


'''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

a_						:       Dict      			=				logging.get_logger(__name__)





@add_end_docstrings(__UpperCAmelCase    )
class   snake_case					(					__UpperCAmelCase    ):





  """simple docstring"""




  def __init__(					self    ,						**UpperCamelCase  ):







   """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 						snake_case	(					self    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   lowerCamelCase_  				=						{}
   lowerCamelCase_  				=						{}
   # preprocess args
   if "points_per_batch" in kwargs:
    lowerCamelCase_  				=						kwargs["points_per_batch"]
   if "points_per_crop" in kwargs:
    lowerCamelCase_  				=						kwargs["points_per_crop"]
   if "crops_n_layers" in kwargs:
    lowerCamelCase_  				=						kwargs["crops_n_layers"]
   if "crop_overlap_ratio" in kwargs:
    lowerCamelCase_  				=						kwargs["crop_overlap_ratio"]
   if "crop_n_points_downscale_factor" in kwargs:
    lowerCamelCase_  				=						kwargs["crop_n_points_downscale_factor"]
   # postprocess args
   if "pred_iou_thresh" in kwargs:
    lowerCamelCase_  				=						kwargs["pred_iou_thresh"]
   if "stability_score_offset" in kwargs:
    lowerCamelCase_  				=						kwargs["stability_score_offset"]
   if "mask_threshold" in kwargs:
    lowerCamelCase_  				=						kwargs["mask_threshold"]
   if "stability_score_thresh" in kwargs:
    lowerCamelCase_  				=						kwargs["stability_score_thresh"]
   if "crops_nms_thresh" in kwargs:
    lowerCamelCase_  				=						kwargs["crops_nms_thresh"]
   if "output_rle_mask" in kwargs:
    lowerCamelCase_  				=						kwargs["output_rle_mask"]
   if "output_bboxes_mask" in kwargs:
    lowerCamelCase_  				=						kwargs["output_bboxes_mask"]
   return preprocess_kwargs, forward_params, postprocess_kwargs






  def __call__(					self    ,						UpperCamelCase    ,						*UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						**UpperCamelCase  ):







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






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=64    ,						UpperCamelCase = 0    ,						UpperCamelCase = 512 / 1500    ,						UpperCamelCase = 32    ,						UpperCamelCase = 1    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(lowerCAmelCase_  )
   lowerCamelCase_  				=						self.image_processor.size["longest_edge"]
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor.generate_crop_boxes(
       lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_  )
   lowerCamelCase_  				=						self.image_processor(images=lowerCAmelCase_    ,						return_tensors="pt"  )

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

   lowerCamelCase_  				=						grid_points.shape[1]
   lowerCamelCase_  				=						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_  ):
    lowerCamelCase_  				=						grid_points[:, i : i + points_per_batch, :, :]
    lowerCamelCase_  				=						input_labels[:, i : i + points_per_batch]
    lowerCamelCase_  				=						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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=0.88    ,						UpperCamelCase=0.95    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						model_inputs.pop("input_boxes"  )
   lowerCamelCase_  				=						model_inputs.pop("is_last"  )
   lowerCamelCase_  				=						model_inputs.pop("original_sizes"  ).tolist()
   lowerCamelCase_  				=						model_inputs.pop("reshaped_input_sizes"  ).tolist()

   lowerCamelCase_  				=						self.model(**lowerCAmelCase_  )

   # post processing happens here in order to avoid CPU GPU copies of ALL the masks
   lowerCamelCase_  				=						model_outputs["pred_masks"]
   lowerCamelCase_  				=						self.image_processor.post_process_masks(
       lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						binarize=lowerCAmelCase_  )
   lowerCamelCase_  				=						model_outputs["iou_scores"]
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=0.7    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   lowerCamelCase_  				=						[]
   lowerCamelCase_  				=						[]
   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"  )  )

   lowerCamelCase_  				=						torch.cat(lowerCAmelCase_  )
   lowerCamelCase_  				=						torch.cat(lowerCAmelCase_  )
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor.post_process_for_mask_generation(
       lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_  )

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

   lowerCamelCase_  				=						{}
   if output_rle_mask:
    lowerCamelCase_  				=						rle_mask

   if output_bboxes_mask:
    lowerCamelCase_  				=						bounding_boxes

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







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[r"pooler", r"logit_scale"]
  _lowerCamelCase						=						[r"position_ids", r"predictions.decoder.bias"]
  _lowerCamelCase						=						"roberta"
  _lowerCamelCase						=						RobertaSeriesConfig






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.base_model(
       input_ids=UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_attentions=UpperCamelCase    ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,						return_dict=UpperCamelCase    ,						)

   if self.has_pre_transformation:
    lowerCamelCase_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						self.transformation_pre(UpperCamelCase  )

    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)
   else:
    lowerCamelCase_  				=						self.transformation(outputs.last_hidden_state  )
    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)



