


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

SCREAMING_SNAKE_CASE       :		Dict         =				logging.get_logger(__name__)






@add_end_docstrings(lowercase__     )
class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							**UpperCamelCase_		):
        super().__init__(**UpperCamelCase_		)
        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(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							**UpperCamelCase_		):
        lowercase_       :List[str]     =       {}
        lowercase_       :Dict     =       {}
        lowercase_       :Tuple     =       {}
        # preprocess args
        if "points_per_batch" in kwargs:
             lowercase_       :str     =       kwargs['''points_per_batch''']
        if "points_per_crop" in kwargs:
             lowercase_       :Union[str, Any]     =       kwargs['''points_per_crop''']
        if "crops_n_layers" in kwargs:
             lowercase_       :Any     =       kwargs['''crops_n_layers''']
        if "crop_overlap_ratio" in kwargs:
             lowercase_       :int     =       kwargs['''crop_overlap_ratio''']
        if "crop_n_points_downscale_factor" in kwargs:
             lowercase_       :Tuple     =       kwargs['''crop_n_points_downscale_factor''']
        # postprocess args
        if "pred_iou_thresh" in kwargs:
             lowercase_       :Tuple     =       kwargs['''pred_iou_thresh''']
        if "stability_score_offset" in kwargs:
             lowercase_       :Tuple     =       kwargs['''stability_score_offset''']
        if "mask_threshold" in kwargs:
             lowercase_       :List[str]     =       kwargs['''mask_threshold''']
        if "stability_score_thresh" in kwargs:
             lowercase_       :Optional[Any]     =       kwargs['''stability_score_thresh''']
        if "crops_nms_thresh" in kwargs:
             lowercase_       :Any     =       kwargs['''crops_nms_thresh''']
        if "output_rle_mask" in kwargs:
             lowercase_       :Any     =       kwargs['''output_rle_mask''']
        if "output_bboxes_mask" in kwargs:
             lowercase_       :str     =       kwargs['''output_bboxes_mask''']
        return preprocess_kwargs, forward_params, postprocess_kwargs
   def __call__(     self   ,							UpperCamelCase_   ,							*UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							**UpperCamelCase_		):
        return super().__call__(UpperCamelCase_   ,							*UpperCamelCase_   ,							num_workers=UpperCamelCase_   ,							batch_size=UpperCamelCase_   ,							**UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=64   ,							UpperCamelCase_ = 0   ,							UpperCamelCase_ = 512 / 1500   ,							UpperCamelCase_ = 32   ,							UpperCamelCase_ = 1   ,							):
        lowercase_       :Optional[Any]     =       load_image(UpperCamelCase_		)
        lowercase_       :Any     =       self.image_processor.size['''longest_edge''']
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Tuple     =       self.image_processor.generate_crop_boxes(
            UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       self.image_processor(images=UpperCamelCase_   ,							return_tensors='''pt'''		)

        with self.device_placement():
             if self.framework == "pt":
                  lowercase_       :str     =       self.get_inference_context()
                  with inference_context():
                       lowercase_       :Optional[Any]     =       self._ensure_tensor_on_device(UpperCamelCase_   ,							device=self.device		)
                       lowercase_       :Any     =       self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''		)		)
                       lowercase_       :Dict     =       image_embeddings

        lowercase_       :Any     =       grid_points.shape[1]
        lowercase_       :Optional[Any]     =       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   ,							UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Any     =       grid_points[:, i : i + points_per_batch, :, :]
             lowercase_       :Tuple     =       input_labels[:, i : i + points_per_batch]
             lowercase_       :str     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=0.88   ,							UpperCamelCase_=0.95   ,							UpperCamelCase_=0   ,							UpperCamelCase_=1   ,							):
        lowercase_       :int     =       model_inputs.pop('''input_boxes'''		)
        lowercase_       :Tuple     =       model_inputs.pop('''is_last'''		)
        lowercase_       :Optional[Any]     =       model_inputs.pop('''original_sizes'''		).tolist()
        lowercase_       :Dict     =       model_inputs.pop('''reshaped_input_sizes'''		).tolist()

        lowercase_       :List[str]     =       self.model(**UpperCamelCase_		)

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




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=False   ,							UpperCamelCase_=False   ,							UpperCamelCase_=0.7   ,							):
        lowercase_       :Optional[int]     =       []
        lowercase_       :Optional[Any]     =       []
        lowercase_       :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'''		)		)

        lowercase_       :Any     =       torch.cat(UpperCamelCase_		)
        lowercase_       :int     =       torch.cat(UpperCamelCase_		)
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Optional[int]     =       self.image_processor.post_process_for_mask_generation(
            UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        lowercase_       :List[str]     =       defaultdict(UpperCamelCase_		)
        for output in model_outputs:
             for k, v in output.items():
                  extra[k].append(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       {}
        if output_rle_mask:
             lowercase_       :int     =       rle_mask

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

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





def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





     assert isinstance(_a       ,		_a		), f"The input value of [n={number}] is not an integer"

     if number == 1:
          return 2
     elif number < 1:
          lowercase_       :str     =       f"The input value of [n={number}] has to be > 0"
          raise ValueError(_a		)
     else:
          lowercase_       :List[str]     =       sylvester(number - 1		)
          lowercase_       :Union[str, Any]     =       num - 1
          lowercase_       :List[str]     =       num
          return lower * upper + 1


if __name__ == "__main__":
   print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")

