





'''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

lowercase_					= logging.get_logger(__name__)


@add_end_docstrings(snake_case_       )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		**A       )		->   Any:
        super().__init__(**A       )
        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(A       )


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			{}
        # preprocess args
        if "points_per_batch" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""output_bboxes_mask"""]
        return preprocess_kwargs, forward_params, postprocess_kwargs


    def __call__(		self ,		A ,		*A ,		A=None ,		A=None ,		**A       )		->   List[Any]:
        return super().__call__(A ,		*A ,		num_workers=A ,		batch_size=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A=64 ,		A = 0 ,		A = 512 / 1500 ,		A = 32 ,		A = 1 ,		)		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			load_image(A       )
        _SCREAMING_SNAKE_CASE									=			self.image_processor.size["""longest_edge"""]
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.image_processor.generate_crop_boxes(
            A ,		A ,		A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			self.image_processor(images=A ,		return_tensors="""pt"""       )

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

        _SCREAMING_SNAKE_CASE									=			grid_points.shape[1]
        _SCREAMING_SNAKE_CASE									=			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 ,		A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			grid_points[:, i : i + points_per_batch, :, :]
            _SCREAMING_SNAKE_CASE									=			input_labels[:, i : i + points_per_batch]
            _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A=0.88 ,		A=0.95 ,		A=0 ,		A=1 ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""input_boxes"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""is_last"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""original_sizes"""       ).tolist()
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""reshaped_input_sizes"""       ).tolist()

        _SCREAMING_SNAKE_CASE									=			self.model(**A       )

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





    def 			snake_case_(		self ,		A ,		A=False ,		A=False ,		A=0.7 ,		)		->   str:
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        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									=			torch.cat(A       )
        _SCREAMING_SNAKE_CASE									=			torch.cat(A       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.image_processor.post_process_for_mask_generation(
            A ,		A ,		A ,		A       )

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

        _SCREAMING_SNAKE_CASE									=			{}
        if output_rle_mask:
            _SCREAMING_SNAKE_CASE									=			rle_mask

        if output_bboxes_mask:
            _SCREAMING_SNAKE_CASE									=			bounding_boxes

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










from __future__ import annotations

import typing
from collections import Counter







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    typing.Counter[int]:
  __snake_case:						typing.Counter[int]   =						Counter()
  for base in range(1					,					max_perimeter + 1):
    for perpendicular in range(SCREAMING_SNAKE_CASE__					,					max_perimeter + 1):
      __snake_case:						Dict   =						(base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(SCREAMING_SNAKE_CASE__):
        __snake_case:						Any   =						int(base + perpendicular + hypotenuse)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1000)   ->    int:
  __snake_case:						List[str]   =						pythagorean_triple(SCREAMING_SNAKE_CASE__)
  return triplets.most_common(1)[0][0]


if __name__ == "__main__":
      print(f'Perimeter {solution()} has maximum solutions')






