"""
    #################################################################################################################################################################################################
    # 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 copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available(): import torch
if is_tf_available(): import tensorflow as tf
class SamProcessor(ProcessorMixin):
    attributes = ["image_processor"]
    image_processor_class = "SamImageProcessor"
    def __init__(self, image_processor):
        super().__init__(image_processor)
        self.current_processor = self.image_processor
        self.point_pad_value = -10
        self.target_size = self.image_processor.size["longest_edge"]
    def __call__(self, images=None, segmentation_maps=None, input_points=None, input_labels=None, input_boxes=None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs) -> BatchEncoding:
        encoding_image_processor = self.image_processor(images, segmentation_maps=segmentation_maps, return_tensors=return_tensors, **kwargs)
        original_sizes = encoding_image_processor["original_sizes"]
        if hasattr(original_sizes, "numpy"): original_sizes = original_sizes.numpy()
        input_points, input_labels, input_boxes = self._check_and_preprocess_points(input_points=input_points, input_labels=input_labels, input_boxes=input_boxes)
        encoding_image_processor = self._normalize_and_convert(encoding_image_processor, original_sizes, input_points=input_points, input_labels=input_labels,
        input_boxes=input_boxes, return_tensors=return_tensors)
        return encoding_image_processor
    def _normalize_and_convert(self, encoding_image_processor, original_sizes, input_points=None, input_labels=None, input_boxes=None, return_tensors="pt"):
        if input_points is not None:
            if len(original_sizes) != len(input_points): input_points = [self._normalize_coordinates(self.target_size, point, original_sizes[0]) for point in input_points]
            else: input_points = [self._normalize_coordinates(self.target_size, point, original_size) for point, original_size in zip(input_points, original_sizes)]
            if not all(point.shape == input_points[0].shape for point in input_points):
                if input_labels is not None: input_points, input_labels = self._pad_points_and_labels(input_points, input_labels)
            input_points = np.array(input_points)
        if input_labels is not None: input_labels = np.array(input_labels)
        if input_boxes is not None:
            if len(original_sizes) != len(input_boxes): input_boxes = [self._normalize_coordinates(self.target_size, box, original_sizes[0], is_bounding_box=True) for box in input_boxes]
            else: input_boxes = [self._normalize_coordinates(self.target_size, box, original_size, is_bounding_box=True) for box, original_size in zip(input_boxes, original_sizes)]
            input_boxes = np.array(input_boxes)
        if input_boxes is not None:
            if return_tensors == "pt":
                input_boxes = torch.from_numpy(input_boxes)
                input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
            elif return_tensors == "tf":
                input_boxes = tf.convert_to_tensor(input_boxes)
                input_boxes = tf.expand_dims(input_boxes, 1) if len(input_boxes.shape) != 3 else input_boxes
            encoding_image_processor.update({"input_boxes": input_boxes})
        if input_points is not None:
            if return_tensors == "pt":
                input_points = torch.from_numpy(input_points)
                input_points = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
            elif return_tensors == "tf":
                input_points = tf.convert_to_tensor(input_points)
                input_points = tf.expand_dims(input_points, 1) if len(input_points.shape) != 4 else input_points
            encoding_image_processor.update({"input_points": input_points})
        if input_labels is not None:
            if return_tensors == "pt":
                input_labels = torch.from_numpy(input_labels)
                input_labels = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
            elif return_tensors == "tf":
                input_labels = tf.convert_to_tensor(input_labels)
                input_labels = tf.expand_dims(input_labels, 1) if len(input_labels.shape) != 3 else input_labels
            encoding_image_processor.update({"input_labels": input_labels})
        return encoding_image_processor
    def _pad_points_and_labels(self, input_points, input_labels):
        expected_nb_points = max([point.shape[0] for point in input_points])
        processed_input_points = []
        for i, point in enumerate(input_points):
            if point.shape[0] != expected_nb_points:
                point = np.concatenate([point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value], axis=0)
                input_labels[i] = np.append(input_labels[i], [self.point_pad_value])
            processed_input_points.append(point)
        input_points = processed_input_points
        return input_points, input_labels
    def _normalize_coordinates(self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False) -> np.ndarray:
        old_h, old_w = original_size
        new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size)
        coords = deepcopy(coords).astype(float)
        if is_bounding_box: coords = coords.reshape(-1, 2, 2)
        coords[..., 0] = coords[..., 0] * (new_w / old_w)
        coords[..., 1] = coords[..., 1] * (new_h / old_h)
        if is_bounding_box: coords = coords.reshape(-1, 4)
        return coords
    def _check_and_preprocess_points(self, input_points=None, input_labels=None, input_boxes=None):
        if input_points is not None:
            if hasattr(input_points, "numpy"): input_points = input_points.numpy().tolist()
            if not isinstance(input_points, list) or not isinstance(input_points[0], list): raise ValueError("Input points must be a list of list of floating points.")
            input_points = [np.array(input_point) for input_point in input_points]
        else: input_points = None
        if input_labels is not None:
            if hasattr(input_labels, "numpy"): input_labels = input_labels.numpy().tolist()
            if not isinstance(input_labels, list) or not isinstance(input_labels[0], list): raise ValueError("Input labels must be a list of list integers.")
            input_labels = [np.array(label) for label in input_labels]
        else: input_labels = None
        if input_boxes is not None:
            if hasattr(input_boxes, "numpy"): input_boxes = input_boxes.numpy().tolist()
            if (not isinstance(input_boxes, list) or not isinstance(input_boxes[0], list) or not isinstance(input_boxes[0][0], list)): raise ValueError("Input boxes must be a list of list of list of floating points.")
            input_boxes = [np.array(box).astype(np.float32) for box in input_boxes]
        else: input_boxes = None
        return input_points, input_labels, input_boxes
    @property
    def model_input_names(self):
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(image_processor_input_names))
    def post_process_masks(self, *args, **kwargs): return self.image_processor.post_process_masks(*args, **kwargs)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
