"""
    #################################################################################################################################################################################################
    # 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 math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_glpn import GLPNConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GLPNConfig"
_CHECKPOINT_FOR_DOC = "vinvino02/glpn-kitti"
_EXPECTED_OUTPUT_SHAPE = [1, 512, 15, 20]
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    if drop_prob == 0.0 or not training: return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()
    output = input.div(keep_prob) * random_tensor
    return output
class GLPNDropPath(nn.Module):
    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return drop_path(hidden_states, self.drop_prob, self.training)
    def extra_repr(self) -> str: return "p={}".format(self.drop_prob)
class GLPNOverlapPatchEmbeddings(nn.Module):
    def __init__(self, patch_size, stride, num_channels, hidden_size):
        super().__init__()
        self.proj = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=stride, padding=patch_size // 2)
        self.layer_norm = nn.LayerNorm(hidden_size)
    def forward(self, pixel_values):
        embeddings = self.proj(pixel_values)
        _, _, height, width = embeddings.shape
        embeddings = embeddings.flatten(2).transpose(1, 2)
        embeddings = self.layer_norm(embeddings)
        return embeddings, height, width
class GLPNEfficientSelfAttention(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        if self.hidden_size % self.num_attention_heads != 0: raise ValueError(f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention heads ({self.num_attention_heads})")
        self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(self.hidden_size, self.all_head_size)
        self.key = nn.Linear(self.hidden_size, self.all_head_size)
        self.value = nn.Linear(self.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.sr_ratio = sequence_reduction_ratio
        if sequence_reduction_ratio > 1:
            self.sr = nn.Conv2d(hidden_size, hidden_size, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio)
            self.layer_norm = nn.LayerNorm(hidden_size)
    def transpose_for_scores(self, hidden_states):
        new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        hidden_states = hidden_states.view(new_shape)
        return hidden_states.permute(0, 2, 1, 3)
    def forward(self, hidden_states, height, width, output_attentions=False):
        query_layer = self.transpose_for_scores(self.query(hidden_states))
        if self.sr_ratio > 1:
            batch_size, seq_len, num_channels = hidden_states.shape
            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
            hidden_states = self.sr(hidden_states)
            hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
            hidden_states = self.layer_norm(hidden_states)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class GLPNSelfOutput(nn.Module):
    def __init__(self, config, hidden_size):
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
class GLPNAttention(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
        super().__init__()
        self.self = GLPNEfficientSelfAttention(config=config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sequence_reduction_ratio=sequence_reduction_ratio)
        self.output = GLPNSelfOutput(config, hidden_size=hidden_size)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states, height, width, output_attentions=False):
        self_outputs = self.self(hidden_states, height, width, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class GLPNDWConv(nn.Module):
    def __init__(self, dim=768):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
    def forward(self, hidden_states, height, width):
        batch_size, seq_len, num_channels = hidden_states.shape
        hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width)
        hidden_states = self.dwconv(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
        return hidden_states
class GLPNMixFFN(nn.Module):
    def __init__(self, config, in_features, hidden_features=None, out_features=None):
        super().__init__()
        out_features = out_features or in_features
        self.dense1 = nn.Linear(in_features, hidden_features)
        self.dwconv = GLPNDWConv(hidden_features)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
        self.dense2 = nn.Linear(hidden_features, out_features)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states, height, width):
        hidden_states = self.dense1(hidden_states)
        hidden_states = self.dwconv(hidden_states, height, width)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense2(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
class GLPNLayer(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, drop_path, sequence_reduction_ratio, mlp_ratio):
        super().__init__()
        self.layer_norm_1 = nn.LayerNorm(hidden_size)
        self.attention = GLPNAttention(config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sequence_reduction_ratio=sequence_reduction_ratio)
        self.drop_path = GLPNDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.layer_norm_2 = nn.LayerNorm(hidden_size)
        mlp_hidden_size = int(hidden_size * mlp_ratio)
        self.mlp = GLPNMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size)
    def forward(self, hidden_states, height, width, output_attentions=False):
        self_attention_outputs = self.attention(self.layer_norm_1(hidden_states), height, width, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        attention_output = self.drop_path(attention_output)
        hidden_states = attention_output + hidden_states
        mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width)
        mlp_output = self.drop_path(mlp_output)
        layer_output = mlp_output + hidden_states
        outputs = (layer_output,) + outputs
        return outputs
class GLPNEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
        embeddings = []
        for i in range(config.num_encoder_blocks): embeddings.append(GLPNOverlapPatchEmbeddings(patch_size=config.patch_sizes[i], stride=config.strides[i],
        num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], hidden_size=config.hidden_sizes[i]))
        self.patch_embeddings = nn.ModuleList(embeddings)
        blocks = []
        cur = 0
        for i in range(config.num_encoder_blocks):
            layers = []
            if i != 0: cur += config.depths[i - 1]
            for j in range(config.depths[i]):
                layers.append(GLPNLayer(config, hidden_size=config.hidden_sizes[i], num_attention_heads=config.num_attention_heads[i], drop_path=dpr[cur + j],
                sequence_reduction_ratio=config.sr_ratios[i], mlp_ratio=config.mlp_ratios[i]))
            blocks.append(nn.ModuleList(layers))
        self.block = nn.ModuleList(blocks)
        self.layer_norm = nn.ModuleList([nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)])
    def forward(self, pixel_values, output_attentions=False, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        batch_size = pixel_values.shape[0]
        hidden_states = pixel_values
        for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
            embedding_layer, block_layer, norm_layer = x
            hidden_states, height, width = embedding_layer(hidden_states)
            for i, blk in enumerate(block_layer):
                layer_outputs = blk(hidden_states, height, width, output_attentions)
                hidden_states = layer_outputs[0]
                if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
            hidden_states = norm_layer(hidden_states)
            hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class GLPNPreTrainedModel(PreTrainedModel):
    config_class = GLPNConfig
    base_model_prefix = "glpn"
    main_input_name = "pixel_values"
    _no_split_modules = []
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
GLPN_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`GLPNConfig`]): 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.
