


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       :      List[Any]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      **lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           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,      **lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		{}
           _lowercase       :							Tuple      		=		{}
           _lowercase       :							Any      		=		{}
           # preprocess args
           if "points_per_batch" in kwargs:
               _lowercase       :							Dict      		=		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       :							Any      		=		kwargs['crop_overlap_ratio']
           if "crop_n_points_downscale_factor" in kwargs:
               _lowercase       :							Dict      		=		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       :							Optional[int]      		=		kwargs['stability_score_offset']
           if "mask_threshold" in kwargs:
               _lowercase       :							List[Any]      		=		kwargs['mask_threshold']
           if "stability_score_thresh" in kwargs:
               _lowercase       :							Optional[Any]      		=		kwargs['stability_score_thresh']
           if "crops_nms_thresh" in kwargs:
               _lowercase       :							int      		=		kwargs['crops_nms_thresh']
           if "output_rle_mask" in kwargs:
               _lowercase       :							Union[str, Any]      		=		kwargs['output_rle_mask']
           if "output_bboxes_mask" in kwargs:
               _lowercase       :							Optional[Any]      		=		kwargs['output_bboxes_mask']
           return preprocess_kwargs, forward_params, postprocess_kwargs
       def __call__(					self,      lowerCamelCase,      *lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=None,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(__UpperCAmelCase,      *__UpperCAmelCase,      num_workers=__UpperCAmelCase,      batch_size=__UpperCAmelCase,      **__UpperCAmelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=64,      lowerCamelCase = 0,      lowerCamelCase = 5_12 / 15_00,      lowerCamelCase = 32,      lowerCamelCase = 1,      )       ->   int:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(__UpperCAmelCase)
           _lowercase       :							int      		=		self.image_processor.size['longest_edge']
           _lowercase       :							int      		=		self.image_processor.generate_crop_boxes(
               __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase)
           _lowercase       :							Union[str, Any]      		=		self.image_processor(images=__UpperCAmelCase,      return_tensors='pt')

           with self.device_placement():
               if self.framework == "pt":
                   _lowercase       :							Optional[int]      		=		self.get_inference_context()
                   with inference_context():
                       _lowercase       :							Any      		=		self._ensure_tensor_on_device(__UpperCAmelCase,      device=self.device)
                       _lowercase       :							Tuple      		=		self.model.get_image_embeddings(model_inputs.pop('pixel_values'))
                       _lowercase       :							Optional[int]      		=		image_embeddings

           _lowercase       :							List[Any]      		=		grid_points.shape[1]
           _lowercase       :							Union[str, 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       :							Optional[Any]      		=		grid_points[:, i : i + points_per_batch, :, :]
               _lowercase       :							List[str]      		=		input_labels[:, i : i + points_per_batch]
               _lowercase       :							List[Any]      		=		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,      lowerCamelCase,      lowerCamelCase=0.8_8,      lowerCamelCase=0.9_5,      lowerCamelCase=0,      lowerCamelCase=1,      )       ->   str:




           """simple docstring"""

           _lowercase       :							Any      		=		model_inputs.pop('input_boxes')
           _lowercase       :							Optional[int]      		=		model_inputs.pop('is_last')
           _lowercase       :							Dict      		=		model_inputs.pop('original_sizes').tolist()
           _lowercase       :							Dict      		=		model_inputs.pop('reshaped_input_sizes').tolist()

           _lowercase       :							Optional[int]      		=		self.model(**__UpperCAmelCase)

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




           """simple docstring"""

           _lowercase       :							Dict      		=		[]
           _lowercase       :							Optional[Any]      		=		[]
           _lowercase       :							int      		=		[]
           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       :							Dict      		=		torch.cat(__UpperCAmelCase)
           _lowercase       :							Dict      		=		torch.cat(__UpperCAmelCase)
           _lowercase       :							Any      		=		self.image_processor.post_process_for_mask_generation(
               __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase)

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

           _lowercase       :							Optional[int]      		=		{}
           if output_rle_mask:
               _lowercase       :							str      		=		rle_mask

           if output_bboxes_mask:
               _lowercase       :							Optional[int]      		=		bounding_boxes

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









"""simple docstring"""





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__A											=		TypeVar("""KEY""")
__A											=		TypeVar("""VAL""")






@dataclass(frozen=a  ,   slots=a       )
class 					_lowerCAmelCase   (     Generic[KEY, VAL]       ):
       """simple docstring"""




       __magic_name__     :KEY
       __magic_name__     :VAL






class 					_lowerCAmelCase   (     _Item       ):
       """simple docstring"""




       def __init__(    self      ):



         '''simple docstring'''

         super().__init__(__UpperCAmelCase    , __UpperCAmelCase      )






       def __bool__(    self      ):



         '''simple docstring'''

         return False






__A											=		_DeletedItem()






class 					_lowerCAmelCase   (     MutableMapping[KEY, VAL]       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase = 8    , __UpperCAmelCase = 0.75      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							initial_block_size
         lowerCAmelCase__			:list[_Item | None]											=							[None] * initial_block_size
         assert 0.0 < capacity_factor < 1.0
         lowerCAmelCase__			:Tuple											=							capacity_factor
         lowerCAmelCase__			:str											=							0






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return hash(__UpperCAmelCase      ) % len(self._buckets      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return (ind + 1) % len(self._buckets      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							self._buckets[ind]
         if not stored:
           lowerCAmelCase__			:Dict											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           self._len += 1
           return True
         elif stored.key == key:
           lowerCAmelCase__			:Optional[Any]											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           return True
         else:
           return False






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							len(self._buckets      ) * self._capacity_factor
         return len(self      ) >= int(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         if len(self._buckets      ) <= self._initial_block_size:
           return False
         lowerCAmelCase__			:Optional[Any]											=							len(self._buckets      ) * self._capacity_factor / 2
         return len(self      ) < limit






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							self._buckets
         lowerCAmelCase__			:Tuple											=							[None] * new_size
         lowerCAmelCase__			:List[Any]											=							0
         for item in old_buckets:
           if item:
             self._add_item(item.key    , item.val      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self._resize(len(self._buckets      ) * 2      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self._resize(len(self._buckets      ) // 2      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self._get_bucket_index(__UpperCAmelCase      )
         for _ in range(len(self._buckets      )      ):
           yield ind
           lowerCAmelCase__			:Tuple											=							self._get_next_ind(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           if self._try_set(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):
             break






       def __setitem__(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         if self._is_full():
           self._size_up()

         self._add_item(__UpperCAmelCase    , __UpperCAmelCase      )






       def __delitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           lowerCAmelCase__			:int											=							self._buckets[ind]
           if item is None:
             raise KeyError(__UpperCAmelCase      )
           if item is _deleted:
             continue
           if item.key == key:
             lowerCAmelCase__			:List[str]											=							_deleted
             self._len -= 1
             break
         if self._is_sparse():
           self._size_down()






       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           lowerCAmelCase__			:str											=							self._buckets[ind]
           if item is None:
             break
           if item is _deleted:
             continue
           if item.key == key:
             return item.val
         raise KeyError(__UpperCAmelCase      )






       def __len__(    self      ):



         '''simple docstring'''

         return self._len






       def __iter__(    self      ):



         '''simple docstring'''

         yield from (item.key for item in self._buckets if item)






       def __repr__(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							' ,'.join(
             F"{item.key}: {item.val}" for item in self._buckets if item      )
         return F"HashMap({val_string})"
