"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
import collections
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_sam import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SamConfig"
_CHECKPOINT_FOR_DOC = "facebook/sam-vit-huge"
@dataclass
class SamVisionEncoderOutput(ModelOutput):
    """Args:"""
    image_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class SamImageSegmentationOutput(ModelOutput):
    """Args:"""
    iou_scores: torch.FloatTensor = None
    pred_masks: torch.FloatTensor = None
    vision_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    vision_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    mask_decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
class SamPatchEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
    def forward(self, pixel_values):
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels: raise ValueError("Make sure that the channel dimension of the pixel values match with the one set in the configuration.")
        if height != self.image_size[0] or width != self.image_size[1]: raise ValueError(f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}).")
        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
        return embeddings
class SamMLPBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
        self.act = ACT2FN[config.hidden_act]
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.lin1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.lin2(hidden_states)
        return hidden_states
class SamLayerNorm(nn.Module):
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]: raise NotImplementedError(f"Unsupported data format: {self.data_format}")
        self.normalized_shape = (normalized_shape,)
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.data_format == "channels_last": x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            input_dtype = x.dtype
            x = x.float()
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = x.to(dtype=input_dtype)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x
class SamAttention(nn.Module):
    def __init__(self, config, downsample_rate=None):
        super().__init__()
        self.hidden_size = config.hidden_size
        downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate
        self.internal_dim = config.hidden_size // downsample_rate
        self.num_attention_heads = config.num_attention_heads
        if self.internal_dim % config.num_attention_heads != 0: raise ValueError("num_attention_heads must divide hidden_size.")
        self.q_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.k_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.v_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.out_proj = nn.Linear(self.internal_dim, self.hidden_size)
    def _separate_heads(self, hidden_states: Tensor, num_attention_heads: int) -> Tensor:
        batch, point_batch_size, n_tokens, channel = hidden_states.shape
        c_per_head = channel // num_attention_heads
        hidden_states = hidden_states.reshape(batch * point_batch_size, n_tokens, num_attention_heads, c_per_head)
        return hidden_states.transpose(1, 2)
    def _recombine_heads(self, hidden_states: Tensor, point_batch_size: int) -> Tensor:
        batch, n_heads, n_tokens, c_per_head = hidden_states.shape
        hidden_states = hidden_states.transpose(1, 2)
        return hidden_states.reshape(batch // point_batch_size, point_batch_size, n_tokens, n_heads * c_per_head)
    def forward(self, query: Tensor, key: Tensor, value: Tensor, attention_similarity: Tensor = None) -> Tensor:
        query = self.q_proj(query)
        key = self.k_proj(key)
        value = self.v_proj(value)
        point_batch_size = query.shape[1]
        query = self._separate_heads(query, self.num_attention_heads)
        key = self._separate_heads(key, self.num_attention_heads)
        value = self._separate_heads(value, self.num_attention_heads)
        _, _, _, c_per_head = query.shape
        attn = query @ key.permute(0, 1, 3, 2)
        attn = attn / (c_per_head**0.5)
        attn = torch.softmax(attn, dim=-1)
        if attention_similarity is not None:
            attn = attn + attention_similarity
            attn = torch.softmax(attn, dim=-1)
        out = attn @ value
        out = self._recombine_heads(out, point_batch_size)
        out = self.out_proj(out)
        return out
class SamTwoWayAttentionBlock(nn.Module):
    def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_norm_eps = config.layer_norm_eps
        self.self_attn = SamAttention(config, downsample_rate=1)
        self.layer_norm1 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
        self.cross_attn_token_to_image = SamAttention(config, downsample_rate=attention_downsample_rate)
