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=model_outputs["""iou_scores"""]
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=self.image_processor.filter_masks(
         masks[0]       ,		iou_scores[0]       ,		original_sizes[0]       ,		input_boxes[0]       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		)
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					str=False       ,		lowerCAmelCase :					Optional[int]=False       ,		lowerCAmelCase :					str=0.7       ,		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=[]
     SCREAMING_SNAKE_CASE_: List[Any]     			=[]
     SCREAMING_SNAKE_CASE_: List[str]     			=[]
     for model_output in model_outputs:
        all_scores.append(model_output.pop("""iou_scores"""		)		)
        all_masks.extend(model_output.pop("""masks"""		)		)
        all_boxes.append(model_output.pop("""boxes"""		)		)

     SCREAMING_SNAKE_CASE_: List[str]     			=torch.cat(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=torch.cat(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.image_processor.post_process_for_mask_generation(
         lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=defaultdict(lowerCAmelCase		)
     for output in model_outputs:
        for k, v in output.items():
           extra[k].append(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			={}
     if output_rle_mask:
        SCREAMING_SNAKE_CASE_: Dict     			=rle_mask

     if output_bboxes_mask:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=bounding_boxes

     return {"masks": output_masks, "scores": iou_scores, **optional, **extra}








"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

   # Display the confusion matrix of the classifier with both training and test sets
   ConfusionMatrixDisplay.from_estimator(
       lowercase							,			lowercase							,			lowercase							,			display_labels=lowercase							,			cmap="""Blues"""							,			normalize="""true"""							,			)
   plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



