
"""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


snake_case    =		logging.get_logger(__name__)




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      int						  =				["pixel_values"]


				def __init__(				self		,	lowercase__ = True		,	lowercase__ = None		,	lowercase__ = True		,	lowercase__ = None		,	lowercase__ = PILImageResampling.BICUBIC		,	lowercase__ = True		,	lowercase__ = 1 / 255		,	lowercase__ = True		,	lowercase__ = None		,	lowercase__ = None		,	**lowercase__		,	)    ->    None:
									"""simple docstring"""







									super().__init__(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	crop_size if crop_size is not None else {'height': 256, 'width': 256}
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__		,	param_name='crop_size'			)
									SCREAMING_SNAKE_CASE				=	size if size is not None else {'height': 224, 'width': 224}
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__			)

									SCREAMING_SNAKE_CASE				=	do_center_crop
									SCREAMING_SNAKE_CASE				=	crop_size
									SCREAMING_SNAKE_CASE				=	do_resize
									SCREAMING_SNAKE_CASE				=	size
									SCREAMING_SNAKE_CASE				=	resample
									SCREAMING_SNAKE_CASE				=	do_rescale
									SCREAMING_SNAKE_CASE				=	rescale_factor
									SCREAMING_SNAKE_CASE				=	do_normalize
									SCREAMING_SNAKE_CASE				=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									SCREAMING_SNAKE_CASE				=	image_std if image_std is not None else IMAGENET_DEFAULT_STD


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	**lowercase__		,	)    ->    np.ndarray:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.size if size is None else size
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__			)
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__		,	param_name='crop_size'			)

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	get_image_size(lowercase__			)
									SCREAMING_SNAKE_CASE				=	min(lowercase__		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	(size['height'] / crop_size['height']) * min_dim
									SCREAMING_SNAKE_CASE				=	(size['width'] / crop_size['width']) * min_dim
									return center_crop(lowercase__		,	size=(cropped_height, cropped_width)		,	data_format=lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__ = PIL.Image.BICUBIC		,	lowercase__ = None		,	**lowercase__		,	)    ->    np.ndarray:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__			)
									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(
									    lowercase__		,	size=(size['height'], size['width'])		,	resample=lowercase__		,	data_format=lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	**lowercase__		,	)    ->    List[str]:
									"""simple docstring"""







									return rescale(lowercase__		,	scale=lowercase__		,	data_format=lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	**lowercase__		,	)    ->    np.ndarray:
									"""simple docstring"""







									return normalize(lowercase__		,	mean=lowercase__		,	std=lowercase__		,	data_format=lowercase__		,	**lowercase__			)




				def 							A					(				self		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = ChannelDimension.FIRST		,	**lowercase__		,	)    ->    PIL.Image.Image:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE				=	crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__		,	param_name='crop_size'			)
									SCREAMING_SNAKE_CASE				=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE				=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE				=	get_size_dict(lowercase__			)
									SCREAMING_SNAKE_CASE				=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE				=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE				=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE				=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE				=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE				=	image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE				=	make_list_of_images(lowercase__			)

									if not valid_images(lowercase__			):
														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.
									SCREAMING_SNAKE_CASE				=	[to_numpy_array(lowercase__			) for image in images]

									if do_center_crop:
														SCREAMING_SNAKE_CASE				=	[self.center_crop(lowercase__		,	lowercase__		,	size=lowercase__			) for image in images]

									if do_resize:
														SCREAMING_SNAKE_CASE				=	[self.resize(image=lowercase__		,	size=lowercase__		,	resample=lowercase__			) for image in images]

									if do_rescale:
														SCREAMING_SNAKE_CASE				=	[self.rescale(image=lowercase__		,	scale=lowercase__			) for image in images]

									if do_normalize:
														SCREAMING_SNAKE_CASE				=	[self.normalize(image=lowercase__		,	mean=lowercase__		,	std=lowercase__			) for image in images]

									SCREAMING_SNAKE_CASE				=	[to_channel_dimension_format(lowercase__		,	lowercase__			) for image in images]

									SCREAMING_SNAKE_CASE				=	{'pixel_values': images}
									return BatchFeature(data=lowercase__		,	tensor_type=lowercase__			)


"""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,
	)


snake_case    =		logging.get_logger(__name__)


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




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""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 							A					(				self		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







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

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(				self		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""simple docstring"""







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

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

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs['segmentation']

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

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

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

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