        self.layer_norm2 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
        self.mlp = SamMLPBlock(config)
        self.layer_norm3 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
        self.layer_norm4 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
        self.cross_attn_image_to_token = SamAttention(config, downsample_rate=attention_downsample_rate)
        self.skip_first_layer_pe = skip_first_layer_pe
    def forward(self, queries: Tensor, keys: Tensor, query_point_embedding: Tensor, key_point_embedding: Tensor, attention_similarity: Tensor, output_attentions: bool = False):
        if self.skip_first_layer_pe: queries = self.self_attn(query=queries, key=queries, value=queries)
        else:
            query = queries + query_point_embedding
            attn_out = self.self_attn(query=query, key=query, value=queries)
            queries = queries + attn_out
        queries = self.layer_norm1(queries)
        query = queries + query_point_embedding
        key = keys + key_point_embedding
        attn_out = self.cross_attn_token_to_image(query=query, key=key, value=keys, attention_similarity=attention_similarity)
        queries = queries + attn_out
        queries = self.layer_norm2(queries)
        mlp_out = self.mlp(queries)
        queries = queries + mlp_out
        queries = self.layer_norm3(queries)
        query = queries + query_point_embedding
        key = keys + key_point_embedding
        attn_out = self.cross_attn_image_to_token(query=key, key=query, value=queries)
        keys = keys + attn_out
        keys = self.layer_norm4(keys)
        outputs = (queries, keys)
        if output_attentions: outputs = outputs + (attn_out,)
        else: outputs = outputs + (None,)
        return outputs
class SamTwoWayTransformer(nn.Module):
    def __init__(self, config: SamMaskDecoderConfig):
        super().__init__()
        self.config = config
        self.num_hidden_layers = config.num_hidden_layers
        self.layers = nn.ModuleList()
        for i in range(self.num_hidden_layers): self.layers.append(SamTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0)))
        self.final_attn_token_to_image = SamAttention(config)
        self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size)
    def forward(self, point_embeddings: Tensor, image_embeddings: Tensor, image_positional_embeddings: Tensor, attention_similarity: Tensor, target_embedding=None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        all_attentions = ()
        if image_embeddings is None: raise ValueError("You have to specify an image_embedding")
        image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1)
        image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1)
        queries = point_embeddings
        keys = image_embeddings
        for layer in self.layers:
            if target_embedding is not None: queries += target_embedding
            queries, keys, attention_outputs = layer(queries=queries, keys=keys, query_point_embedding=point_embeddings, key_point_embedding=image_positional_embeddings,
            attention_similarity=attention_similarity, output_attentions=output_attentions)
            if output_attentions: all_attentions = all_attentions + (attention_outputs,)
        query = queries + point_embeddings
        key = keys + image_positional_embeddings
        attn_out = self.final_attn_token_to_image(query=query, key=key, value=keys)
        queries = queries + attn_out
        queries = self.layer_norm_final_attn(queries)
        return queries, keys, all_attentions
class SamFeedForward(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, sigmoid_output: bool = False):
        super().__init__()
        self.num_layers = num_layers
        self.activation = nn.ReLU()
        self.proj_in = nn.Linear(input_dim, hidden_dim)
        self.proj_out = nn.Linear(hidden_dim, output_dim)
        self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)])
        self.sigmoid_output = sigmoid_output
    def forward(self, hidden_states):
        hidden_states = self.proj_in(hidden_states)
        hidden_states = self.activation(hidden_states)
        for layer in self.layers: hidden_states = self.activation(layer(hidden_states))
        hidden_states = self.proj_out(hidden_states)
        if self.sigmoid_output: hidden_states = F.sigmoid(hidden_states)
        return hidden_states
class SamMaskDecoder(nn.Module):
    def __init__(self, config: SamMaskDecoderConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_multimask_outputs = config.num_multimask_outputs
        self.num_mask_tokens = config.num_multimask_outputs + 1
        self.iou_token = nn.Embedding(1, self.hidden_size)
