


'''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

__a     =			logging.get_logger(__name__)






@add_end_docstrings(__snake_case  )
class   __lowercase				(  __snake_case  ):





      def __init__(		self						:					Union[str, Any]					,       **__lowerCamelCase						:					Dict       )    ->   Optional[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        _lowercase				(		self						:					Tuple					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     {}
         UpperCAmelCase							=     {}
         UpperCAmelCase							=     {}
         # preprocess args
         if "points_per_batch" in kwargs:
            UpperCAmelCase							=     kwargs["""points_per_batch"""]
         if "points_per_crop" in kwargs:
            UpperCAmelCase							=     kwargs["""points_per_crop"""]
         if "crops_n_layers" in kwargs:
            UpperCAmelCase							=     kwargs["""crops_n_layers"""]
         if "crop_overlap_ratio" in kwargs:
            UpperCAmelCase							=     kwargs["""crop_overlap_ratio"""]
         if "crop_n_points_downscale_factor" in kwargs:
            UpperCAmelCase							=     kwargs["""crop_n_points_downscale_factor"""]
         # postprocess args
         if "pred_iou_thresh" in kwargs:
            UpperCAmelCase							=     kwargs["""pred_iou_thresh"""]
         if "stability_score_offset" in kwargs:
            UpperCAmelCase							=     kwargs["""stability_score_offset"""]
         if "mask_threshold" in kwargs:
            UpperCAmelCase							=     kwargs["""mask_threshold"""]
         if "stability_score_thresh" in kwargs:
            UpperCAmelCase							=     kwargs["""stability_score_thresh"""]
         if "crops_nms_thresh" in kwargs:
            UpperCAmelCase							=     kwargs["""crops_nms_thresh"""]
         if "output_rle_mask" in kwargs:
            UpperCAmelCase							=     kwargs["""output_rle_mask"""]
         if "output_bboxes_mask" in kwargs:
            UpperCAmelCase							=     kwargs["""output_bboxes_mask"""]
         return preprocess_kwargs, forward_params, postprocess_kwargs





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






         """simple docstring"""







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





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Tuple=6_4					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					float = 5_1_2 / 1_5_0_0					,       __lowerCamelCase						:					Optional[int] = 3_2					,       __lowerCamelCase						:					Optional[int] = 1					,       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     load_image(__lowerCamelCase       )
         UpperCAmelCase							=     self.image_processor.size["""longest_edge"""]
         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self.image_processor.generate_crop_boxes(
             __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )
         UpperCAmelCase							=     self.image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""       )

         with self.device_placement():
            if self.framework == "pt":
               UpperCAmelCase							=     self.get_inference_context()
               with inference_context():
                  UpperCAmelCase							=     self._ensure_tensor_on_device(__lowerCamelCase					,       device=self.device       )
                  UpperCAmelCase							=     self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""       )       )
                  UpperCAmelCase							=     image_embeddings

         UpperCAmelCase							=     grid_points.shape[1]
         UpperCAmelCase							=     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       ):
            UpperCAmelCase							=     grid_points[:, i : i + points_per_batch, :, :]
            UpperCAmelCase							=     input_labels[:, i : i + points_per_batch]
            UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Tuple					,       __lowerCamelCase						:					Optional[int]=0.88					,       __lowerCamelCase						:					List[Any]=0.95					,       __lowerCamelCase						:					Optional[Any]=0					,       __lowerCamelCase						:					str=1					,       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     model_inputs.pop("""input_boxes"""       )
         UpperCAmelCase							=     model_inputs.pop("""is_last"""       )
         UpperCAmelCase							=     model_inputs.pop("""original_sizes"""       ).tolist()
         UpperCAmelCase							=     model_inputs.pop("""reshaped_input_sizes"""       ).tolist()

         UpperCAmelCase							=     self.model(**__lowerCamelCase       )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         UpperCAmelCase							=     model_outputs["""pred_masks"""]
         UpperCAmelCase							=     self.image_processor.post_process_masks(
             __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       binarize=__lowerCamelCase       )
         UpperCAmelCase							=     model_outputs["""iou_scores"""]
         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					List[str]=False					,       __lowerCamelCase						:					Tuple=False					,       __lowerCamelCase						:					Optional[int]=0.7					,       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     []
         UpperCAmelCase							=     []
         UpperCAmelCase							=     []
         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"""       )       )

         UpperCAmelCase							=     torch.cat(__lowerCamelCase       )
         UpperCAmelCase							=     torch.cat(__lowerCamelCase       )
         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self.image_processor.post_process_for_mask_generation(
             __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

         UpperCAmelCase							=     defaultdict(__lowerCamelCase       )
         for output in model_outputs:
            for k, v in output.items():
               extra[k].append(__lowerCamelCase       )

         UpperCAmelCase							=     {}
         if output_rle_mask:
            UpperCAmelCase							=     rle_mask

         if output_bboxes_mask:
            UpperCAmelCase							=     bounding_boxes

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







import math






class   __lowercase				:





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					list[list[float]]					,       __lowerCamelCase						:					list[int]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     0.0
         UpperCAmelCase							=     0.0
         for i in range(len(__lowerCamelCase       )       ):
            da += math.pow((sample[i] - weights[0][i])					,       2       )
            da += math.pow((sample[i] - weights[1][i])					,       2       )
            return 0 if da > da else 1
         return 0





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					list[list[int | float]]					,       __lowerCamelCase						:					list[int]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float       )    ->   list[list[int | float]]:






         """simple docstring"""







         for i in range(len(__lowerCamelCase       )       ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights

def   _UpperCamelCase				(						)						->None:
   # Training Examples ( m, n )
   UpperCAmelCase							=     [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

   # weight initialization ( n, C )
   UpperCAmelCase							=     [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

   # training
   UpperCAmelCase							=     SelfOrganizingMap()
   UpperCAmelCase							=     3
   UpperCAmelCase							=     0.5

   for _ in range(lowerCAmelCase_   ):
      for j in range(len(lowerCAmelCase_   )   ):
         # training sample
         UpperCAmelCase							=     training_samples[j]

         # Compute the winning vector
         UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

         # Update the winning vector
         UpperCAmelCase							=     self_organizing_map.update(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

    # classify test sample
   UpperCAmelCase							=     [0, 0, 0, 1]
   UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

   # results
   print(F"""Clusters that the test sample belongs to : {winner}"""   )
   print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()



