




'''simple docstring'''


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

_snake_case : Union[str, Any]  				=						logging.get_logger(__name__)

@add_end_docstrings(_a       )
class      A       (						_a       ):







							def __init__(    self			:    Any				,     **lowerCAmelCase_			:    Dict   )     ->				Optional[int]:


														"""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			:    Tuple				,     **lowerCAmelCase_			:    Tuple   )     ->				Optional[int]:


														"""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			:    Dict				,     lowerCAmelCase_			:    Optional[Any]				,     *lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    str=None				,     **lowerCAmelCase_			:    Union[str, Any]   )     ->				List[Any]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Union[str, Any]=64				,     lowerCAmelCase_			:    int = 0				,     lowerCAmelCase_			:    float = 5_12 / 15_00				,     lowerCAmelCase_			:    Optional[int] = 32				,     lowerCAmelCase_			:    Optional[int] = 1				,     )     ->				Dict:


														"""simple docstring"""


														_a      	=							load_image(lowerCAmelCase_   )
														_a      	=							self.image_processor.size['''longest_edge''']
														_a	,				_a	,				_a	,				_a      	=							self.image_processor.generate_crop_boxes(
														    lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.image_processor(images=lowerCAmelCase_				,     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(lowerCAmelCase_				,     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				,     lowerCAmelCase_				,     lowerCAmelCase_   ):
																					_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   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    List[Any]=0.8_8				,     lowerCAmelCase_			:    Tuple=0.9_5				,     lowerCAmelCase_			:    str=0				,     lowerCAmelCase_			:    Optional[int]=1				,     )     ->				str:


														"""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(**lowerCAmelCase_   )

														# 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(
														    lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     binarize=lowerCAmelCase_   )
														_a      	=							model_outputs['''iou_scores''']
														_a	,				_a	,				_a      	=							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			:    str				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[Any]=False				,     lowerCAmelCase_			:    Union[str, Any]=False				,     lowerCAmelCase_			:    List[Any]=0.7				,     )     ->				int:


														"""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(lowerCAmelCase_   )
														_a      	=							torch.cat(lowerCAmelCase_   )
														_a	,				_a	,				_a	,				_a      	=							self.image_processor.post_process_for_mask_generation(
														    lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )

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

														_a      	=							{}
														if output_rle_mask:
																					_a      	=							rle_mask

														if output_bboxes_mask:
																					_a      	=							bounding_boxes

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







'''simple docstring'''


import requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

