


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

_A			=						logging.get_logger(__name__)






@add_end_docstrings(A_		)
class    UpperCAmelCase__	( A_		):



				"""simple docstring"""






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






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






				def _a (      self ,       A_ ,       A_=64 ,       A_ = 0 ,       A_ = 512 / 1500 ,       A_ = 32 ,       A_ = 1 ,       )  ->	List[str]:
									__UpperCamelCase   							=load_image(A_   )
									__UpperCamelCase   							=self.image_processor.size['longest_edge']
									__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self.image_processor.generate_crop_boxes(
									    A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )
									__UpperCamelCase   							=self.image_processor(images=A_ ,       return_tensors='pt'   )

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

									__UpperCamelCase   							=grid_points.shape[1]
									__UpperCamelCase   							=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_   ):
														__UpperCamelCase   							=grid_points[:, i : i + points_per_batch, :, :]
														__UpperCamelCase   							=input_labels[:, i : i + points_per_batch]
														__UpperCamelCase   							=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 _a (      self ,       A_ ,       A_=0.88 ,       A_=0.95 ,       A_=0 ,       A_=1 ,       )  ->	Any:
									__UpperCamelCase   							=model_inputs.pop('input_boxes'   )
									__UpperCamelCase   							=model_inputs.pop('is_last'   )
									__UpperCamelCase   							=model_inputs.pop('original_sizes'   ).tolist()
									__UpperCamelCase   							=model_inputs.pop('reshaped_input_sizes'   ).tolist()

									__UpperCamelCase   							=self.model(**A_   )

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

									__UpperCamelCase   							=torch.cat(A_   )
									__UpperCamelCase   							=torch.cat(A_   )
									__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self.image_processor.post_process_for_mask_generation(
									    A_ ,       A_ ,       A_ ,       A_   )

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

									__UpperCamelCase   							={}
									if output_rle_mask:
														__UpperCamelCase   							=rle_mask

									if output_bboxes_mask:
														__UpperCamelCase   							=bounding_boxes

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









from __future__ import annotations

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if b == 0:
          return (1, 0)
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					a % b    )
     __UpperCamelCase   							=a // b
     return (y, x - k * y)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=na * na
     __UpperCamelCase   							=ra * x * na + ra * y * na
     return (n % m + m) % m

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     if b < 0:
          __UpperCamelCase   							=(b % n + n) % n
     return b

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase							,				__UpperCamelCase   							=invert_modulo(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ), invert_modulo(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=na * na
     __UpperCamelCase   							=ra * x * na + ra * y * na
     return (n % m + m) % m


if __name__ == "__main__":
   from doctest import testmod

   testmod(name='chinese_remainder_theorem', verbose=True)
   testmod(name='chinese_remainder_theorem2', verbose=True)
   testmod(name='invert_modulo', verbose=True)
   testmod(name='extended_euclid', verbose=True)





