



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__   			=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase_   )
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Any					,       **_snake_case       :							str       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )
								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(_snake_case       )



					def      _a				(  self       :							str					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								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       :							Union[str, Any]					,       _snake_case       :							Tuple					,       *_snake_case       :							List[str]					,       _snake_case       :							Optional[int]=None					,       _snake_case       :							Union[str, Any]=None					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Tuple					,       _snake_case       :							Tuple					,       _snake_case       :							List[Any]=64					,       _snake_case       :							int = 0					,       _snake_case       :							float = 5_12 / 15_00					,       _snake_case       :							Optional[int] = 32					,       _snake_case       :							Optional[int] = 1					,       ):






								"""simple docstring"""


								A__	  =     load_image(_snake_case       )
								A__	  =     self.image_processor.size['longest_edge']
								A__  ,			A__  ,			A__  ,			A__	  =     self.image_processor.generate_crop_boxes(
								    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case       )
								A__	  =     self.image_processor(images=_snake_case					,       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(_snake_case					,       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					,       _snake_case					,       _snake_case       ):
											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      _a				(  self       :							Any					,       _snake_case       :							str					,       _snake_case       :							int=0.88					,       _snake_case       :							Union[str, Any]=0.95					,       _snake_case       :							Any=0					,       _snake_case       :							Dict=1					,       ):






								"""simple docstring"""


								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(**_snake_case       )

								# 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(
								    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       binarize=_snake_case       )
								A__	  =     model_outputs['iou_scores']
								A__  ,			A__  ,			A__	  =     self.image_processor.filter_masks(
								    masks[0]					,       iou_scores[0]					,       original_sizes[0]					,       input_boxes[0]					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       )
								return {
								    "masks": masks,
								    "is_last": is_last,
								    "boxes": boxes,
								    "iou_scores": iou_scores,
								}



					def      _a				(  self       :							Dict					,       _snake_case       :							int					,       _snake_case       :							Optional[int]=False					,       _snake_case       :							Union[str, Any]=False					,       _snake_case       :							Tuple=0.7					,       ):






								"""simple docstring"""


								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(_snake_case       )
								A__	  =     torch.cat(_snake_case       )
								A__  ,			A__  ,			A__  ,			A__	  =     self.image_processor.post_process_for_mask_generation(
								    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case       )

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

								A__	  =     {}
								if output_rle_mask:
											A__	  =     rle_mask

								if output_bboxes_mask:
											A__	  =     bounding_boxes

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









SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




