





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[Any]  							= logging.get_logger(__name__)


@add_end_docstrings(__lowerCamelCase      )
class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def __init__( self     :					Tuple,			**_UpperCAmelCase     :					int						)				->			Union[str, 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  A_     ( self     :					Union[str, Any],			**_UpperCAmelCase     :					str						)				->			Union[str, Any]:





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






							def __call__( self     :					Any,			_UpperCAmelCase     :					Any,			*_UpperCAmelCase     :					str,			_UpperCAmelCase     :					int=None,			_UpperCAmelCase     :					Tuple=None,			**_UpperCAmelCase     :					Dict						)				->			int:





														"""simple docstring"""
														return super().__call__(_UpperCAmelCase,			*_UpperCAmelCase,			num_workers=_UpperCAmelCase,			batch_size=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					str=6_4,			_UpperCAmelCase     :					int = 0,			_UpperCAmelCase     :					float = 5_1_2 / 1_5_0_0,			_UpperCAmelCase     :					Optional[int] = 3_2,			_UpperCAmelCase     :					Optional[int] = 1,			)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						load_image(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						self.image_processor.size["longest_edge"]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.image_processor.generate_crop_boxes(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.image_processor(images=_UpperCAmelCase,			return_tensors="pt"						)

														with self.device_placement():
																					if self.framework == "pt":
																												SCREAMING_SNAKE_CASE__       :      int     =						self.get_inference_context()
																												with inference_context():
																																			SCREAMING_SNAKE_CASE__       :      Any     =						self._ensure_tensor_on_device(_UpperCAmelCase,			device=self.device						)
																																			SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model.get_image_embeddings(model_inputs.pop("pixel_values"						)						)
																																			SCREAMING_SNAKE_CASE__       :      List[str]     =						image_embeddings

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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						model_inputs.pop("input_boxes"						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						model_inputs.pop("is_last"						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						model_inputs.pop("original_sizes"						).tolist()
														SCREAMING_SNAKE_CASE__       :      str     =						model_inputs.pop("reshaped_input_sizes"						).tolist()

														SCREAMING_SNAKE_CASE__       :      str     =						self.model(**_UpperCAmelCase						)

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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						[]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[]
														SCREAMING_SNAKE_CASE__       :      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"						)						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.cat(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.cat(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.image_processor.post_process_for_mask_generation(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						defaultdict(_UpperCAmelCase						)
														for output in model_outputs:
																					for k, v in output.items():
																												extra[k].append(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						{}
														if output_rle_mask:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						rle_mask

														if output_bboxes_mask:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						bounding_boxes

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






