






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

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

@add_end_docstrings(__a			)
class 		lowerCAmelCase     ( __a			):

     '''simple docstring'''

     def __init__(  self				:	Tuple		,     **__a				:	List[Any]							)			->  List[Any]:


            """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       lowerCAmelCase				(  self				:	Optional[Any]		,     **__a				:	int							)			->  List[str]:


            """simple docstring"""

            __lowercase      :							List[Any]				=     {}
            __lowercase      :							Tuple				=     {}
            __lowercase      :							Union[str, Any]				=     {}
            # preprocess args
            if "points_per_batch" in kwargs:
                   __lowercase      :							str				=     kwargs["""points_per_batch"""]
            if "points_per_crop" in kwargs:
                   __lowercase      :							Optional[int]				=     kwargs["""points_per_crop"""]
            if "crops_n_layers" in kwargs:
                   __lowercase      :							List[Any]				=     kwargs["""crops_n_layers"""]
            if "crop_overlap_ratio" in kwargs:
                   __lowercase      :							List[str]				=     kwargs["""crop_overlap_ratio"""]
            if "crop_n_points_downscale_factor" in kwargs:
                   __lowercase      :							Optional[int]				=     kwargs["""crop_n_points_downscale_factor"""]
            # postprocess args
            if "pred_iou_thresh" in kwargs:
                   __lowercase      :							List[str]				=     kwargs["""pred_iou_thresh"""]
            if "stability_score_offset" in kwargs:
                   __lowercase      :							str				=     kwargs["""stability_score_offset"""]
            if "mask_threshold" in kwargs:
                   __lowercase      :							List[Any]				=     kwargs["""mask_threshold"""]
            if "stability_score_thresh" in kwargs:
                   __lowercase      :							Tuple				=     kwargs["""stability_score_thresh"""]
            if "crops_nms_thresh" in kwargs:
                   __lowercase      :							Tuple				=     kwargs["""crops_nms_thresh"""]
            if "output_rle_mask" in kwargs:
                   __lowercase      :							Any				=     kwargs["""output_rle_mask"""]
            if "output_bboxes_mask" in kwargs:
                   __lowercase      :							Tuple				=     kwargs["""output_bboxes_mask"""]
            return preprocess_kwargs, forward_params, postprocess_kwargs



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


            """simple docstring"""

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



     def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Dict		,     __a				:	Optional[Any]=64		,     __a				:	Any = 0		,     __a				:	List[str] = 512 / 1500		,     __a				:	List[Any] = 32		,     __a				:	Optional[Any] = 1		,     )			->  Union[str, Any]:


            """simple docstring"""

            __lowercase      :							List[Any]				=     load_image(_lowerCAmelCase							)
            __lowercase      :							Optional[Any]				=     self.image_processor.size["""longest_edge"""]
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Tuple				=     self.image_processor.generate_crop_boxes(
                _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase							)
            __lowercase      :							Optional[int]				=     self.image_processor(images=_lowerCAmelCase		,     return_tensors="""pt"""							)

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

            __lowercase      :							str				=     grid_points.shape[1]
            __lowercase      :							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		,     _lowerCAmelCase		,     _lowerCAmelCase							):
                   __lowercase      :							Optional[int]				=     grid_points[:, i : i + points_per_batch, :, :]
                   __lowercase      :							int				=     input_labels[:, i : i + points_per_batch]
                   __lowercase      :							Optional[int]				=     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				:	Any		,     __a				:	Tuple		,     __a				:	Optional[Any]=0.88		,     __a				:	List[str]=0.95		,     __a				:	str=0		,     __a				:	Dict=1		,     )			->  Optional[Any]:


            """simple docstring"""

            __lowercase      :							Union[str, Any]				=     model_inputs.pop("""input_boxes"""							)
            __lowercase      :							Dict				=     model_inputs.pop("""is_last"""							)
            __lowercase      :							Any				=     model_inputs.pop("""original_sizes"""							).tolist()
            __lowercase      :							Union[str, Any]				=     model_inputs.pop("""reshaped_input_sizes"""							).tolist()

            __lowercase      :							Dict				=     self.model(**_lowerCAmelCase							)

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


            """simple docstring"""

            __lowercase      :							Dict				=     []
            __lowercase      :							Any				=     []
            __lowercase      :							List[Any]				=     []
            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(_lowerCAmelCase							)
            __lowercase      :							List[Any]				=     torch.cat(_lowerCAmelCase							)
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							int				=     self.image_processor.post_process_for_mask_generation(
                _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase							)

            __lowercase      :							Optional[int]				=     defaultdict(_lowerCAmelCase							)
            for output in model_outputs:
                   for k, v in output.items():
                          extra[k].append(_lowerCAmelCase							)

            __lowercase      :							int				=     {}
            if output_rle_mask:
                   __lowercase      :							str				=     rle_mask

            if output_bboxes_mask:
                   __lowercase      :							str				=     bounding_boxes

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







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)