"""
GLPN_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`GLPNImageProcessor.__call__`] for details.
        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("The bare GLPN encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.", GLPN_START_DOCSTRING)
class GLPNModel(GLPNPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.encoder = GLPNEncoder(config)
        self.post_init()
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(GLPN_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
    def forward(self, pixel_values: torch.FloatTensor, 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
        encoder_outputs = self.encoder(pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        if not return_dict: return (sequence_output,) + encoder_outputs[1:]
        return BaseModelOutput(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
class GLPNSelectiveFeatureFusion(nn.Module):
    def __init__(self, in_channel=64):
        super().__init__()
        self.convolutional_layer1 = nn.Sequential(nn.Conv2d(in_channels=int(in_channel * 2), out_channels=in_channel, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(in_channel), nn.ReLU())
        self.convolutional_layer2 = nn.Sequential(nn.Conv2d(in_channels=in_channel, out_channels=int(in_channel / 2), kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(int(in_channel / 2)), nn.ReLU())
        self.convolutional_layer3 = nn.Conv2d(in_channels=int(in_channel / 2), out_channels=2, kernel_size=3, stride=1, padding=1)
        self.sigmoid = nn.Sigmoid()
    def forward(self, local_features, global_features):
        features = torch.cat((local_features, global_features), dim=1)
        features = self.convolutional_layer1(features)
        features = self.convolutional_layer2(features)
        features = self.convolutional_layer3(features)
        attn = self.sigmoid(features)
        hybrid_features = local_features * attn[:, 0, :, :].unsqueeze(1) + global_features * attn[:, 1, :, :].unsqueeze(1)
        return hybrid_features
class GLPNDecoderStage(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        should_skip = in_channels == out_channels
        self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1) if not should_skip else nn.Identity()
        self.fusion = GLPNSelectiveFeatureFusion(out_channels)
        self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
    def forward(self, hidden_state, residual=None):
        hidden_state = self.convolution(hidden_state)
        if residual is not None: hidden_state = self.fusion(hidden_state, residual)
        hidden_state = self.upsample(hidden_state)
        return hidden_state
        hidden_state = self.upsample(hidden_state)
        return hidden_state
class GLPNDecoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        reserved_hidden_sizes = config.hidden_sizes[::-1]
        out_channels = config.decoder_hidden_size
        self.stages = nn.ModuleList([GLPNDecoderStage(hidden_size, out_channels) for hidden_size in reserved_hidden_sizes])
        self.stages[0].fusion = None
        self.final_upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
    def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]:
        stage_hidden_states = []
        stage_hidden_state = None
        for hidden_state, stage in zip(hidden_states[::-1], self.stages):
            stage_hidden_state = stage(hidden_state, stage_hidden_state)
            stage_hidden_states.append(stage_hidden_state)
        stage_hidden_states[-1] = self.final_upsample(stage_hidden_state)
        return stage_hidden_states
class SiLogLoss(nn.Module):
    def __init__(self, lambd=0.5):
        super().__init__()
        self.lambd = lambd
    def forward(self, pred, target):
        valid_mask = (target > 0).detach()
        diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask])
        loss = torch.sqrt(torch.pow(diff_log, 2).mean() - self.lambd * torch.pow(diff_log.mean(), 2))
        return loss
class GLPNDepthEstimationHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        channels = config.decoder_hidden_size
        self.head = nn.Sequential(nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=False), nn.Conv2d(channels, 1, kernel_size=3, stride=1, padding=1))
    def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor:
        hidden_states = hidden_states[self.config.head_in_index]
        hidden_states = self.head(hidden_states)
        predicted_depth = torch.sigmoid(hidden_states) * self.config.max_depth
        predicted_depth = predicted_depth.squeeze(dim=1)
        return predicted_depth
@add_start_docstrings("GLPN Model transformer with a lightweight depth estimation head on top e.g. for KITTI, NYUv2.", GLPN_START_DOCSTRING)
class GLPNForDepthEstimation(GLPNPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.glpn = GLPNModel(config)
        self.decoder = GLPNDecoder(config)
        self.head = GLPNDepthEstimationHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(GLPN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, pixel_values: torch.FloatTensor, labels: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        outputs = self.glpn(pixel_values, output_attentions=output_attentions, output_hidden_states=True, return_dict=return_dict)
        hidden_states = outputs.hidden_states if return_dict else outputs[1]
        out = self.decoder(hidden_states)
        predicted_depth = self.head(out)
        loss = None
        if labels is not None:
            loss_fct = SiLogLoss()
            loss = loss_fct(predicted_depth, labels)
        if not return_dict:
            if output_hidden_states: output = (predicted_depth,) + outputs[1:]
            else: output = (predicted_depth,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return DepthEstimatorOutput(loss=loss, predicted_depth=predicted_depth, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.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.                                                                 #
    #################################################################################################################################################################################################
"""