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size)
        self.transformer = SamTwoWayTransformer(config)
        self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2)
        self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2)
        self.upscale_layer_norm = SamLayerNorm(self.hidden_size // 4, data_format="channels_first")
        self.activation = nn.GELU()
        mlps_list = []
        for _ in range(self.num_mask_tokens): mlps_list += [SamFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)]
        self.output_hypernetworks_mlps = nn.ModuleList(mlps_list)
        self.iou_prediction_head = SamFeedForward(self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth)
    def forward(self, image_embeddings: torch.Tensor, image_positional_embeddings: torch.Tensor, sparse_prompt_embeddings: torch.Tensor, dense_prompt_embeddings: torch.Tensor,
    multimask_output: bool, output_attentions: Optional[bool] = None, attention_similarity: torch.Tensor = None, target_embedding: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor]:
        batch_size, num_channels, height, width = image_embeddings.shape
        point_batch_size = sparse_prompt_embeddings.shape[1]
        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
        output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1)
        if sparse_prompt_embeddings.sum().item() != 0: tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2)
        else: tokens = output_tokens
        point_embeddings = tokens.to(self.iou_token.weight.dtype)
        image_embeddings = image_embeddings + dense_prompt_embeddings
        image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0)
        image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0)
        point_embedding, image_embeddings, attentions = self.transformer(point_embeddings=point_embeddings, image_embeddings=image_embeddings, image_positional_embeddings=image_positional_embeddings,
        attention_similarity=attention_similarity, target_embedding=target_embedding, output_attentions=output_attentions)
        iou_token_out = point_embedding[:, :, 0, :]
        mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :]
        image_embeddings = image_embeddings.transpose(2, 3).reshape(batch_size * point_batch_size, num_channels, height, width)
        upscaled_embedding = self.upscale_conv1(image_embeddings)
        upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding))
        upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding))
        hyper_in_list = []
        for i in range(self.num_mask_tokens):
            current_mlp = self.output_hypernetworks_mlps[i]
            hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])]
        hyper_in = torch.stack(hyper_in_list, dim=2)
        _, num_channels, height, width = upscaled_embedding.shape
        upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width)
        masks = (hyper_in @ upscaled_embedding).reshape(batch_size, point_batch_size, -1, height, width)
        iou_pred = self.iou_prediction_head(iou_token_out)
        if multimask_output: mask_slice = slice(1, None)
        else: mask_slice = slice(0, 1)
        masks = masks[:, :, mask_slice, :, :]
        iou_pred = iou_pred[:, :, mask_slice]
        outputs = (masks, iou_pred)
        if output_attentions: outputs = outputs + (attentions,)
        else: outputs = outputs + (None,)
        return outputs
class SamPositionalEmbedding(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.scale = config.hidden_size // 2
        self.register_buffer("positional_embedding", self.scale * torch.randn((2, config.num_pos_feats)))
    def forward(self, input_coords, input_shape=None):
        coordinates = input_coords.clone()
        if input_shape is not None:
            coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1]
            coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0]
        coordinates = 2 * coordinates - 1
        coordinates = coordinates.to(self.positional_embedding.dtype)
        coordinates = coordinates @ self.positional_embedding
        coordinates = 2 * np.pi * coordinates
        return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1)
class SamMaskEmbedding(nn.Module):
    def __init__(self, config: SamPromptEncoderConfig):
        super().__init__()
        self.mask_input_channels = config.mask_input_channels // 4
        self.activation = ACT2FN[config.hidden_act]
        self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1)
        self.layer_norm1 = SamLayerNorm(self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first")
        self.layer_norm2 = SamLayerNorm(self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first")
    def forward(self, masks):
