





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

snake_case__					:							List[str]													=     logging.get_logger(__name__)





@add_end_docstrings(_lowerCamelCase   )
class A_					(			_lowerCamelCase   ):




   def __init__(self	:Optional[Any]    ,   **_UpperCamelCase	:List[str] )->					List[Any]:
         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 _lowerCAmelCase      (self	:List[Any]    ,   **_UpperCamelCase	:Optional[int] )->					List[Any]:
         __A          =				{}
         __A          =				{}
         __A          =				{}
         # preprocess args
         if "points_per_batch" in kwargs:
               __A          =				kwargs['''points_per_batch''']
         if "points_per_crop" in kwargs:
               __A          =				kwargs['''points_per_crop''']
         if "crops_n_layers" in kwargs:
               __A          =				kwargs['''crops_n_layers''']
         if "crop_overlap_ratio" in kwargs:
               __A          =				kwargs['''crop_overlap_ratio''']
         if "crop_n_points_downscale_factor" in kwargs:
               __A          =				kwargs['''crop_n_points_downscale_factor''']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
               __A          =				kwargs['''pred_iou_thresh''']
         if "stability_score_offset" in kwargs:
               __A          =				kwargs['''stability_score_offset''']
         if "mask_threshold" in kwargs:
               __A          =				kwargs['''mask_threshold''']
         if "stability_score_thresh" in kwargs:
               __A          =				kwargs['''stability_score_thresh''']
         if "crops_nms_thresh" in kwargs:
               __A          =				kwargs['''crops_nms_thresh''']
         if "output_rle_mask" in kwargs:
               __A          =				kwargs['''output_rle_mask''']
         if "output_bboxes_mask" in kwargs:
               __A          =				kwargs['''output_bboxes_mask''']
         return preprocess_kwargs, forward_params, postprocess_kwargs




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




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:str=64    ,   _UpperCamelCase	:int = 0    ,   _UpperCamelCase	:float = 512 / 1500    ,   _UpperCamelCase	:Optional[int] = 32    ,   _UpperCamelCase	:Optional[int] = 1    ,   )->					str:
         __A          =				load_image(_UpperCamelCase )
         __A          =				self.image_processor.size['''longest_edge''']
         __A   ,						__A   ,						__A   ,						__A          =				self.image_processor.generate_crop_boxes(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				self.image_processor(images=_UpperCamelCase    ,   return_tensors='''pt''' )

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

         __A          =				grid_points.shape[1]
         __A          =				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 ):
               __A          =				grid_points[:, i : i + points_per_batch, :, :]
               __A          =				input_labels[:, i : i + points_per_batch]
               __A          =				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	:List[Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Any=0.8_8    ,   _UpperCamelCase	:Union[str, Any]=0.9_5    ,   _UpperCamelCase	:int=0    ,   _UpperCamelCase	:List[Any]=1    ,   )->					Tuple:
         __A          =				model_inputs.pop('''input_boxes''' )
         __A          =				model_inputs.pop('''is_last''' )
         __A          =				model_inputs.pop('''original_sizes''' ).tolist()
         __A          =				model_inputs.pop('''reshaped_input_sizes''' ).tolist()

         __A          =				self.model(**_UpperCamelCase )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         __A          =				model_outputs['''pred_masks''']
         __A          =				self.image_processor.post_process_masks(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   binarize=_UpperCamelCase )
         __A          =				model_outputs['''iou_scores''']
         __A   ,						__A   ,						__A          =				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 _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Union[str, Any]=False    ,   _UpperCamelCase	:List[str]=False    ,   _UpperCamelCase	:Tuple=0.7    ,   )->					Union[str, Any]:
         __A          =				[]
         __A          =				[]
         __A          =				[]
         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''' ) )

         __A          =				torch.cat(_UpperCamelCase )
         __A          =				torch.cat(_UpperCamelCase )
         __A   ,						__A   ,						__A   ,						__A          =				self.image_processor.post_process_for_mask_generation(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

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

         __A          =				{}
         if output_rle_mask:
               __A          =				rle_mask

         if output_bboxes_mask:
               __A          =				bounding_boxes

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












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





