




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					CONFIG_NAME

					@register_to_config
					def __init__(							self					,			__lowercase = True					,			__lowercase = 8					,			__lowercase = "lanczos"					,			__lowercase = True					,			__lowercase = False					,			):







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						str				  =				images[None, ...]
						lowerCamelCase_				:						Dict				  =				(images * 255).round().astype('uint8'   )
						if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							lowerCamelCase_				:						Optional[int]				  =				[Image.fromarray(image.squeeze()					,			mode='L'   ) for image in images]
						else:
							lowerCamelCase_				:						int				  =				[Image.fromarray(__lowercase   ) for image in images]

						return pil_images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if not isinstance(__lowercase					,			__lowercase   ):
							lowerCamelCase_				:						Tuple				  =				[images]
						lowerCamelCase_				:						Union[str, Any]				  =				[np.array(__lowercase   ).astype(np.floataa   ) / 2_55.0 for image in images]
						lowerCamelCase_				:						List[Any]				  =				np.stack(__lowercase					,			axis=0   )

						return images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						Tuple				  =				images[..., None]

						lowerCamelCase_				:						List[str]				  =				torch.from_numpy(images.transpose(0					,			3					,			1					,			2   )   )
						return images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				images.cpu().permute(0					,			2					,			3					,			1   ).float().numpy()
						return images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						return 2.0 * images - 1.0

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						return (images / 2 + 0.5).clamp(0					,			1   )

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				image.convert('RGB'   )
						return image

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




						if height is None:
							lowerCamelCase_				:						str				  =				image.height
						if width is None:
							lowerCamelCase_				:						Optional[int]				  =				image.width

						lowerCamelCase_  ,     lowerCamelCase_				:						Tuple				  =				(
						    x - x % self.config.vae_scale_factor for x in (width, height)
						)  # resize to integer multiple of vae_scale_factor
						lowerCamelCase_				:						Dict				  =				image.resize((width, height)					,			resample=PIL_INTERPOLATION[self.config.resample]   )
						return image

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				(PIL.Image.Image, np.ndarray, torch.Tensor)
						if isinstance(__lowercase					,			__lowercase   ):
							lowerCamelCase_				:						Optional[int]				  =				[image]
						elif not (isinstance(__lowercase					,			__lowercase   ) and all(isinstance(__lowercase					,			__lowercase   ) for i in image   )):
							raise ValueError(
							    f"Input is in incorrect format: {[type(__lowercase   ) for i in image]}. Currently, we only support {', '.join(__lowercase   )}"   )

						if isinstance(image[0]					,			PIL.Image.Image   ):
							if self.config.do_convert_rgb:
								lowerCamelCase_				:						int				  =				[self.convert_to_rgb(__lowercase   ) for i in image]
							if self.config.do_resize:
								lowerCamelCase_				:						str				  =				[self.resize(__lowercase					,			__lowercase					,			__lowercase   ) for i in image]
							lowerCamelCase_				:						Optional[int]				  =				self.pil_to_numpy(__lowercase   )  # to np
							lowerCamelCase_				:						Union[str, Any]				  =				self.numpy_to_pt(__lowercase   )  # to pt

						elif isinstance(image[0]					,			np.ndarray   ):
							lowerCamelCase_				:						Optional[int]				  =				np.concatenate(__lowercase					,			axis=0   ) if image[0].ndim == 4 else np.stack(__lowercase					,			axis=0   )
							lowerCamelCase_				:						Dict				  =				self.numpy_to_pt(__lowercase   )
							lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_				:						Optional[Any]				  =				image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
								raise ValueError(
								    f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
								    f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"   )

						elif isinstance(image[0]					,			torch.Tensor   ):
							lowerCamelCase_				:						List[str]				  =				torch.cat(__lowercase					,			axis=0   ) if image[0].ndim == 4 else torch.stack(__lowercase					,			axis=0   )
							lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_				:						List[str]				  =				image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
								return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
								raise ValueError(
								    f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
								    f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"   )

        # expected range [0,1], normalize to [-1,1]
						lowerCamelCase_				:						str				  =				self.config.do_normalize
						if image.min() < 0:
							warnings.warn(
							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
							    f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"					,			__lowercase					,			)
							lowerCamelCase_				:						Dict				  =				False

						if do_normalize:
							lowerCamelCase_				:						int				  =				self.normalize(__lowercase   )

						return image





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"					,			__lowercase = None					,			):