        hidden_states = self.conv1(masks)
        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.activation(hidden_states)
        dense_embeddings = self.conv3(hidden_states)
        return dense_embeddings
class SamPromptEncoder(nn.Module):
    def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedding):
        super().__init__()
        self.shared_embedding = shared_patch_embedding
        self.mask_embed = SamMaskEmbedding(config)
        self.no_mask_embed = nn.Embedding(1, config.hidden_size)
        self.image_embedding_size = (config.image_embedding_size, config.image_embedding_size)
        self.input_image_size = config.image_size
        self.point_embed = nn.ModuleList([nn.Embedding(1, config.hidden_size) for i in range(config.num_point_embeddings)])
        self.hidden_size = config.hidden_size
        self.not_a_point_embed = nn.Embedding(1, config.hidden_size)
    def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
        points = points + 0.5
        if pad:
            target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1])
            target_labels_shape = (points.shape[0], points.shape[1], 1)
            padding_point = torch.zeros(target_point_shape, device=points.device)
            padding_label = -torch.ones(target_labels_shape, device=labels.device)
            points = torch.cat([points, padding_point], dim=2)
            labels = torch.cat([labels, padding_label], dim=2)
        input_shape = (self.input_image_size, self.input_image_size)
        point_embedding = self.shared_embedding(points, input_shape)
        point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding)
        point_embedding = torch.where(labels[..., None] != -10, point_embedding, torch.tensor(0.0, dtype=point_embedding.dtype, device=point_embedding.device))
        point_embedding = torch.where((labels == 0)[:, :, :, None], point_embedding + self.point_embed[0].weight[None, None, :, :], point_embedding)
        point_embedding = torch.where((labels == 1)[:, :, :, None], point_embedding + self.point_embed[1].weight[None, None, :, :], point_embedding)
        return point_embedding
    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
        boxes = boxes + 0.5
        batch_size, nb_boxes = boxes.shape[:2]
        coords = boxes.reshape(batch_size, nb_boxes, 2, 2)
        input_shape = (self.input_image_size, self.input_image_size)
        corner_embedding = self.shared_embedding(coords, input_shape)
        corner_embedding[:, :, 0, :] += self.point_embed[2].weight
        corner_embedding[:, :, 1, :] += self.point_embed[3].weight
        return corner_embedding
    def forward(self, input_points: Optional[Tuple[torch.Tensor, torch.Tensor]], input_labels: Optional[torch.Tensor], input_boxes: Optional[torch.Tensor],
    input_masks: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
        sparse_embeddings = None
        batch_size = 1
        target_device = self.shared_embedding.positional_embedding.device
        if input_points is not None:
            batch_size, point_batch_size = input_points.shape[:2]
            if input_labels is None: raise ValueError("If points are provided, labels must also be provided.")
            point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
            sparse_embeddings = point_embeddings
        if input_boxes is not None:
            batch_size = input_boxes.shape[0]
            box_embeddings = self._embed_boxes(input_boxes)
            if sparse_embeddings is None: sparse_embeddings = box_embeddings
            else: sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2)
        if input_masks is not None: dense_embeddings = self.mask_embed(input_masks)
        else: dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1])
        if sparse_embeddings is None: sparse_embeddings = torch.zeros((batch_size, 1, 1, self.hidden_size), device=target_device)
        return sparse_embeddings, dense_embeddings
class SamVisionAttention(nn.Module):
    def __init__(self, config, window_size):
        super().__init__()
        input_size = ((config.image_size // config.patch_size, config.image_size // config.patch_size) if window_size == 0 else (window_size, window_size))
        self.num_attention_heads = config.num_attention_heads
        head_dim = config.hidden_size // config.num_attention_heads
        self.scale = head_dim**-0.5
        self.dropout = config.attention_dropout
        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
        self.proj = nn.Linear(config.hidden_size, config.hidden_size)
        self.use_rel_pos = config.use_rel_pos
        if self.use_rel_pos:
            if input_size is None: raise ValueError("Input size must be provided if using relative positional encoding.")
