






"""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

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





@add_end_docstrings(_lowerCamelCase       )
class       snake_case_       (    _lowerCamelCase       ):



 """simple docstring"""

 def __init__(      self  ,					**__a						):


     """simple docstring"""

     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       _UpperCAmelCase						(      self  ,					**__a						):


     """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  ,					__a  ,					*__a  ,					__a=None  ,					__a=None  ,					**__a						):


     """simple docstring"""

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


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a=64  ,					__a = 0  ,					__a = 512 / 1500  ,					__a = 32  ,					__a = 1  ,					):


     """simple docstring"""

     A__							 =       load_image(__a						)
     A__							 =       self.image_processor.size['longest_edge']
     A__ ,			A__ ,			A__ ,			A__							 =       self.image_processor.generate_crop_boxes(
         __a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						)
     A__							 =       self.image_processor(images=__a  ,					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(__a  ,					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  ,					__a  ,					__a						):
         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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0.88  ,					__a=0.95  ,					__a=0  ,					__a=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(**__a						)

     # 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(
         __a  ,					__a  ,					__a  ,					__a  ,					binarize=__a						)
     A__							 =       model_outputs['iou_scores']
     A__ ,			A__ ,			A__							 =       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       _UpperCAmelCase						(      self  ,					__a  ,					__a=False  ,					__a=False  ,					__a=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(__a						)
     A__							 =       torch.cat(__a						)
     A__ ,			A__ ,			A__ ,			A__							 =       self.image_processor.post_process_for_mask_generation(
         __a  ,					__a  ,					__a  ,					__a						)

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

     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 torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


