"""
    #################################################################################################################################################################################################
    # 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 itertools
from dataclasses import dataclass
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 BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ....modeling_utils import PreTrainedModel
from ....utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging)
from .configuration_efficientformer import EfficientFormerConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "EfficientFormerConfig"
_CHECKPOINT_FOR_DOC = "snap-research/efficientformer-l1-300"
_EXPECTED_OUTPUT_SHAPE = [1, 49, 448]
_IMAGE_CLASS_CHECKPOINT = "snap-research/efficientformer-l1-300"
_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
class EfficientFormerPatchEmbeddings(nn.Module):
    def __init__(self, config: EfficientFormerConfig, num_channels: int, embed_dim: int, apply_norm: bool = True):
        super().__init__()
        self.num_channels = num_channels
        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=config.downsample_patch_size, stride=config.downsample_stride, padding=config.downsample_pad)
        self.norm = nn.BatchNorm2d(embed_dim, eps=config.batch_norm_eps) if apply_norm else nn.Identity()
    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        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.")
        embeddings = self.projection(pixel_values)
        embeddings = self.norm(embeddings)
        return embeddings
class EfficientFormerSelfAttention(nn.Module):
    def __init__(self, dim: int, key_dim: int, num_heads: int, attention_ratio: int, resolution: int):
        super().__init__()
        self.num_heads = num_heads
        self.key_dim = key_dim
        self.attention_ratio = attention_ratio
        self.scale = key_dim**-0.5
        self.total_key_dim = key_dim * num_heads
        self.expanded_key_dim = int(attention_ratio * key_dim)
        self.total_expanded_key_dim = int(self.expanded_key_dim * num_heads)
        hidden_size = self.total_expanded_key_dim + self.total_key_dim * 2
        self.qkv = nn.Linear(dim, hidden_size)
        self.projection = nn.Linear(self.total_expanded_key_dim, dim)
        points = list(itertools.product(range(resolution), range(resolution)))
        num_points = len(points)
        attention_offsets = {}
        idxs = []
        for point_1 in points:
            for point_2 in points:
                offset = (abs(point_1[0] - point_2[0]), abs(point_1[1] - point_2[1]))
                if offset not in attention_offsets: attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(num_points, num_points))
    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, "ab"): del self.ab
        else: self.ab = self.attention_biases[:, self.attention_bias_idxs]
    def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False) -> Tuple[torch.Tensor]:
        batch_size, sequence_length, num_channels = hidden_states.shape
        qkv = self.qkv(hidden_states)
        query_layer, key_layer, value_layer = qkv.reshape(batch_size, sequence_length, self.num_heads, -1).split([self.key_dim, self.key_dim, self.expanded_key_dim], dim=3)
        query_layer = query_layer.permute(0, 2, 1, 3)
        key_layer = key_layer.permute(0, 2, 1, 3)
        value_layer = value_layer.permute(0, 2, 1, 3)
        if not self.training: self.ab = self.ab.to(self.attention_biases.device)
        attention_probs = (torch.matmul(query_layer, key_layer.transpose(-2, -1))) * self.scale + (self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab)
        attention_probs = attention_probs.softmax(dim=-1)
        context_layer = torch.matmul(attention_probs, value_layer).transpose(1, 2)
        context_layer = context_layer.reshape(batch_size, sequence_length, self.total_expanded_key_dim)
        context_layer = self.projection(context_layer)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class EfficientFormerConvStem(nn.Module):
    def __init__(self, config: EfficientFormerConfig, out_channels: int):
        super().__init__()
        self.convolution1 = nn.Conv2d(config.num_channels, out_channels // 2, kernel_size=3, stride=2, padding=1)
        self.batchnorm_before = nn.BatchNorm2d(out_channels // 2, eps=config.batch_norm_eps)
        self.convolution2 = nn.Conv2d(out_channels // 2, out_channels, kernel_size=3, stride=2, padding=1)
        self.batchnorm_after = nn.BatchNorm2d(out_channels, eps=config.batch_norm_eps)
        self.activation = nn.ReLU()
    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        features = self.batchnorm_before(self.convolution1(pixel_values))
        features = self.activation(features)
        features = self.batchnorm_after(self.convolution2(features))
        features = self.activation(features)
        return features
class EfficientFormerPooling(nn.Module):
    def __init__(self, pool_size: int):
        super().__init__()
        self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        output = self.pool(hidden_states) - hidden_states
        return output
class EfficientFormerDenseMlp(nn.Module):
    def __init__(self, config: EfficientFormerConfig, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.linear_in = nn.Linear(in_features, hidden_features)
        self.activation = ACT2FN[config.hidden_act]
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.linear_out = nn.Linear(hidden_features, out_features)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.linear_in(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.linear_out(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
class EfficientFormerConvMlp(nn.Module):
    def __init__(self, config: EfficientFormerConfig, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, drop: float = 0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.convolution1 = nn.Conv2d(in_features, hidden_features, 1)
        self.activation = ACT2FN[config.hidden_act]
        self.convolution2 = nn.Conv2d(hidden_features, out_features, 1)
        self.dropout = nn.Dropout(drop)
        self.batchnorm_before = nn.BatchNorm2d(hidden_features, eps=config.batch_norm_eps)
        self.batchnorm_after = nn.BatchNorm2d(out_features, eps=config.batch_norm_eps)
