"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
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, build_pipeline_init_args
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_NAMES,
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES)
logger = logging.get_logger(__name__)
Prediction = Dict[str, Any]
Predictions = List[Prediction]
@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
class ImageSegmentationPipeline(Pipeline):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.framework == "tf": raise ValueError(f"The {self.__class__} is only available in PyTorch.")
        requires_backends(self, "vision")
        mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES.copy()
        mapping.update(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES)
        mapping.update(MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES)
        mapping.update(MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES)
        self.check_model_type(mapping)
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        postprocess_kwargs = {}
        if "subtask" in kwargs:
            postprocess_kwargs["subtask"] = kwargs["subtask"]
            preprocess_kwargs["subtask"] = kwargs["subtask"]
        if "threshold" in kwargs: postprocess_kwargs["threshold"] = kwargs["threshold"]
        if "mask_threshold" in kwargs: postprocess_kwargs["mask_threshold"] = kwargs["mask_threshold"]
        if "overlap_mask_area_threshold" in kwargs: postprocess_kwargs["overlap_mask_area_threshold"] = kwargs["overlap_mask_area_threshold"]
        if "timeout" in kwargs: preprocess_kwargs["timeout"] = kwargs["timeout"]
        return preprocess_kwargs, {}, postprocess_kwargs
    def __call__(self, images, **kwargs) -> Union[Predictions, List[Prediction]]: return super().__call__(images, **kwargs)
    def preprocess(self, image, subtask=None, timeout=None):
        image = load_image(image, timeout=timeout)
        target_size = [(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
            if subtask is None: kwargs = {}
            else: kwargs = {"task_inputs": [subtask]}
            inputs = self.image_processor(images=[image], return_tensors="pt", **kwargs)
            if self.framework == "pt": inputs = inputs.to(self.torch_dtype)
            inputs["task_inputs"] = self.tokenizer(inputs["task_inputs"], padding="max_length", max_length=self.model.config.task_seq_len, return_tensors=self.framework)["input_ids"]
        else:
            inputs = self.image_processor(images=[image], return_tensors="pt")
            if self.framework == "pt": inputs = inputs.to(self.torch_dtype)
        inputs["target_size"] = target_size
        return inputs
    def _forward(self, model_inputs):
        target_size = model_inputs.pop("target_size")
        model_outputs = self.model(**model_inputs)
        model_outputs["target_size"] = target_size
        return model_outputs
    def postprocess(self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5):
        fn = None
        if subtask in {"panoptic", None} and hasattr(self.image_processor, "post_process_panoptic_segmentation"): fn = self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor, "post_process_instance_segmentation"): fn = self.image_processor.post_process_instance_segmentation
        if fn is not None:
            outputs = fn(model_outputs, threshold=threshold, mask_threshold=mask_threshold, overlap_mask_area_threshold=overlap_mask_area_threshold, target_sizes=model_outputs["target_size"])[0]
            annotation = []
            segmentation = outputs["segmentation"]
            for segment in outputs["segments_info"]:
                mask = (segmentation == segment["id"]) * 255
                mask = Image.fromarray(mask.numpy().astype(np.uint8), mode="L")
                label = self.model.config.id2label[segment["label_id"]]
                score = segment["score"]
                annotation.append({"score": score, "label": label, "mask": mask})
        elif subtask in {"semantic", None} and hasattr(self.image_processor, "post_process_semantic_segmentation"):
            outputs = self.image_processor.post_process_semantic_segmentation(model_outputs, target_sizes=model_outputs["target_size"])[0]
            annotation = []
            segmentation = outputs.numpy()
            labels = np.unique(segmentation)
            for label in labels:
                mask = (segmentation == label) * 255
                mask = Image.fromarray(mask.astype(np.uint8), mode="L")
                label = self.model.config.id2label[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
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
