"""
    #################################################################################################################################################################################################
    # 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 Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BackboneOutput, BaseModelOutput, ImageClassifierOutput
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 ...utils.backbone_utils import BackboneMixin
from .configuration_pvt_v2 import PvtV2Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "PvtV2Config"
_CHECKPOINT_FOR_DOC = "OpenGVLab/pvt_v2_b0"
_EXPECTED_OUTPUT_SHAPE = [1, 256, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "OpenGVLab/pvt_v2_b0"
_IMAGE_CLASS_EXPECTED_OUTPUT = "LABEL_281"
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 PvtV2DropPath(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 PvtV2OverlapPatchEmbeddings(nn.Module):
    def __init__(self, config: PvtV2Config, layer_idx: int):
        super().__init__()
        patch_size = config.patch_sizes[layer_idx]
        patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else patch_size
        stride = config.strides[layer_idx]
        num_channels = config.num_channels if layer_idx == 0 else config.hidden_sizes[layer_idx - 1]
        hidden_size = config.hidden_sizes[layer_idx]
        self.patch_size = patch_size
        self.proj = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=stride, padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
    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 PvtV2DepthWiseConv(nn.Module):
    def __init__(self, config: PvtV2Config, dim: int = 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 PvtV2SelfAttention(nn.Module):
    def __init__(self, config: PvtV2Config, hidden_size: int, num_attention_heads: int, spatial_reduction_ratio: int):
        super().__init__()
        self.linear_attention = config.linear_attention
        self.pruned_heads = set()
        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, bias=config.qkv_bias)
        self.key = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.attn_drop = nn.Dropout(config.attention_probs_dropout_prob)
        self.proj = nn.Linear(self.hidden_size, self.hidden_size)
        self.proj_drop = nn.Dropout(config.hidden_dropout_prob)
        self.spatial_reduction_ratio = spatial_reduction_ratio
        if self.linear_attention:
            self.pool = nn.AdaptiveAvgPool2d(7)
            self.spatial_reduction = nn.Conv2d(self.hidden_size, self.hidden_size, kernel_size=1, stride=1)
            self.layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
            self.act = nn.GELU()
        elif spatial_reduction_ratio > 1:
            self.spatial_reduction = nn.Conv2d(self.hidden_size, self.hidden_size, kernel_size=spatial_reduction_ratio, stride=spatial_reduction_ratio)
            self.layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
    def transpose_for_scores(self, hidden_states) -> torch.Tensor:
        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: torch.Tensor, height: int, width: int, output_attentions: bool = False) -> Tuple[torch.Tensor]:
        batch_size, seq_len, num_channels = hidden_states.shape
        query_layer = self.transpose_for_scores(self.query(hidden_states))
        if self.linear_attention:
            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
            hidden_states = (self.spatial_reduction(self.pool(hidden_states)).reshape(batch_size, num_channels, -1).permute(0, 2, 1))
            hidden_states = self.act(self.layer_norm(hidden_states))
        elif self.spatial_reduction_ratio > 1:
            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
            hidden_states = (self.spatial_reduction(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.attn_drop(attention_probs)
        context_layer = (attention_probs @ value_layer).transpose(1, 2).reshape(batch_size, seq_len, num_channels)
        context_layer = self.proj(context_layer)
        context_layer = self.proj_drop(context_layer)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.num_attention_heads, self.attention_head_size, self.pruned_heads)
        self.query = prune_linear_layer(self.query, index)
        self.key = prune_linear_layer(self.key, index)
        self.value = prune_linear_layer(self.value, index)
        self.proj = prune_linear_layer(self.proj, index, dim=1)
        self.num_attention_heads = self.num_attention_heads - len(heads)
        self.all_head_size = self.attention_head_size * self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
class PvtV2ConvFeedForwardNetwork(nn.Module):
    def __init__(self, config: PvtV2Config, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None):
        super().__init__()
        out_features = out_features if out_features is not None else in_features
        self.dense1 = nn.Linear(in_features, hidden_features)
        self.dwconv = PvtV2DepthWiseConv(config, 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)
        self.relu = nn.ReLU() if config.linear_attention else nn.Identity()
    def forward(self, hidden_states: torch.Tensor, height, width) -> torch.Tensor:
        hidden_states = self.dense1(hidden_states)
        hidden_states = self.relu(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 PvtV2BlockLayer(nn.Module):
    def __init__(self, config: PvtV2Config, layer_idx: int, drop_path: float = 0.0):
        super().__init__()
        hidden_size: int = config.hidden_sizes[layer_idx]
        num_attention_heads: int = config.num_attention_heads[layer_idx]
        spatial_reduction_ratio: int = config.sr_ratios[layer_idx]
        mlp_ratio: float = config.mlp_ratios[layer_idx]
        self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
        self.attention = PvtV2SelfAttention(config=config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, spatial_reduction_ratio=spatial_reduction_ratio)
        self.drop_path = PvtV2DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
        mlp_hidden_size = int(hidden_size * mlp_ratio)
        self.mlp = PvtV2ConvFeedForwardNetwork(config=config, in_features=hidden_size, hidden_features=mlp_hidden_size)