    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        hidden_state = self.convolution1(hidden_state)
        hidden_state = self.batchnorm_before(hidden_state)
        hidden_state = self.activation(hidden_state)
        hidden_state = self.dropout(hidden_state)
        hidden_state = self.convolution2(hidden_state)
        hidden_state = self.batchnorm_after(hidden_state)
        hidden_state = self.dropout(hidden_state)
        return hidden_state
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 EfficientFormerDropPath(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 EfficientFormerFlat(nn.Module):
    def __init__(self): super().__init__()
    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
        return hidden_states
class EfficientFormerMeta3D(nn.Module):
    def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
        super().__init__()
        self.token_mixer = EfficientFormerSelfAttention(dim=config.dim, key_dim=config.key_dim, num_heads=config.num_attention_heads, attention_ratio=config.attention_ratio, resolution=config.resolution)
        self.layernorm1 = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.layernorm2 = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        mlp_hidden_dim = int(dim * config.mlp_expansion_ratio)
        self.mlp = EfficientFormerDenseMlp(config, in_features=dim, hidden_features=mlp_hidden_dim)
        self.drop_path = EfficientFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.use_layer_scale = config.use_layer_scale
        if config.use_layer_scale:
            self.layer_scale_1 = nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
    def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False) -> Tuple[torch.Tensor]:
        self_attention_outputs = self.token_mixer(self.layernorm1(hidden_states), output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        if self.use_layer_scale:
            layer_output = hidden_states + self.drop_path(self.layer_scale_1.unsqueeze(0).unsqueeze(0) * attention_output)
            layer_output = layer_output + self.drop_path(self.layer_scale_2.unsqueeze(0).unsqueeze(0) * self.mlp(self.layernorm2(layer_output)))
        else:
            layer_output = hidden_states + self.drop_path(attention_output)
            layer_output = layer_output + self.drop_path(self.mlp(self.layernorm2(layer_output)))
        outputs = (layer_output,) + outputs
        return outputs
class EfficientFormerMeta3DLayers(nn.Module):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__()
        drop_paths = [config.drop_path_rate * (block_idx + sum(config.depths[:-1])) for block_idx in range(config.num_meta3d_blocks)]
        self.blocks = nn.ModuleList([EfficientFormerMeta3D(config, config.hidden_sizes[-1], drop_path=drop_path) for drop_path in drop_paths])
    def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False) -> Tuple[torch.Tensor]:
        all_attention_outputs = () if output_attentions else None
        for layer_module in self.blocks:
            if isinstance(hidden_states, tuple): hidden_states = hidden_states[0]
            hidden_states = layer_module(hidden_states, output_attentions)
            if output_attentions: all_attention_outputs = all_attention_outputs + (hidden_states[1],)
        if output_attentions:
            outputs = (hidden_states[0],) + all_attention_outputs
            return outputs
        return hidden_states
class EfficientFormerMeta4D(nn.Module):
    def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
        super().__init__()
        pool_size = config.pool_size if config.pool_size is not None else 3
        self.token_mixer = EfficientFormerPooling(pool_size=pool_size)
        mlp_hidden_dim = int(dim * config.mlp_expansion_ratio)
        self.mlp = EfficientFormerConvMlp(config, in_features=dim, hidden_features=mlp_hidden_dim, drop=config.hidden_dropout_prob)
        self.drop_path = EfficientFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.use_layer_scale = config.use_layer_scale
        if config.use_layer_scale:
            self.layer_scale_1 = nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        outputs = self.token_mixer(hidden_states)
        if self.use_layer_scale:
            layer_output = hidden_states + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * outputs)
            layer_output = layer_output + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(layer_output))
        else:
            layer_output = hidden_states + self.drop_path(outputs)
            layer_output = layer_output + self.drop_path(self.mlp(layer_output))
        return layer_output
class EfficientFormerMeta4DLayers(nn.Module):
    def __init__(self, config: EfficientFormerConfig, stage_idx: int):
        super().__init__()
        num_layers = (config.depths[stage_idx] if stage_idx != -1 else config.depths[stage_idx] - config.num_meta3d_blocks)
        drop_paths = [config.drop_path_rate * (block_idx + sum(config.depths[:stage_idx])) for block_idx in range(num_layers)]
        self.blocks = nn.ModuleList([EfficientFormerMeta4D(config, config.hidden_sizes[stage_idx], drop_path=drop_path) for drop_path in drop_paths])
    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        for layer_module in self.blocks: hidden_states = layer_module(hidden_states)
        return hidden_states
class EfficientFormerIntermediateStage(nn.Module):
    def __init__(self, config: EfficientFormerConfig, index: int):
        super().__init__()
        self.meta4D_layers = EfficientFormerMeta4DLayers(config, index)
    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        hidden_states = self.meta4D_layers(hidden_states)
        return hidden_states
class EfficientFormerLastStage(nn.Module):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__()
        self.meta4D_layers = EfficientFormerMeta4DLayers(config, -1)
        self.flat = EfficientFormerFlat()
        self.meta3D_layers = EfficientFormerMeta3DLayers(config)
    def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False) -> Tuple[torch.Tensor]:
        hidden_states = self.meta4D_layers(hidden_states)
        hidden_states = self.flat(hidden_states)
        hidden_states = self.meta3D_layers(hidden_states, output_attentions)
        return hidden_states
