

'''simple docstring'''






from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'efficientnet'






  def __init__(      self   :     List[Any]					,lowercase__   :     int = 3					,lowercase__   :     int = 6_0_0					,lowercase__   :     float = 2.0					,lowercase__   :     float = 3.1					,lowercase__   :     int = 8					,lowercase__   :     List[int] = [3, 3, 5, 3, 5, 5, 3]					,lowercase__   :     List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]					,lowercase__   :     List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]					,lowercase__   :     List[int] = []					,lowercase__   :     List[int] = [1, 2, 2, 2, 1, 2, 1]					,lowercase__   :     List[int] = [1, 2, 2, 3, 3, 4, 1]					,lowercase__   :     List[int] = [1, 6, 6, 6, 6, 6, 6]					,lowercase__   :     float = 0.2_5					,lowercase__   :     str = "swish"					,lowercase__   :     int = 2_5_6_0					,lowercase__   :     str = "mean"					,lowercase__   :     float = 0.0_2					,lowercase__   :     float = 0.0_0_1					,lowercase__   :     float = 0.9_9					,lowercase__   :     float = 0.5					,lowercase__   :     float = 0.2					,**lowercase__   :     int					,):
   super().__init__(**lowercase__			)

   __lowercase							  = num_channels
   __lowercase							  = image_size
   __lowercase							  = width_coefficient
   __lowercase							  = depth_coefficient
   __lowercase							  = depth_divisor
   __lowercase							  = kernel_sizes
   __lowercase							  = in_channels
   __lowercase							  = out_channels
   __lowercase							  = depthwise_padding
   __lowercase							  = strides
   __lowercase							  = num_block_repeats
   __lowercase							  = expand_ratios
   __lowercase							  = squeeze_expansion_ratio
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dim
   __lowercase							  = pooling_type
   __lowercase							  = initializer_range
   __lowercase							  = batch_norm_eps
   __lowercase							  = batch_norm_momentum
   __lowercase							  = dropout_rate
   __lowercase							  = drop_connect_rate
   __lowercase							  = sum(lowercase__			) * 4






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			version.parse('1.11'      )






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
       ]			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return 1e-5


"""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			   = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase  )
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			**_UpperCAmelCase							):
       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 _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      {}
       lowercase__:   List[Any]					=      {}
       lowercase__:   Dict					=      {}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowercase__:   Dict					=      kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
          lowercase__:   Any					=      kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
          lowercase__:   Optional[Any]					=      kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowercase__:   Any					=      kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
          lowercase__:   Tuple					=      kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
          lowercase__:   List[str]					=      kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
          lowercase__:   str					=      kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
          lowercase__:   List[str]					=      kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
          lowercase__:   Dict					=      kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
          lowercase__:   int					=      kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs






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






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=64    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = 512 / 1500    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 1    ,			):
       lowercase__:   Union[str, Any]					=      load_image(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.image_processor.size['''longest_edge''']
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      self.image_processor.generate_crop_boxes(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.image_processor(images=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       with self.device_placement():
          if self.framework == "pt":
             lowercase__:   Tuple					=      self.get_inference_context()
             with inference_context():
                lowercase__:   Optional[Any]					=      self._ensure_tensor_on_device(_UpperCAmelCase    ,			device=self.device							)
                lowercase__:   Any					=      self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
                lowercase__:   Tuple					=      image_embeddings

       lowercase__:   Optional[Any]					=      grid_points.shape[1]
       lowercase__:   Tuple					=      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							):
          lowercase__:   Dict					=      grid_points[:, i : i + points_per_batch, :, :]
          lowercase__:   int					=      input_labels[:, i : i + points_per_batch]
          lowercase__:   Any					=      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    ,			):
       lowercase__:   List[Any]					=      model_inputs.pop('''input_boxes'''							)
       lowercase__:   List[Any]					=      model_inputs.pop('''is_last'''							)
       lowercase__:   Any					=      model_inputs.pop('''original_sizes'''							).tolist()
       lowercase__:   Union[str, Any]					=      model_inputs.pop('''reshaped_input_sizes'''							).tolist()

       lowercase__:   List[Any]					=      self.model(**_UpperCAmelCase							)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       lowercase__:   int					=      model_outputs['''pred_masks''']
       lowercase__:   str					=      self.image_processor.post_process_masks(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			binarize=_UpperCAmelCase							)
       lowercase__:   str					=      model_outputs['''iou_scores''']
       lowercase__, lowercase__, lowercase__:   Optional[int]					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=0.7    ,			):
       lowercase__:   int					=      []
       lowercase__:   str					=      []
       lowercase__:   List[Any]					=      []
       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'''							)							)

       lowercase__:   Any					=      torch.cat(_UpperCAmelCase							)
       lowercase__:   Dict					=      torch.cat(_UpperCAmelCase							)
       lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      self.image_processor.post_process_for_mask_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

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

       lowercase__:   Any					=      {}
       if output_rle_mask:
          lowercase__:   Optional[Any]					=      rle_mask

       if output_bboxes_mask:
          lowercase__:   Optional[int]					=      bounding_boxes

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