





'''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:					Tuple							=				logging.get_logger(__name__)






@add_end_docstrings(UpperCAmelCase_							)
class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			**__A					):
     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 					__lowerCamelCase						(  self     ,			**__A					):
     __UpperCAmelCase					=   {}
     __UpperCAmelCase					=   {}
     __UpperCAmelCase					=   {}
     # preprocess args
     if "points_per_batch" in kwargs:
      __UpperCAmelCase					=   kwargs['points_per_batch']
     if "points_per_crop" in kwargs:
      __UpperCAmelCase					=   kwargs['points_per_crop']
     if "crops_n_layers" in kwargs:
      __UpperCAmelCase					=   kwargs['crops_n_layers']
     if "crop_overlap_ratio" in kwargs:
      __UpperCAmelCase					=   kwargs['crop_overlap_ratio']
     if "crop_n_points_downscale_factor" in kwargs:
      __UpperCAmelCase					=   kwargs['crop_n_points_downscale_factor']
     # postprocess args
     if "pred_iou_thresh" in kwargs:
      __UpperCAmelCase					=   kwargs['pred_iou_thresh']
     if "stability_score_offset" in kwargs:
      __UpperCAmelCase					=   kwargs['stability_score_offset']
     if "mask_threshold" in kwargs:
      __UpperCAmelCase					=   kwargs['mask_threshold']
     if "stability_score_thresh" in kwargs:
      __UpperCAmelCase					=   kwargs['stability_score_thresh']
     if "crops_nms_thresh" in kwargs:
      __UpperCAmelCase					=   kwargs['crops_nms_thresh']
     if "output_rle_mask" in kwargs:
      __UpperCAmelCase					=   kwargs['output_rle_mask']
     if "output_bboxes_mask" in kwargs:
      __UpperCAmelCase					=   kwargs['output_bboxes_mask']
     return preprocess_kwargs, forward_params, postprocess_kwargs



    def __call__(  self     ,			__A     ,			*__A     ,			__A=None     ,			__A=None     ,			**__A					):
     return super().__call__(__A     ,			*__A     ,			num_workers=__A     ,			batch_size=__A     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=64     ,			__A = 0     ,			__A = 512 / 1_500     ,			__A = 32     ,			__A = 1     ,			):
     __UpperCAmelCase					=   load_image(__A					)
     __UpperCAmelCase					=   self.image_processor.size['longest_edge']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   self.image_processor.generate_crop_boxes(
         __A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					)
     __UpperCAmelCase					=   self.image_processor(images=__A     ,			return_tensors='pt'					)

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

     __UpperCAmelCase					=   grid_points.shape[1]
     __UpperCAmelCase					=   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					):
      __UpperCAmelCase					=   grid_points[:, i : i + points_per_batch, :, :]
      __UpperCAmelCase					=   input_labels[:, i : i + points_per_batch]
      __UpperCAmelCase					=   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     ,			__A     ,			__A=0.8_8     ,			__A=0.9_5     ,			__A=0     ,			__A=1     ,			):
     __UpperCAmelCase					=   model_inputs.pop('input_boxes'					)
     __UpperCAmelCase					=   model_inputs.pop('is_last'					)
     __UpperCAmelCase					=   model_inputs.pop('original_sizes'					).tolist()
     __UpperCAmelCase					=   model_inputs.pop('reshaped_input_sizes'					).tolist()

     __UpperCAmelCase					=   self.model(**__A					)

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     __UpperCAmelCase					=   model_outputs['pred_masks']
     __UpperCAmelCase					=   self.image_processor.post_process_masks(
         __A     ,			__A     ,			__A     ,			__A     ,			binarize=__A					)
     __UpperCAmelCase					=   model_outputs['iou_scores']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A=False     ,			__A=False     ,			__A=0.7     ,			):
     __UpperCAmelCase					=   []
     __UpperCAmelCase					=   []
     __UpperCAmelCase					=   []
     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'					)					)

     __UpperCAmelCase					=   torch.cat(__A					)
     __UpperCAmelCase					=   torch.cat(__A					)
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   self.image_processor.post_process_for_mask_generation(
         __A     ,			__A     ,			__A     ,			__A					)

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

     __UpperCAmelCase					=   {}
     if output_rle_mask:
      __UpperCAmelCase					=   rle_mask

     if output_bboxes_mask:
      __UpperCAmelCase					=   bounding_boxes

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












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_A:					List[str]							=				{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			int						   =       """roc_bert"""



    def __init__(  self     ,			__A=30_522     ,			__A=768     ,			__A=12     ,			__A=12     ,			__A=3_072     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=2     ,			__A=0.0_2     ,			__A=1E-12     ,			__A=True     ,			__A=0     ,			__A="absolute"     ,			__A=None     ,			__A=True     ,			__A=True     ,			__A=768     ,			__A=910     ,			__A=512     ,			__A=24_858     ,			__A=True     ,			**__A     ,			):
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   hidden_size
     __UpperCAmelCase					=   num_hidden_layers
     __UpperCAmelCase					=   num_attention_heads
     __UpperCAmelCase					=   intermediate_size
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   hidden_dropout_prob
     __UpperCAmelCase					=   attention_probs_dropout_prob
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   type_vocab_size
     __UpperCAmelCase					=   layer_norm_eps
     __UpperCAmelCase					=   use_cache
     __UpperCAmelCase					=   enable_pronunciation
     __UpperCAmelCase					=   enable_shape
     __UpperCAmelCase					=   pronunciation_embed_dim
     __UpperCAmelCase					=   pronunciation_vocab_size
     __UpperCAmelCase					=   shape_embed_dim
     __UpperCAmelCase					=   shape_vocab_size
     __UpperCAmelCase					=   concat_input
     __UpperCAmelCase					=   position_embedding_type
     __UpperCAmelCase					=   classifier_dropout
     super().__init__(pad_token_id=__A     ,			**__A					)





