





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class 							_a(     unittest.TestCase     ):







		@property
		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= 1
							_snake_case							:       Union[str, Any]												= 3
							_snake_case							:       Dict												= 8
							_snake_case							:       Union[str, Any]												= 8

							_snake_case							:       Dict												= torch.rand((batch_size, num_channels, height, width)					)

							return sample







		def 							lowercase							(     self			,    __snake_case					)   ->   List[str]:


							'''simple docstring'''

							if isinstance(image[0]			,    PIL.Image.Image					):
												return np.stack([np.array(__snake_case					) for i in image]			,    axis=0					)
							elif isinstance(__snake_case			,    torch.Tensor					):
												return image.cpu().numpy().transpose(0			,    2			,    3			,    1					)
							return image







		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       int												= VaeImageProcessor(do_resize=__snake_case			,    do_normalize=__snake_case					)

							_snake_case							:       int												= self.dummy_sample
							_snake_case							:       List[Any]												= self.to_np(__snake_case					)

							for output_type in ["pt", "np", "pil"]:
												_snake_case							:       Any												= image_processor.postprocess(
												    image_processor.preprocess(__snake_case					)			,    output_type=__snake_case			,    )
												_snake_case							:       Optional[int]												= self.to_np(__snake_case					)
												_snake_case							:       Optional[int]												= (input_np * 2_5_5).round() if output_type == "pil" else input_np
												assert (
												    np.abs(in_np - out_np					).max() < 1E-6
												), f"""decoded output does not match input for output_type {output_type}"""







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= VaeImageProcessor(do_resize=__snake_case			,    do_normalize=__snake_case					)
							_snake_case							:       str												= self.dummy_sample.cpu().numpy().transpose(0			,    2			,    3			,    1					)

							for output_type in ["pt", "np", "pil"]:
												_snake_case							:       Dict												= image_processor.postprocess(image_processor.preprocess(__snake_case					)			,    output_type=__snake_case					)

												_snake_case							:       Optional[Any]												= self.to_np(__snake_case					)
												_snake_case							:       List[str]												= (input_np * 2_5_5).round() if output_type == "pil" else input_np
												assert (
												    np.abs(in_np - out_np					).max() < 1E-6
												), f"""decoded output does not match input for output_type {output_type}"""







		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       Tuple												= VaeImageProcessor(do_resize=__snake_case			,    do_normalize=__snake_case					)

							_snake_case							:       List[Any]												= self.dummy_sample.cpu().numpy().transpose(0			,    2			,    3			,    1					)
							_snake_case							:       Any												= image_processor.numpy_to_pil(__snake_case					)

							for output_type in ["pt", "np", "pil"]:
												_snake_case							:       List[Any]												= image_processor.postprocess(image_processor.preprocess(__snake_case					)			,    output_type=__snake_case					)
												for i, o in zip(__snake_case			,    __snake_case					):
																	_snake_case							:       Dict												= np.array(__snake_case					)
																	_snake_case							:       str												= self.to_np(__snake_case					) if output_type == "pil" else (self.to_np(__snake_case					) * 2_5_5).round()
																	assert (
																	    np.abs(in_np - out_np					).max() < 1E-6
																	), f"""decoded output does not match input for output_type {output_type}"""







		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							_snake_case							:       int												= VaeImageProcessor(do_resize=__snake_case			,    do_normalize=__snake_case					)

							_snake_case							:       Optional[Any]												= self.dummy_sample
							_snake_case							:       Optional[int]												= input_pt_ad.squeeze(0					)

							_snake_case							:       Tuple												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )
							_snake_case							:       str												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							_snake_case							:       List[Any]												= self.to_np(self.dummy_sample					)
							_snake_case							:       List[str]												= input_np_ad.squeeze(0					)

							_snake_case							:       str												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )
							_snake_case							:       Union[str, Any]												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							assert np.abs(out_pt_ad - out_pt_ad					).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad					).max() < 1E-6







		def 							lowercase							(     self					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= VaeImageProcessor(do_resize=__snake_case			,    do_normalize=__snake_case					)

							_snake_case							:       Optional[Any]												= self.dummy_sample
							_snake_case							:       Optional[Any]												= list(__snake_case					)

							_snake_case							:       Dict												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							_snake_case							:       Dict												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							_snake_case							:       Optional[Any]												= self.to_np(self.dummy_sample					)
							list(__snake_case					)

							_snake_case							:       Optional[int]												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							_snake_case							:       Any												= image_processor.postprocess(
							    image_processor.preprocess(__snake_case					)			,    output_type="np"			,    )

							assert np.abs(out_pt_ad - out_pt_list					).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list					).max() < 1E-6






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