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
        max_rel_dist = int(2 * max(q_size, k_size) - 1)
        rel_pos_resized = F.interpolate(rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), size=max_rel_dist, mode="linear")
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
        return rel_pos_resized[relative_coords.long()]
    def add_decomposed_rel_pos(self, attn: torch.Tensor, query: torch.Tensor, rel_pos_h: torch.Tensor, rel_pos_w: torch.Tensor, q_size: Tuple[int, int], k_size: Tuple[int, int]) -> torch.Tensor:
        query_height, query_width = q_size
        key_height, key_width = k_size
        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)
        batch_size, _, dim = query.shape
        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
        rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
        rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)
        attn = attn.reshape(batch_size, query_height, query_width, key_height, key_width)
        attn = attn + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
        attn = attn.reshape(batch_size, query_height * query_width, key_height * key_width)
        return attn
    def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
        batch_size, height, width, _ = hidden_states.shape
        qkv = (self.qkv(hidden_states).reshape(batch_size, height * width, 3, self.num_attention_heads, -1).permute(2, 0, 3, 1, 4))
        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)
        attn_weights = (query * self.scale) @ key.transpose(-2, -1)
        if self.use_rel_pos: attn_weights = self.add_decomposed_rel_pos(attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width))
        attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)
        attn_output = self.proj(attn_output)
        if output_attentions: outputs = (attn_output, attn_weights)
        else: outputs = (attn_output, None)
        return outputs
class SamVisionLayer(nn.Module):
    def __init__(self, config, window_size):
        super().__init__()
        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attn = SamVisionAttention(config, window_size)
        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = SamMLPBlock(config)
        self.window_size = window_size
    def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
        batch_size, height, width, channel = hidden_states.shape
        pad_h = (window_size - height % window_size) % window_size
        pad_w = (window_size - width % window_size) % window_size
        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
        pad_height, pad_width = height + pad_h, width + pad_w
        hidden_states = hidden_states.reshape(batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel)
        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel)
        return windows, (pad_height, pad_width)
    def window_unpartition(self, windows: torch.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]) -> torch.Tensor:
        pad_height, pad_width = padding_shape
        height, width = original_shape
        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
        hidden_states = windows.reshape(batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1)
        hidden_states = (hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1))
        hidden_states = hidden_states[:, :height, :width, :].contiguous()
        return hidden_states
    def forward(self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False) -> Tuple[torch.FloatTensor]:
        residual = hidden_states
        hidden_states = self.layer_norm1(hidden_states)
        if self.window_size > 0:
            height, width = hidden_states.shape[1], hidden_states.shape[2]
            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)
        hidden_states, attn_weights = self.attn(hidden_states=hidden_states, output_attentions=output_attentions)
        if self.window_size > 0: hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))
        hidden_states = residual + hidden_states
        layernorm_output = self.layer_norm2(hidden_states)
        hidden_states = hidden_states + self.mlp(layernorm_output)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_weights,)
        return outputs
class SamVisionNeck(nn.Module):
    def __init__(self, config: SamVisionConfig):
        super().__init__()
        self.config = config
        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
        self.layer_norm1 = SamLayerNorm(config.output_channels, data_format="channels_first")
        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
        self.layer_norm2 = SamLayerNorm(config.output_channels, data_format="channels_first")
    def forward(self, hidden_states):
        hidden_states = hidden_states.permute(0, 3, 1, 2)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        return hidden_states
class SamVisionEncoder(nn.Module):
    def __init__(self, config: SamVisionConfig):
        super().__init__()
        self.config = config
        self.image_size = config.image_size
        self.patch_embed = SamPatchEmbeddings(config)
        self.pos_embed = None
        if config.use_abs_pos: self.pos_embed = nn.Parameter(torch.zeros(1, config.image_size // config.patch_size, config.image_size // config.patch_size, config.hidden_size))
        self.layers = nn.ModuleList()
        for i in range(config.num_hidden_layers):
            layer = SamVisionLayer(config, window_size=config.window_size if i not in config.global_attn_indexes else 0)
            self.layers.append(layer)
        self.neck = SamVisionNeck(config)
        self.gradient_checkpointing = False
    def get_input_embeddings(self): return self.patch_embed
    def forward(self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple, SamVisionEncoderOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if pixel_values is None: raise ValueError("You have to specify pixel_values")
        hidden_states = self.patch_embed(pixel_values)
        if self.pos_embed is not None: hidden_states = hidden_states + self.pos_embed
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layers):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states)
            else: layer_outputs = layer_module(hidden_states, output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        hidden_states = self.neck(hidden_states)
        if not return_dict:
            outputs = (hidden_states,)
            if output_hidden_states: outputs = outputs + (all_hidden_states,)
            if output_attentions: outputs = outputs + (all_self_attentions,)
            return outputs
        return SamVisionEncoderOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class SamPreTrainedModel(PreTrainedModel):
    config_class = SamConfig
    base_model_prefix = "sam"
    main_input_name = "pixel_values"
    _no_split_modules = ["SamVisionAttention"]
    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
SAM_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`SamConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SAM_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`SamProcessor`]. See [`SamProcessor.__call__`] for
            details.
