




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,
				)


lowercase_      :							Dict    							=					logging.get_logger(__name__)


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






@add_end_docstrings(UpperCAmelCase__					)
class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     Dict       ,       *snake_case__       :     int       ,       **snake_case__       :     Union[str, 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 UpperCamelCase     (			self       :     Any       ,       **snake_case__       :     int ):




									"""simple docstring"""


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

									return preprocess_kwargs, {}, postprocess_kwargs







			def __call__(			self       :     Tuple       ,       snake_case__       :     str       ,       **snake_case__       :     Tuple ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     str=None ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Dict       ,       snake_case__       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	model_inputs.pop("target_size" )
									_UpperCAmelCase						=	self.model(**snake_case__ )
									_UpperCAmelCase						=	target_size
									return model_outputs







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Any       ,       snake_case__       :     Union[str, Any]=None       ,       snake_case__       :     str=0.9       ,       snake_case__       :     Union[str, Any]=0.5       ,       snake_case__       :     List[str]=0.5 ):




									"""simple docstring"""


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

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

															_UpperCAmelCase						=	[]
															_UpperCAmelCase						=	outputs["segmentation"]

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

															_UpperCAmelCase						=	[]
															_UpperCAmelCase						=	outputs.numpy()
															_UpperCAmelCase						=	np.unique(snake_case__ )

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










import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_UpperCAmelCase				,					_UpperCAmelCase						=	sample.shape
															_UpperCAmelCase						=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels) )
															_UpperCAmelCase						=	F.log_softmax(logits.double()       ,       dim=1 ).float()
															return return_value

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




