





"""simple docstring"""





import argparse
import os
import re


SCREAMING_SNAKE_CASE__					=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__					=   re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__					=   re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[Any] = False			):







      '''simple docstring'''




      with open(_SCREAMING_SNAKE_CASE							,						"""r"""							,						encoding="""utf-8"""			) as f:
            lowerCAmelCase  						=       f.read()

      lowerCAmelCase  						=       content.split("""\n"""			)
      lowerCAmelCase  						=       []
      lowerCAmelCase  						=       0
      while line_idx < len(_SCREAMING_SNAKE_CASE			):
            if _re_intro_mapping.search(lines[line_idx]			) is not None:
                  lowerCAmelCase  						=       len(re.search(R"""^(\s*)\S"""							,						lines[line_idx]			).groups()[0]			) + 8
                  # Start of a new mapping!
                  while not lines[line_idx].startswith(""" """ * indent + """("""			):
                        new_lines.append(lines[line_idx]			)
                        line_idx += 1

                  lowerCAmelCase  						=       []
                  while lines[line_idx].strip() != "]":
                        # Blocks either fit in one line or not
                        if lines[line_idx].strip() == "(":
                              lowerCAmelCase  						=       line_idx
                              while not lines[line_idx].startswith(""" """ * indent + """)"""			):
                                    line_idx += 1
                              blocks.append("""\n""".join(lines[start_idx : line_idx + 1]			)			)
                        else:
                              blocks.append(lines[line_idx]			)
                        line_idx += 1

                  # Sort blocks by their identifiers
                  lowerCAmelCase  						=       sorted(_SCREAMING_SNAKE_CASE							,						key=lambda SCREAMING_SNAKE_CASE			: _re_identifier.search(_SCREAMING_SNAKE_CASE			).groups()[0]			)
                  new_lines += blocks
            else:
                  new_lines.append(lines[line_idx]			)
                  line_idx += 1

      if overwrite:
            with open(_SCREAMING_SNAKE_CASE							,						"""w"""							,						encoding="""utf-8"""			) as f:
                  f.write("""\n""".join(_SCREAMING_SNAKE_CASE			)			)
      elif "\n".join(_SCREAMING_SNAKE_CASE			) != content:
            return True


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str = False			):







      '''simple docstring'''




      lowerCAmelCase  						=       [os.path.join(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			) for f in os.listdir(_SCREAMING_SNAKE_CASE			) if f.endswith(""".py"""			)]
      lowerCAmelCase  						=       [sort_auto_mapping(_SCREAMING_SNAKE_CASE							,						overwrite=_SCREAMING_SNAKE_CASE			) for fname in fnames]

      if not overwrite and any(_SCREAMING_SNAKE_CASE			):
            lowerCAmelCase  						=       [f for f, d in zip(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			) if d]
            raise ValueError(
                F'The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE			)}. Run `make style` to fix'
                """ this."""			)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   argparse.ArgumentParser()
       parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)













"""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(a       )
class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         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      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							{}
         lowerCAmelCase__			:Tuple											=							{}
         lowerCAmelCase__			:Any											=							{}
         # preprocess args
         if "points_per_batch" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['points_per_batch']
         if "points_per_crop" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['points_per_crop']
         if "crops_n_layers" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crops_n_layers']
         if "crop_overlap_ratio" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crop_overlap_ratio']
         if "crop_n_points_downscale_factor" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['crop_n_points_downscale_factor']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
           lowerCAmelCase__			:Tuple											=							kwargs['pred_iou_thresh']
         if "stability_score_offset" in kwargs:
           lowerCAmelCase__			:Optional[int]											=							kwargs['stability_score_offset']
         if "mask_threshold" in kwargs:
           lowerCAmelCase__			:List[Any]											=							kwargs['mask_threshold']
         if "stability_score_thresh" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							kwargs['stability_score_thresh']
         if "crops_nms_thresh" in kwargs:
           lowerCAmelCase__			:int											=							kwargs['crops_nms_thresh']
         if "output_rle_mask" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['output_rle_mask']
         if "output_bboxes_mask" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							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__(__UpperCAmelCase    , *__UpperCAmelCase    , num_workers=__UpperCAmelCase    , batch_size=__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=6_4    , __UpperCAmelCase = 0    , __UpperCAmelCase = 5_1_2 / 1_5_0_0    , __UpperCAmelCase = 3_2    , __UpperCAmelCase = 1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							load_image(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							self.image_processor.size['longest_edge']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:int											=							self.image_processor.generate_crop_boxes(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							self.image_processor(images=__UpperCAmelCase    , return_tensors='pt'      )

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

         lowerCAmelCase__			:List[Any]											=							grid_points.shape[1]
         lowerCAmelCase__			:Union[str, Any]											=							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      ):
           lowerCAmelCase__			:Optional[Any]											=							grid_points[:, i : i + points_per_batch, :, :]
           lowerCAmelCase__			:List[str]											=							input_labels[:, i : i + points_per_batch]
           lowerCAmelCase__			:List[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    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							model_inputs.pop('input_boxes'      )
         lowerCAmelCase__			:Optional[int]											=							model_inputs.pop('is_last'      )
         lowerCAmelCase__			:Dict											=							model_inputs.pop('original_sizes'      ).tolist()
         lowerCAmelCase__			:Dict											=							model_inputs.pop('reshaped_input_sizes'      ).tolist()

         lowerCAmelCase__			:Optional[int]											=							self.model(**__UpperCAmelCase      )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         lowerCAmelCase__			:int											=							model_outputs['pred_masks']
         lowerCAmelCase__			:Optional[Any]											=							self.image_processor.post_process_masks(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , binarize=__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							model_outputs['iou_scores']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							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    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[]
         lowerCAmelCase__			:Optional[Any]											=							[]
         lowerCAmelCase__			:int											=							[]
         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__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							self.image_processor.post_process_for_mask_generation(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

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

         lowerCAmelCase__			:Optional[int]											=							{}
         if output_rle_mask:
           lowerCAmelCase__			:str											=							rle_mask

         if output_bboxes_mask:
           lowerCAmelCase__			:Optional[int]											=							bounding_boxes

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