        input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`):
            Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much
            better results. The points can be obtained by passing a list of list of list to the processor that will
            create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the
            second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict
            per input point), the third dimension is the number of points per segmentation mask (it is possible to pass
            multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal)
            coordinates of the point. If a different number of points is passed either for each image, or for each
            mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the
            computation of the embedding will be skipped for these points using the labels.
        input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`):
            Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the
            official implementation, there are 3 types of labels
            - `1`: the point is a point that contains the object of interest
            - `0`: the point is a point that does not contain the object of interest
            - `-1`: the point corresponds to the background
            We added the label:
            - `-10`: the point is a padding point, thus should be ignored by the prompt encoder
            The padding labels should be automatically done by the processor.
        input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`):
            Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to
            much better generated masks. The boxes can be obtained by passing a list of list of list to the processor,
            that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch
            size, the number of boxes per image and the coordinates of the top left and botton right point of the box.
            In the order (`x1`, `y1`, `x2`, `y2`):
            - `x1`: the x coordinate of the top left point of the input box
            - `y1`: the y coordinate of the top left point of the input box
            - `x2`: the x coordinate of the bottom right point of the input box
            - `y2`: the y coordinate of the bottom right point of the input box
        input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`):
            SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to
            generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be
            manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`).
        image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`):
            Image embeddings, this is used by the mask decder to generate masks and iou scores. For more memory
            efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings`
            method, and then feed them to the `forward` method instead of feeding the `pixel_values`.
        multimask_output (`bool`, *optional*):
            In the original implementation and paper, the model always outputs 3 masks per image (or per point / per
            bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the
            "best" mask, by specifying `multimask_output=False`.
        attention_similarity (`torch.FloatTensor`, *optional*):
            Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the
            model is used for personalization as introduced in [PerSAM](https://arxiv.org/abs/2305.03048).
        target_embedding (`torch.FloatTensor`, *optional*):
            Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case
            the model is used for personalization as introduced in [PerSAM](https://arxiv.org/abs/2305.03048).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("Segment Anything Model (SAM) for generating segmentation masks, given an input image and ", " optional 2D location and bounding boxes.", SAM_START_DOCSTRING)
class SamModel(SamPreTrainedModel):
    _tied_weights_keys = ["prompt_encoder.shared_embedding.positional_embedding"]
    def __init__(self, config):
        super().__init__(config)
        self.shared_image_embedding = SamPositionalEmbedding(config.vision_config)
        self.vision_encoder = SamVisionEncoder(config.vision_config)
        self.prompt_encoder = SamPromptEncoder(config.prompt_encoder_config, self.shared_image_embedding)
        self.mask_decoder = SamMaskDecoder(config.mask_decoder_config)
        self.post_init()
    def get_input_embeddings(self): return self.vision_encoder.get_input_embeddings()
    def get_image_wide_positional_embeddings(self):
        size = self.config.prompt_encoder_config.image_embedding_size
        target_device = self.shared_image_embedding.positional_embedding.device
        target_dtype = self.shared_image_embedding.positional_embedding.dtype
        grid = torch.ones((size, size), device=target_device, dtype=target_dtype)
        y_embed = grid.cumsum(dim=0) - 0.5
        x_embed = grid.cumsum(dim=1) - 0.5