    def forward(self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False):
        self_attention_outputs = self.attention(hidden_states=self.layer_norm_1(hidden_states), height=height, width=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 = hidden_states + mlp_output
        outputs = (layer_output,) + outputs
        return outputs
class PvtV2EncoderLayer(nn.Module):
    def __init__(self, config: PvtV2Config, layer_idx: int):
        super().__init__()
        self.patch_embedding = PvtV2OverlapPatchEmbeddings(config=config, layer_idx=layer_idx)
        drop_path_decays = torch.linspace(0, config.drop_path_rate, sum(config.depths)).tolist()
        block_layers = []
        for block_idx in range(config.depths[layer_idx]): block_layers.append(PvtV2BlockLayer(config=config, layer_idx=layer_idx, drop_path=drop_path_decays[sum(config.depths[:layer_idx]) + block_idx]))
        self.blocks = nn.ModuleList(block_layers)
        self.layer_norm = nn.LayerNorm(config.hidden_sizes[layer_idx], eps=config.layer_norm_eps)
    def forward(self, hidden_states, output_attentions):
        all_self_attentions = () if output_attentions else None
        hidden_states, height, width = self.patch_embedding(hidden_states)
        for block in self.blocks:
            layer_outputs = block(hidden_states, height, width, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions += (layer_outputs[1],)
        hidden_states = self.layer_norm(hidden_states)
        outputs = (hidden_states,)
        if output_attentions: outputs += (all_self_attentions,)
        return outputs, height, width
class PvtV2Encoder(nn.Module):
    def __init__(self, config: PvtV2Config):
        super().__init__()
        self.config = config
        self.gradient_checkpointing = False
        self.layers = nn.ModuleList([PvtV2EncoderLayer(config, i) for i in range(config.num_encoder_blocks)])
    def forward(self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple, BaseModelOutput]:
        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, layer in enumerate(self.layers):
            if self.gradient_checkpointing and self.training: layer_output = self._gradient_checkpointing_func(layer.__call__, hidden_states, output_attentions)
            else: layer_output = layer(hidden_states, output_attentions)
            outputs, height, width = layer_output
            hidden_states = outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (outputs[1],)
            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 PvtV2PreTrainedModel(PreTrainedModel):
    config_class = PvtV2Config
    base_model_prefix = "pvt_v2"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        if isinstance(module, nn.Linear):
            module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Conv2d):
            fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
            fan_out //= module.groups
            module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if module.bias is not None: module.bias.data.zero_()
PVT_V2_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 ([`~PvtV2Config`]): 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.
"""
PVT_V2_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PvtImageProcessor.__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 Pvt-v2 encoder outputting raw hidden-states without any specific head on top.", PVT_V2_START_DOCSTRING)
class PvtV2Model(PvtV2PreTrainedModel):
    def __init__(self, config: PvtV2Config):
        super().__init__(config)
        self.config = config
        self.encoder = PvtV2Encoder(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(PVT_V2_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
    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=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)
@add_start_docstrings("Pvt-v2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.", PVT_V2_START_DOCSTRING)
class PvtV2ForImageClassification(PvtV2PreTrainedModel):
    def __init__(self, config: PvtV2Config) -> None:
        super().__init__(config)
        self.num_labels = config.num_labels
        self.pvt_v2 = PvtV2Model(config)
        self.classifier = (nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity())
        self.post_init()
    @add_start_docstrings_to_model_forward(PVT_V2_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
    def forward(self, pixel_values: Optional[torch.Tensor], labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[tuple, ImageClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.pvt_v2(pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        batch_size = sequence_output.shape[0]
        sequence_output = sequence_output.permute(0, 2, 3, 1)
        sequence_output = sequence_output.reshape(batch_size, -1, self.config.hidden_sizes[-1])
        sequence_output = sequence_output.mean(dim=1)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return ImageClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("PVTv2 backbone, to be used with frameworks like DETR and MaskFormer.", PVT_V2_START_DOCSTRING)
class PvtV2Backbone(PvtV2Model, BackboneMixin):
    def __init__(self, config: PvtV2Config):
        super().__init__(config)
        super()._init_backbone(config)
        self.num_features = config.hidden_sizes
    @add_start_docstrings_to_model_forward(PVT_V2_INPUTS_DOCSTRING)
    def forward(self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> BackboneOutput:
        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.encoder(pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=True, return_dict=return_dict)
        hidden_states = outputs.hidden_states
        feature_maps = ()
        for idx, stage in enumerate(self.stage_names):
            if stage in self.out_features: feature_maps += (hidden_states[idx],)
        if not return_dict:
            output = (feature_maps,)
            if output_hidden_states: output += (outputs.hidden_states,)
            return output
        return BackboneOutput(feature_maps=feature_maps, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=None)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
