



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,
			)


UpperCamelCase__			=    logging.get_logger(__name__)


UpperCamelCase__			=    Dict[str, Any]
UpperCamelCase__			=    List[Prediction]
@add_end_docstrings(_UpperCAmelCase      )
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			def __init__(					self						:							int      ,  *lowercase						:							List[str]      ,  **lowercase						:							Tuple )    ->       Any:
									"""simple docstring"""







									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 		snake_case__      (					self						:							List[str]      ,  **lowercase						:							List[Any] )    ->       Any:
									"""simple docstring"""







									__lowercase											=					{}
									__lowercase											=					{}
									if "subtask" in kwargs:
															__lowercase											=					kwargs["""subtask"""]
															__lowercase											=					kwargs["""subtask"""]
									if "threshold" in kwargs:
															__lowercase											=					kwargs["""threshold"""]
									if "mask_threshold" in kwargs:
															__lowercase											=					kwargs["""mask_threshold"""]
									if "overlap_mask_area_threshold" in kwargs:
															__lowercase											=					kwargs["""overlap_mask_area_threshold"""]

									return preprocess_kwargs, {}, postprocess_kwargs





			def __call__(					self						:							List[Any]      ,  lowercase						:							Dict      ,  **lowercase						:							Optional[Any] )    ->       Union[Predictions, List[Prediction]]:
									"""simple docstring"""







									return super().__call__(lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							Any      ,  lowercase						:							Any=None )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					load_image(lowercase )
									__lowercase											=					[(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
															if subtask is None:
																					__lowercase											=					{}
															else:
																					__lowercase											=					{"""task_inputs""": [subtask]}
															__lowercase											=					self.image_processor(images=[image]      ,  return_tensors="""pt"""      ,  **lowercase )
															__lowercase											=					self.tokenizer(
															    inputs["""task_inputs"""]      ,  padding="""max_length"""      ,  max_length=self.model.config.task_seq_len      ,  return_tensors=self.framework      ,  )["""input_ids"""]
									else:
															__lowercase											=					self.image_processor(images=[image]      ,  return_tensors="""pt""" )
									__lowercase											=					target_size
									return inputs





			def 		snake_case__      (					self						:							Any      ,  lowercase						:							str )    ->       str:
									"""simple docstring"""







									__lowercase											=					model_inputs.pop("""target_size""" )
									__lowercase											=					self.model(**lowercase )
									__lowercase											=					target_size
									return model_outputs







			def 		snake_case__      (					self						:							str      ,  lowercase						:							Union[str, Any]      ,  lowercase						:							Dict=None      ,  lowercase						:							Any=0.9      ,  lowercase						:							List[Any]=0.5      ,  lowercase						:							Dict=0.5 )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					None
									if subtask in {"panoptic", None} and hasattr(self.image_processor      ,  """post_process_panoptic_segmentation""" ):
															__lowercase											=					self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor      ,  """post_process_instance_segmentation""" ):
															__lowercase											=					self.image_processor.post_process_instance_segmentation

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

															__lowercase											=					[]
															__lowercase											=					outputs["""segmentation"""]

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

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

															__lowercase											=					[]
															__lowercase											=					outputs.numpy()
															__lowercase											=					np.unique(lowercase )

															for label in labels:
																					__lowercase											=					(segmentation == label) * 255
																					__lowercase											=					Image.fromarray(mask.astype(np.uinta )      ,  mode="""L""" )
																					__lowercase											=					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










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
UpperCamelCase__			=    "src/transformers"

# Matches is_xxx_available()
UpperCamelCase__			=    re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
UpperCamelCase__			=    re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
UpperCamelCase__			=    re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


UpperCamelCase__			=    "\n{0} = None\n"

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


UpperCamelCase__			=    "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"






def 			UpperCAmelCase__     (     lowercase__ )			->   List[Any]:
						if _re_test_backend.search(lowercase__ ) is None:
												return None
						__lowercase											=					[b[0] for b in _re_backend.findall(lowercase__ )]
						backends.sort()
						return "_and_".join(lowercase__ )






def 			UpperCAmelCase__     (     )			->   Dict:
						with open(os.path.join(lowercase__    ,							"""__init__.py""" )    ,							"""r"""    ,							encoding="""utf-8"""    ,							newline="""\n""" ) as f:
												__lowercase											=					f.readlines()

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

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

																		__lowercase											=					[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
																								__lowercase											=					lines[line_index]
																								__lowercase											=					_re_single_line_import.search(lowercase__ )
																								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

																		__lowercase											=					objects
												else:
																		line_index += 1

						return backend_specific_objects






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   int:
						if name.isupper():
												return DUMMY_CONSTANT.format(lowercase__ )
						elif name.islower():
												return DUMMY_FUNCTION.format(lowercase__    ,							lowercase__ )
						else:
												return DUMMY_CLASS.format(lowercase__    ,							lowercase__ )






def 			UpperCAmelCase__     (     lowercase__=None )			->   List[str]:
						if backend_specific_objects is None:
												__lowercase											=					read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						__lowercase											=					{}

						for backend, objects in backend_specific_objects.items():
												__lowercase											=					"""[""" + """, """.join(F"\"{b}\"" for b in backend.split("""_and_""" ) ) + """]"""
												__lowercase											=					"""# 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(lowercase__    ,							lowercase__ ) for o in objects] )
												__lowercase											=					dummy_file

						return dummy_files






def 			UpperCAmelCase__     (     lowercase__=False )			->   Optional[Any]:
						__lowercase											=					create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						__lowercase											=					{"""torch""": """pt"""}

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

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

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"Updating transformers.utils.dummy_{short_names.get(lowercase__    ,							lowercase__ )}_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(lowercase__    ,							lowercase__ )}_objects.py. Run `make fix-copies` "
																								    """to fix this.""" )


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

			check_dummies(args.fix_and_overwrite)