class EfficientFormerEncoder(nn.Module):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__()
        self.config = config
        num_intermediate_stages = len(config.depths) - 1
        downsamples = [config.downsamples[i] or config.hidden_sizes[i] != config.hidden_sizes[i + 1] for i in range(num_intermediate_stages)]
        intermediate_stages = []
        for i in range(num_intermediate_stages):
            intermediate_stages.append(EfficientFormerIntermediateStage(config, i))
            if downsamples[i]: intermediate_stages.append(EfficientFormerPatchEmbeddings(config, config.hidden_sizes[i], config.hidden_sizes[i + 1]))
        self.intermediate_stages = nn.ModuleList(intermediate_stages)
        self.last_stage = EfficientFormerLastStage(config)
    def forward(self, hidden_states: torch.Tensor, output_hidden_states: bool = False, output_attentions: bool = False, return_dict: bool = True) -> BaseModelOutput:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        for layer_module in self.intermediate_stages:
            hidden_states = layer_module(hidden_states)
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        layer_output = self.last_stage(hidden_states, output_attentions=output_attentions)
        if output_attentions: all_self_attentions = all_self_attentions + layer_output[1:]
        if output_hidden_states: all_hidden_states = all_hidden_states + (layer_output[0],)
        if not return_dict: return tuple(v for v in [layer_output[0], all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=layer_output[0], hidden_states=all_hidden_states, attentions=all_self_attentions)
class EfficientFormerPreTrainedModel(PreTrainedModel):
    config_class = EfficientFormerConfig
    base_model_prefix = "efficientformer"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = False
    def _init_weights(self, module: nn.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.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
EFFICIENTFORMER_START_DOCSTRING = r"""
    This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#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 ([`EfficientFormerConfig`]): 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.
"""
EFFICIENTFORMER_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`ViTImageProcessor`]. See
            [`ViTImageProcessor.preprocess`] 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 EfficientFormer Model transformer outputting raw hidden-states without any specific head on top.", EFFICIENTFORMER_START_DOCSTRING)
class EfficientFormerModel(EfficientFormerPreTrainedModel):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__(config)
        self.config = config
        _no_split_modules = ["EfficientFormerMeta4D"]
        self.patch_embed = EfficientFormerConvStem(config, config.hidden_sizes[0])
        self.encoder = EfficientFormerEncoder(config)
        self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
        self.post_init()
    @add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = 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
        if pixel_values is None: raise ValueError("You have to specify pixel_values")
        embedding_output = self.patch_embed(pixel_values)
        encoder_outputs = self.encoder(embedding_output, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        if not return_dict:
            head_outputs = (sequence_output,)
            return head_outputs + encoder_outputs[1:]
        return BaseModelOutput(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
@add_start_docstrings("EfficientFormer 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.", EFFICIENTFORMER_START_DOCSTRING)
class EfficientFormerForImageClassification(EfficientFormerPreTrainedModel):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.efficientformer = EfficientFormerModel(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(EFFICIENTFORMER_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, 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.efficientformer(pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output.mean(-2))
        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)
@dataclass
class EfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
    """Args:"""
    logits: torch.FloatTensor = None
    cls_logits: torch.FloatTensor = None
    distillation_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
@add_start_docstrings("""
    EfficientFormer Model transformer with image classification heads on top (a linear layer on top of the final hidden
    state of the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for
    ImageNet.
    <Tip warning={true}>
           This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
           supported.
    </Tip>""", EFFICIENTFORMER_START_DOCSTRING)
class EfficientFormerForImageClassificationWithTeacher(EfficientFormerPreTrainedModel):
    def __init__(self, config: EfficientFormerConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.efficientformer = EfficientFormerModel(config)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
        self.distillation_classifier = (nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity())
        self.post_init()
    @add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[tuple, EfficientFormerForImageClassificationWithTeacherOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.efficientformer(pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        cls_logits = self.classifier(sequence_output.mean(-2))
        distillation_logits = self.distillation_classifier(sequence_output.mean(-2))
        logits = (cls_logits + distillation_logits) / 2
        if not return_dict:
            output = (logits, cls_logits, distillation_logits) + outputs[1:]
            return output
        return EfficientFormerForImageClassificationWithTeacherOutput(logits=logits, cls_logits=cls_logits, distillation_logits=distillation_logits, hidden_states=outputs.hidden_states, 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.                                                                 #
    #################################################################################################################################################################################################
"""