						'''simple docstring'''




						if not isinstance(__lowercase					,			torch.Tensor   ):
							raise ValueError(
							    f"Input for postprocessing is in incorrect format: {type(__lowercase   )}. We only support pytorch tensor"   )
						if output_type not in ["latent", "pt", "np", "pil"]:
							lowerCamelCase_				:						str				  =				(
							    f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'					,			'1.0.0'					,			__lowercase					,			standard_warn=__lowercase   )
							lowerCamelCase_				:						Optional[int]				  =				'np'

						if output_type == "latent":
							return image

						if do_denormalize is None:
							lowerCamelCase_				:						Optional[int]				  =				[self.config.do_normalize] * image.shape[0]

						lowerCamelCase_				:						Tuple				  =				torch.stack(
						    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

						if output_type == "pt":
							return image

						lowerCamelCase_				:						Tuple				  =				self.pt_to_numpy(__lowercase   )

						if output_type == "np":
							return image

						if output_type == "pil":
							return self.numpy_to_pil(__lowercase   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					CONFIG_NAME

					@register_to_config
					def __init__(							self					,			__lowercase = True					,			__lowercase = 8					,			__lowercase = "lanczos"					,			__lowercase = True					,			):







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						Any				  =				images[None, ...]
						lowerCamelCase_				:						Optional[int]				  =				(images * 255).round().astype('uint8'   )
						if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							lowerCamelCase_				:						Any				  =				[Image.fromarray(image.squeeze()					,			mode='L'   ) for image in images]
						else:
							lowerCamelCase_				:						Any				  =				[Image.fromarray(image[:, :, :3]   ) for image in images]

						return pil_images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						return image[:, :, 1] * 2**8 + image[:, :, 2]

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						Union[str, Any]				  =				images[None, ...]
						lowerCamelCase_				:						Union[str, Any]				  =				images[:, :, :, 3:]
						if images.shape[-1] == 6:
							lowerCamelCase_				:						int				  =				(images_depth * 255).round().astype('uint8'   )
							lowerCamelCase_				:						int				  =				[
							    Image.fromarray(self.rgblike_to_depthmap(__lowercase   )					,			mode='I;16'   ) for image_depth in images_depth
							]
						elif images.shape[-1] == 4:
							lowerCamelCase_				:						List[Any]				  =				(images_depth * 6_55_35.0).astype(np.uintaa   )
							lowerCamelCase_				:						List[Any]				  =				[Image.fromarray(__lowercase					,			mode='I;16'   ) for image_depth in images_depth]
						else:
							raise Exception('Not supported'   )

						return pil_images





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"					,			__lowercase = None					,			):







						'''simple docstring'''




						if not isinstance(__lowercase					,			torch.Tensor   ):
							raise ValueError(
							    f"Input for postprocessing is in incorrect format: {type(__lowercase   )}. We only support pytorch tensor"   )
						if output_type not in ["latent", "pt", "np", "pil"]:
							lowerCamelCase_				:						List[str]				  =				(
							    f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'					,			'1.0.0'					,			__lowercase					,			standard_warn=__lowercase   )
							lowerCamelCase_				:						List[Any]				  =				'np'

						if do_denormalize is None:
							lowerCamelCase_				:						int				  =				[self.config.do_normalize] * image.shape[0]

						lowerCamelCase_				:						Union[str, Any]				  =				torch.stack(
						    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

						lowerCamelCase_				:						Union[str, Any]				  =				self.pt_to_numpy(__lowercase   )

						if output_type == "np":
							if image.shape[-1] == 6:
								lowerCamelCase_				:						List[str]				  =				np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]					,			axis=0   )
							else:
								lowerCamelCase_				:						int				  =				image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

						if output_type == "pil":
							return self.numpy_to_pil(__lowercase   ), self.numpy_to_depth(__lowercase   )
						else:
							raise Exception(f"This type {output_type} is not supported"   )







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			:Optional[Any]	 =     logging.get_logger(__name__)


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							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 							lowercase							(     self			,    **__snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

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







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= load_image(__snake_case					)
							_snake_case							:       Tuple												= [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	_snake_case							:       List[Any]												= {}
												else:
																	_snake_case							:       int												= {"task_inputs": [subtask]}
												_snake_case							:       Tuple												= self.image_processor(images=[image]			,    return_tensors="pt"			,    **__snake_case					)
												_snake_case							:       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:
												_snake_case							:       str												= self.image_processor(images=[image]			,    return_tensors="pt"					)
							_snake_case							:       Optional[int]												= target_size
							return inputs







		def 							lowercase							(     self			,    __snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

							if fn is not None:
												_snake_case							:       Optional[Any]												= fn(
												    __snake_case			,    threshold=__snake_case			,    mask_threshold=__snake_case			,    overlap_mask_area_threshold=__snake_case			,    target_sizes=model_outputs["target_size"]			,    )[0]

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

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

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= 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