






'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__A      :       Union[str, Any]      =	'src/transformers'

# Matches is_xxx_available()
__A      :       Tuple      =	re.compile(r'is\_([a-z_]*)_available()')
# Matches from xxx import bla
__A      :       Dict      =	re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
__A      :       List[Any]      =	re.compile(r'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


__A      :       Union[str, Any]      =	'\n{0} = None\n'

__A      :       Any      =	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


__A      :       List[Any]      =	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


def 						_UpperCamelCase							(     snake_case__							):
     if _re_test_backend.search(snake_case__							) is None:
          return None
     __A		   =      [b[0] for b in _re_backend.findall(snake_case__							)]
     backends.sort()
     return "_and_".join(snake_case__							)


def 						_UpperCamelCase							(     ):
     with open(os.path.join(snake_case__							,			"__init__.py"							)							,			"r"							,			encoding="utf-8"							,			newline="\n"							) as f:
          __A		   =      f.readlines()

     # Get to the point we do the actual imports for type checking
     __A		   =      0
     while not lines[line_index].startswith("if TYPE_CHECKING"							):
          line_index += 1

     __A		   =      {}
     # Go through the end of the file
     while line_index < len(snake_case__							):
          # If the line is an if is_backend_available, we grab all objects associated.
          __A		   =      find_backend(lines[line_index]							)
          if backend is not None:
               while not lines[line_index].startswith("    else:"							):
                    line_index += 1
               line_index += 1

               __A		   =      []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 8							):
                    __A		   =      lines[line_index]
                    __A		   =      _re_single_line_import.search(snake_case__							)
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(", "							)							)
                    elif line.startswith(" " * 12							):
                         objects.append(line[12:-2]							)
                    line_index += 1

               __A		   =      objects
          else:
               line_index += 1

     return backend_specific_objects


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
     if name.isupper():
          return DUMMY_CONSTANT.format(snake_case__							)
     elif name.islower():
          return DUMMY_FUNCTION.format(snake_case__							,			snake_case__							)
     else:
          return DUMMY_CLASS.format(snake_case__							,			snake_case__							)


def 						_UpperCamelCase							(     snake_case__=None							):
     if backend_specific_objects is None:
          __A		   =      read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __A		   =      {}

     for backend, objects in backend_specific_objects.items():
          __A		   =      "[" + ", ".join(f"\"{b}\"" for b in backend.split("_and_"							)							) + "]"
          __A		   =      "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(snake_case__							,			snake_case__							) for o in objects]							)
          __A		   =      dummy_file

     return dummy_files


def 						_UpperCamelCase							(     snake_case__=False							):
     __A		   =      create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __A		   =      {"torch": "pt"}

     # Locate actual dummy modules and read their content.
     __A		   =      os.path.join(snake_case__							,			"utils"							)
     __A		   =      {
         backend: os.path.join(snake_case__							,			f"dummy_{short_names.get(snake_case__							,			snake_case__							)}_objects.py"							)
         for backend in dummy_files.keys()
     }

     __A		   =      {}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(snake_case__							):
               with open(snake_case__							,			"r"							,			encoding="utf-8"							,			newline="\n"							) as f:
                    __A		   =      f.read()
          else:
               __A		   =      ""

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        f"Updating transformers.utils.dummy_{short_names.get(snake_case__							,			snake_case__							)}_objects.py as the main "
                        "__init__ has new objects."							)
                    with open(dummy_file_paths[backend]							,			"w"							,			encoding="utf-8"							,			newline="\n"							) as f:
                         f.write(dummy_files[backend]							)
               else:
                    raise ValueError(
                        "The main __init__ has objects that are not present in "
                        f"transformers.utils.dummy_{short_names.get(snake_case__							,			snake_case__							)}_objects.py. Run `make fix-copies` "
                        "to fix this."							)


if __name__ == "__main__":
       __A      :       int      =	argparse.ArgumentParser()
       parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
       __A      :       Tuple      =	parser.parse_args()

       check_dummies(args.fix_and_overwrite)










'''simple docstring'''







from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       from ..models.auto.modeling_auto import (
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
           MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
           MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
           MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
       )


__A      :       int      =	logging.get_logger(__name__)


__A      :       Optional[int]      =	Dict[str, Any]
__A      :       Any      =	List[Prediction]






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           if self.framework == "tf":
                 raise ValueError(f"The {self.__class__} is only available in PyTorch."					)

           requires_backends(self				,				"vision"					)
           self.check_model_type(
               dict(
                   MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)






     def     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]=None					)  ->       Any:
           __A		   =      load_image(lowercase__					)
           __A		   =      [(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                       __A		   =      {}
                 else:
                       __A		   =      {"task_inputs": [subtask]}
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"				,				**lowercase__					)
                 __A		   =      self.tokenizer(
                     inputs["task_inputs"]				,				padding="max_length"				,				max_length=self.model.config.task_seq_len				,				return_tensors=self.framework				,				)["input_ids"]
           else:
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"					)
           __A		   =      target_size
           return inputs






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 __A		   =      fn(
                     lowercase__				,				threshold=lowercase__				,				mask_threshold=lowercase__				,				overlap_mask_area_threshold=lowercase__				,				target_sizes=model_outputs["target_size"]				,				)[0]

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                       __A		   =      (segmentation == segment["id"]) * 255
                       __A		   =      Image.fromarray(mask.numpy().astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[segment["label_id"]]
                       __A		   =      segment["score"]
                       annotation.append({"score": score, "label": label, "mask": mask}					)

           elif subtask in {"semantic", None} and hasattr(self.image_processor				,				"post_process_semantic_segmentation"					):
                 __A		   =      self.image_processor.post_process_semantic_segmentation(
                     lowercase__				,				target_sizes=model_outputs["target_size"]					)[0]

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[label]
                       annotation.append({"score": None, "label": label, "mask": mask}					)
           else:
                 raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model					)}"					)
           return annotation


