


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							     =       logging.get_logger(__name__)


_A							     =       Dict[str, Any]
_A							     =       List[Prediction]

@add_end_docstrings(_SCREAMING_SNAKE_CASE    )
class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ):
								super().__init__(*_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    )

								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   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								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			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ):
								return super().__call__(_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None    ):
								a_     			=   load_image(_SCREAMING_SNAKE_CASE    )
								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"""			,       **_SCREAMING_SNAKE_CASE    )
									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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   model_inputs.pop("""target_size"""    )
								a_     			=   self.model(**_SCREAMING_SNAKE_CASE    )
								a_     			=   target_size
								return model_outputs





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=0.9			,       _SCREAMING_SNAKE_CASE=0.5			,       _SCREAMING_SNAKE_CASE=0.5    ):
								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(
									    _SCREAMING_SNAKE_CASE			,       threshold=_SCREAMING_SNAKE_CASE			,       mask_threshold=_SCREAMING_SNAKE_CASE			,       overlap_mask_area_threshold=_SCREAMING_SNAKE_CASE			,       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(
									    _SCREAMING_SNAKE_CASE			,       target_sizes=model_outputs["""target_size"""]    )[0]

									a_     			=   []
									a_     			=   outputs.numpy()
									a_     			=   np.unique(_SCREAMING_SNAKE_CASE    )

									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



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A							     =       set()
_A							     =       get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[str] = None	)     ->	Optional[int]:



	"""simple docstring"""






	def decorator(UpperCamelCase		:      Callable	):
		global _emitted_deprecation_warnings

		if inspect.isclass(UpperCamelCase	):
			a_     			=   deprecated_class_or_function.__init__
			a_     			=   deprecated_class_or_function.__name__
		else:
			a_     			=   deprecated_class_or_function
			a_     			=   deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			a_     			=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

		a_     			=   (
		    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
		    if help_message
		    else """"""
		)

		@wraps(UpperCamelCase	)
		def wrapper(*UpperCamelCase		:      List[str]     ,					**UpperCamelCase		:      Union[str, Any]	):
			a_     			=   hash(UpperCamelCase	)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(UpperCamelCase     ,					category=UpperCamelCase     ,					stacklevel=2	)
				_emitted_deprecation_warnings.add(UpperCamelCase	)
			return deprecated_function(*UpperCamelCase     ,					**UpperCamelCase	)

		a_     			=   """deprecated"""

		if inspect.isclass(UpperCamelCase	):
			a_     			=   wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class 			lowerCamelCase_					(    enum.EnumMeta    ):





							def __getattribute__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getattribute__(_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj





							def __getitem__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getitem__(_SCREAMING_SNAKE_CASE    )
								if member._on_access:
									member._on_access()
								return member





							def __call__(							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       *, _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=1    ):
								a_     			=   super().__call__(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       module=_SCREAMING_SNAKE_CASE			,       qualname=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       start=_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj







class 			lowerCamelCase_					(    enum.Enum   ,		metaclass=_SCREAMING_SNAKE_CASE    ):





							def __new__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   object.__new__(cls    )
								a_     			=   value
								a_     			=   member.deprecate
								return member





							@property
							def   __magic_name__      (							self    ):
								return ""





							def   __magic_name__      (							self    ):
								a_     			=   f""" {self.help_message}""" if self.help_message else """"""
								warnings.warn(
								    f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
								    + help_message			,       _SCREAMING_SNAKE_CASE			,       stacklevel=3			,       )