        y_embed = y_embed / size
        x_embed = x_embed / size
        positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1))
        return positional_embedding.permute(2, 0, 1).unsqueeze(0)
    @torch.no_grad()
    def get_image_embeddings(self, pixel_values, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None):
        vision_output = self.vision_encoder(pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        image_embeddings = vision_output[0]
        return image_embeddings
    @torch.no_grad()
    def get_prompt_embeddings(self, input_points: Optional[torch.FloatTensor] = None, input_labels: Optional[torch.LongTensor] = None, input_boxes: Optional[torch.FloatTensor] = None,
    input_masks: Optional[torch.LongTensor] = None):
        prompt_output = self.prompt_encoder(input_points=input_points, input_labels=input_labels, input_boxes=input_boxes, input_masks=input_masks)
        return prompt_output
    @add_start_docstrings_to_model_forward(SAM_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.FloatTensor] = None, input_points: Optional[torch.FloatTensor] = None, input_labels: Optional[torch.LongTensor] = None,
    input_boxes: Optional[torch.FloatTensor] = None, input_masks: Optional[torch.LongTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None,
    multimask_output: bool = True, attention_similarity: Optional[torch.FloatTensor] = None, target_embedding: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs) -> List[Dict[str, torch.Tensor]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if pixel_values is None and image_embeddings is None: raise ValueError("Either pixel_values or image_embeddings must be provided.")
        if pixel_values is not None and image_embeddings is not None: raise ValueError("Only one of pixel_values and image_embeddings can be provided.")
        if input_points is not None and len(input_points.shape) != 4: raise ValueError("The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.", " got {}.".format(input_points.shape))
        if input_boxes is not None and len(input_boxes.shape) != 3: raise ValueError("The input_points must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`.", " got {}.".format(input_boxes.shape))
        if input_points is not None and input_boxes is not None:
            point_batch_size = input_points.shape[1]
            box_batch_size = input_boxes.shape[1]
            if point_batch_size != box_batch_size: raise ValueError("You should provide as many bounding boxes as input points per box. Got {} and {}.".format(point_batch_size, box_batch_size))
        image_positional_embeddings = self.get_image_wide_positional_embeddings()
        batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0]
        image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1)
        vision_attentions = None
        vision_hidden_states = None
        if pixel_values is not None:
            vision_outputs = self.vision_encoder(pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
            image_embeddings = vision_outputs[0]
            if output_hidden_states: vision_hidden_states = vision_outputs[1]
            if output_attentions: vision_attentions = vision_outputs[-1]
        if input_points is not None and input_labels is None: input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device)
        if input_points is not None and image_embeddings.shape[0] != input_points.shape[0]:
            raise ValueError("The batch size of the image embeddings and the input points must be the same. ", "Got {} and {} respectively.".format(image_embeddings.shape[0], input_points.shape[0]),
            " if you want to pass multiple points for the same image, make sure that you passed", " input_points of shape (batch_size, point_batch_size, num_points_per_image, 3) and",
            " input_labels of shape (batch_size, point_batch_size, num_points_per_image)")
        sparse_embeddings, dense_embeddings = self.prompt_encoder(input_points=input_points, input_labels=input_labels, input_boxes=input_boxes, input_masks=input_masks)
        low_res_masks, iou_predictions, mask_decoder_attentions = self.mask_decoder(image_embeddings=image_embeddings, image_positional_embeddings=image_positional_embeddings,
        sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, attention_similarity=attention_similarity,
        target_embedding=target_embedding, output_attentions=output_attentions)
        if not return_dict:
            output = (iou_predictions, low_res_masks)
            if output_hidden_states: output = output + (vision_hidden_states,)
            if output_attentions: output = output + (vision_attentions, mask_decoder_attentions)
            return output
        return SamImageSegmentationOutput(iou_scores=iou_predictions, pred_masks=low_res_masks, vision_hidden_states=vision_hidden_states, vision_attentions=vision_attentions,
        mask_decoder_attentions=mask_decoder_attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
