


'''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,
		)


_SCREAMING_SNAKE_CASE       :   Tuple        = logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE       :   Any        = Dict[str, Any]
_SCREAMING_SNAKE_CASE       :   str        = List[Prediction]






@add_end_docstrings(snake_case_		)
class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						def __init__(  self:	Optional[Any]      ,							*__UpperCamelCase:	Optional[int]      ,							**__UpperCamelCase:	Dict     )							->	str:
										super().__init__(*__UpperCamelCase      ,							**__UpperCamelCase     )

										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 lowerCAmelCase__ (  self:	Union[str, Any]      ,							**__UpperCamelCase:	Any     )							->	Optional[int]:
										__magic_name__				:					List[str]						       =       {}
										__magic_name__				:					Dict						       =       {}
										if "subtask" in kwargs:
														__magic_name__				:					int						       =       kwargs["subtask"]
														__magic_name__				:					Optional[int]						       =       kwargs["subtask"]
										if "threshold" in kwargs:
														__magic_name__				:					Tuple						       =       kwargs["threshold"]
										if "mask_threshold" in kwargs:
														__magic_name__				:					List[Any]						       =       kwargs["mask_threshold"]
										if "overlap_mask_area_threshold" in kwargs:
														__magic_name__				:					Tuple						       =       kwargs["overlap_mask_area_threshold"]

										return preprocess_kwargs, {}, postprocess_kwargs







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







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







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	List[Any]     )							->	List[str]:
										__magic_name__				:					Optional[int]						       =       model_inputs.pop("target_size"     )
										__magic_name__				:					Optional[Any]						       =       self.model(**__UpperCamelCase     )
										__magic_name__				:					Union[str, Any]						       =       target_size
										return model_outputs







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

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

														__magic_name__				:					Optional[int]						       =       []
														__magic_name__				:					Union[str, Any]						       =       outputs["segmentation"]

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

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

														__magic_name__				:					List[str]						       =       []
														__magic_name__				:					Dict						       =       outputs.numpy()
														__magic_name__				:					int						       =       np.unique(__UpperCamelCase     )

														for label in labels:
																		__magic_name__				:					Dict						       =       (segmentation == label) * 255
																		__magic_name__				:					Tuple						       =       Image.fromarray(mask.astype(np.uinta     )      ,							mode="L"     )
																		__magic_name__				:					Optional[int]						       =       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



'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_SCREAMING_SNAKE_CASE       :   int        = logging.get_logger(__name__)






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["pixel_values"]







						def __init__(  self:	Union[str, Any]      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	PILImageResampling = PILImageResampling.BICUBIC      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Union[int, float] = 1 / 255      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							**__UpperCamelCase:	Union[str, Any]      ,							)							->	None:
										super().__init__(**__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else {"height": 256, "width": 256}
										__magic_name__				:					Optional[int]						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					Union[str, Any]						       =       size if size is not None else {"height": 224, "width": 224}
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase     )

										__magic_name__				:					Tuple						       =       do_center_crop
										__magic_name__				:					int						       =       crop_size
										__magic_name__				:					Dict						       =       do_resize
										__magic_name__				:					Optional[Any]						       =       size
										__magic_name__				:					Optional[Any]						       =       resample
										__magic_name__				:					str						       =       do_rescale
										__magic_name__				:					int						       =       rescale_factor
										__magic_name__				:					Dict						       =       do_normalize
										__magic_name__				:					Any						       =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__magic_name__				:					str						       =       image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	Optional[int] = None      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[str]      ,							)							->	np.ndarray:
										__magic_name__				:					List[str]						       =       self.size if size is None else size
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )

										__magic_name__       ,     __magic_name__				:					Dict						       =       get_image_size(__UpperCamelCase     )
										__magic_name__				:					int						       =       min(__UpperCamelCase      ,							__UpperCamelCase     )
										__magic_name__				:					Dict						       =       (size["height"] / crop_size["height"]) * min_dim
										__magic_name__				:					str						       =       (size["width"] / crop_size["width"]) * min_dim
										return center_crop(__UpperCamelCase      ,							size=(cropped_height, cropped_width)      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	PILImageResampling = PIL.Image.BICUBIC      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	Any      ,							)							->	np.ndarray:
										__magic_name__				:					Optional[Any]						       =       get_size_dict(__UpperCamelCase     )
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
										return resize(
										    __UpperCamelCase      ,							size=(size["height"], size["width"])      ,							resample=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[int]      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[int, float]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[Any]      ,							)							->	List[Any]:
										return rescale(__UpperCamelCase      ,							scale=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	str      ,							)							->	np.ndarray:
										return normalize(__UpperCamelCase      ,							mean=__UpperCamelCase      ,							std=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	ImageInput      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	PILImageResampling = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[float] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[str, TensorType]] = None      ,							__UpperCamelCase:	ChannelDimension = ChannelDimension.FIRST      ,							**__UpperCamelCase:	Dict      ,							)							->	PIL.Image.Image:
										__magic_name__				:					Optional[Any]						       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else self.crop_size
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					int						       =       do_resize if do_resize is not None else self.do_resize
										__magic_name__				:					Optional[Any]						       =       size if size is not None else self.size
										__magic_name__				:					Any						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Optional[int]						       =       resample if resample is not None else self.resample
										__magic_name__				:					int						       =       do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:					int						       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:					Any						       =       do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:					Dict						       =       image_mean if image_mean is not None else self.image_mean
										__magic_name__				:					Tuple						       =       image_std if image_std is not None else self.image_std

										__magic_name__				:					List[Any]						       =       make_list_of_images(__UpperCamelCase     )

										if not valid_images(__UpperCamelCase     ):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."     )

										if do_center_crop and crop_size is None:
														raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize is True."     )

										if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."     )

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."     )

										# All transformations expect numpy arrays.
										__magic_name__				:					Optional[Any]						       =       [to_numpy_array(__UpperCamelCase     ) for image in images]

										if do_center_crop:
														__magic_name__				:					Union[str, Any]						       =       [self.center_crop(__UpperCamelCase      ,							__UpperCamelCase      ,							size=__UpperCamelCase     ) for image in images]

										if do_resize:
														__magic_name__				:					List[Any]						       =       [self.resize(image=__UpperCamelCase      ,							size=__UpperCamelCase      ,							resample=__UpperCamelCase     ) for image in images]

										if do_rescale:
														__magic_name__				:					str						       =       [self.rescale(image=__UpperCamelCase      ,							scale=__UpperCamelCase     ) for image in images]

										if do_normalize:
														__magic_name__				:					Optional[int]						       =       [self.normalize(image=__UpperCamelCase      ,							mean=__UpperCamelCase      ,							std=__UpperCamelCase     ) for image in images]

										__magic_name__				:					List[Any]						       =       [to_channel_dimension_format(__UpperCamelCase      ,							__UpperCamelCase     ) for image in images]

										__magic_name__				:					Optional[int]						       =       {"pixel_values": images}
										return BatchFeature(data=__UpperCamelCase      ,							tensor_type=__UpperCamelCase     )