

'''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,
						)


lowerCAmelCase__      				=			logging.get_logger(__name__)


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



@add_end_docstrings(lowerCamelCase__			)
class        lowercase    (	lowerCamelCase__			):


							def __init__(      self     ,      *_snake_case     ,      **_snake_case)				->					int:
								super().__init__(*_snake_case     ,      **_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     _snake_case				(      self     ,      **_snake_case)				->					List[Any]:
								UpperCAmelCase_		:   Dict													=       {}
								UpperCAmelCase_		:   List[Any]													=       {}
								if "subtask" in kwargs:
									UpperCAmelCase_		:   Optional[int]													=       kwargs['''subtask''']
									UpperCAmelCase_		:   Optional[Any]													=       kwargs['''subtask''']
								if "threshold" in kwargs:
									UpperCAmelCase_		:   Optional[Any]													=       kwargs['''threshold''']
								if "mask_threshold" in kwargs:
									UpperCAmelCase_		:   Dict													=       kwargs['''mask_threshold''']
								if "overlap_mask_area_threshold" in kwargs:
									UpperCAmelCase_		:   List[str]													=       kwargs['''overlap_mask_area_threshold''']

								return preprocess_kwargs, {}, postprocess_kwargs


							def __call__(      self     ,      _snake_case     ,      **_snake_case)				->					Union[Predictions, List[Prediction]]:
								return super().__call__(_snake_case     ,      **_snake_case)


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


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   str													=       model_inputs.pop('target_size')
								UpperCAmelCase_		:   Any													=       self.model(**_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       target_size
								return model_outputs





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case=None     ,      _snake_case=0.9     ,      _snake_case=0.5     ,      _snake_case=0.5)				->					Tuple:
								UpperCAmelCase_		:   Dict													=       None
								if subtask in {"panoptic", None} and hasattr(self.image_processor     ,      'post_process_panoptic_segmentation'):
									UpperCAmelCase_		:   Optional[Any]													=       self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor     ,      'post_process_instance_segmentation'):
									UpperCAmelCase_		:   Optional[Any]													=       self.image_processor.post_process_instance_segmentation

								if fn is not None:
									UpperCAmelCase_		:   List[Any]													=       fn(
									    _snake_case     ,      threshold=_snake_case     ,      mask_threshold=_snake_case     ,      overlap_mask_area_threshold=_snake_case     ,      target_sizes=model_outputs['target_size']     ,      )[0]

									UpperCAmelCase_		:   Dict													=       []
									UpperCAmelCase_		:   Optional[Any]													=       outputs['''segmentation''']

									for segment in outputs["segments_info"]:
										UpperCAmelCase_		:   Any													=       (segmentation == segment['''id''']) * 255
										UpperCAmelCase_		:   Optional[Any]													=       Image.fromarray(mask.numpy().astype(np.uinta)     ,      mode='L')
										UpperCAmelCase_		:   int													=       self.model.config.idalabel[segment['''label_id''']]
										UpperCAmelCase_		:   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'):
									UpperCAmelCase_		:   List[Any]													=       self.image_processor.post_process_semantic_segmentation(
									    _snake_case     ,      target_sizes=model_outputs['target_size'])[0]

									UpperCAmelCase_		:   Union[str, Any]													=       []
									UpperCAmelCase_		:   Tuple													=       outputs.numpy()
									UpperCAmelCase_		:   Optional[int]													=       np.unique(_snake_case)

									for label in labels:
										UpperCAmelCase_		:   int													=       (segmentation == label) * 255
										UpperCAmelCase_		:   Optional[int]													=       Image.fromarray(mask.astype(np.uinta)     ,      mode='L')
										UpperCAmelCase_		:   Optional[Any]													=       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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     make_list_of_images(snake_case      )

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

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

         if do_center_crop and crop_size is None:
                raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'''      )

         # All transformations expect numpy arrays.
         __magic_name__			:		Union[str, Any]        =     [to_numpy_array(snake_case      ) for image in images]

         if do_resize:
                __magic_name__			:		Optional[int]        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

         if do_rescale:
                __magic_name__			:		Any        =     [self.rescale(image=snake_case       ,       scale=snake_case       ,       offset=snake_case      ) for image in images]

         if do_normalize:
                __magic_name__			:		Union[str, Any]        =     [self.normalize(image=snake_case       ,       mean=snake_case       ,       std=snake_case      ) for image in images]

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

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

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
