"""
    #################################################################################################################################################################################################
    # 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 (BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging)
from .configuration_efficientnet import EfficientNetConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "EfficientNetConfig"
_CHECKPOINT_FOR_DOC = "google/efficientnet-b7"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "google/efficientnet-b7"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
EFFICIENTNET_START_DOCSTRING = r"""
    This model is 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 ([`EfficientNetConfig`]): 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.
"""
EFFICIENTNET_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
            [`AutoImageProcessor.__call__`] for details.
        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.
"""
def round_filters(config: EfficientNetConfig, num_channels: int):
    divisor = config.depth_divisor
    num_channels *= config.width_coefficient
    new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
    if new_dim < 0.9 * num_channels: new_dim += divisor
    return int(new_dim)
def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
    if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size)
    correct = (kernel_size[0] // 2, kernel_size[1] // 2)
    if adjust: return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
    else: return (correct[1], correct[1], correct[0], correct[0])
class EfficientNetEmbeddings(nn.Module):
    def __init__(self, config: EfficientNetConfig):
        super().__init__()
        self.out_dim = round_filters(config, 32)
        self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
        self.convolution = nn.Conv2d(config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False)
        self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
        self.activation = ACT2FN[config.hidden_act]
    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        features = self.padding(pixel_values)
        features = self.convolution(features)
        features = self.batchnorm(features)
        features = self.activation(features)
        return features
class EfficientNetDepthwiseConv2d(nn.Conv2d):
    def __init__(self, in_channels, depth_multiplier=1, kernel_size=3, stride=1, padding=0, dilation=1, bias=True, padding_mode="zeros"):
        out_channels = in_channels * depth_multiplier
        super().__init__(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation,
        groups=in_channels, bias=bias, padding_mode=padding_mode)
class EfficientNetExpansionLayer(nn.Module):
    def __init__(self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int):
        super().__init__()
        self.expand_conv = nn.Conv2d(in_channels=in_dim, out_channels=out_dim, kernel_size=1, padding="same", bias=False)
        self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
        self.expand_act = ACT2FN[config.hidden_act]
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        hidden_states = self.expand_conv(hidden_states)
        hidden_states = self.expand_bn(hidden_states)
        hidden_states = self.expand_act(hidden_states)
        return hidden_states
class EfficientNetDepthwiseLayer(nn.Module):
    def __init__(self, config: EfficientNetConfig, in_dim: int, stride: int, kernel_size: int, adjust_padding: bool):
        super().__init__()
        self.stride = stride
        conv_pad = "valid" if self.stride == 2 else "same"
        padding = correct_pad(kernel_size, adjust=adjust_padding)
        self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
        self.depthwise_conv = EfficientNetDepthwiseConv2d(in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False)
        self.depthwise_norm = nn.BatchNorm2d(num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
        self.depthwise_act = ACT2FN[config.hidden_act]
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        if self.stride == 2: hidden_states = self.depthwise_conv_pad(hidden_states)
        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.depthwise_norm(hidden_states)
        hidden_states = self.depthwise_act(hidden_states)
        return hidden_states
class EfficientNetSqueezeExciteLayer(nn.Module):
    def __init__(self, config: EfficientNetConfig, in_dim: int, expand_dim: int, expand: bool = False):
        super().__init__()
        self.dim = expand_dim if expand else in_dim
        self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))
        self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
        self.reduce = nn.Conv2d(in_channels=self.dim, out_channels=self.dim_se, kernel_size=1, padding="same")
        self.expand = nn.Conv2d(in_channels=self.dim_se, out_channels=self.dim, kernel_size=1, padding="same")
        self.act_reduce = ACT2FN[config.hidden_act]
        self.act_expand = nn.Sigmoid()
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        inputs = hidden_states
        hidden_states = self.squeeze(hidden_states)
        hidden_states = self.reduce(hidden_states)
        hidden_states = self.act_reduce(hidden_states)
        hidden_states = self.expand(hidden_states)
        hidden_states = self.act_expand(hidden_states)
        hidden_states = torch.mul(inputs, hidden_states)
        return hidden_states
class EfficientNetFinalBlockLayer(nn.Module):
    def __init__(self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool):
        super().__init__()
        self.apply_dropout = stride == 1 and not id_skip
        self.project_conv = nn.Conv2d(in_channels=in_dim, out_channels=out_dim, kernel_size=1, padding="same", bias=False)
        self.project_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
        self.dropout = nn.Dropout(p=drop_rate)
    def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
        hidden_states = self.project_conv(hidden_states)
        hidden_states = self.project_bn(hidden_states)
        if self.apply_dropout:
            hidden_states = self.dropout(hidden_states)
            hidden_states = hidden_states + embeddings
        return hidden_states
class EfficientNetBlock(nn.Module):
    def __init__(self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int, expand_ratio: int, kernel_size: int, drop_rate: float, id_skip: bool, adjust_padding: bool):
        super().__init__()
        self.expand_ratio = expand_ratio
        self.expand = True if self.expand_ratio != 1 else False
        expand_in_dim = in_dim * expand_ratio
        if self.expand: self.expansion = EfficientNetExpansionLayer(config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride)
        self.depthwise_conv = EfficientNetDepthwiseLayer(config=config, in_dim=expand_in_dim if self.expand else in_dim, stride=stride, kernel_size=kernel_size, adjust_padding=adjust_padding)
        self.squeeze_excite = EfficientNetSqueezeExciteLayer(config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand)
        self.projection = EfficientNetFinalBlockLayer(config=config, in_dim=expand_in_dim if self.expand else in_dim, out_dim=out_dim, stride=stride, drop_rate=drop_rate, id_skip=id_skip)
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        embeddings = hidden_states
        if self.expand_ratio != 1: hidden_states = self.expansion(hidden_states)
        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.squeeze_excite(hidden_states)
        hidden_states = self.projection(embeddings, hidden_states)
        return hidden_states
class EfficientNetEncoder(nn.Module):
    def __init__(self, config: EfficientNetConfig):
        super().__init__()
        self.config = config
        self.depth_coefficient = config.depth_coefficient
        def round_repeats(repeats): return int(math.ceil(self.depth_coefficient * repeats))
        num_base_blocks = len(config.in_channels)
        num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)
        curr_block_num = 0
        blocks = []
        for i in range(num_base_blocks):
            in_dim = round_filters(config, config.in_channels[i])
            out_dim = round_filters(config, config.out_channels[i])
            stride = config.strides[i]
            kernel_size = config.kernel_sizes[i]
            expand_ratio = config.expand_ratios[i]
            for j in range(round_repeats(config.num_block_repeats[i])):
                id_skip = True if j == 0 else False
                stride = 1 if j > 0 else stride
                in_dim = out_dim if j > 0 else in_dim
                adjust_padding = False if curr_block_num in config.depthwise_padding else True
                drop_rate = config.drop_connect_rate * curr_block_num / num_blocks
                block = EfficientNetBlock(config=config, in_dim=in_dim, out_dim=out_dim, stride=stride, kernel_size=kernel_size, expand_ratio=expand_ratio, drop_rate=drop_rate,
                id_skip=id_skip, adjust_padding=adjust_padding)
                blocks.append(block)
                curr_block_num += 1
        self.blocks = nn.ModuleList(blocks)
        self.top_conv = nn.Conv2d(in_channels=out_dim, out_channels=round_filters(config, 1280), kernel_size=1, padding="same", bias=False)
        self.top_bn = nn.BatchNorm2d(num_features=config.hidden_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
        self.top_activation = ACT2FN[config.hidden_act]
    def forward(self, hidden_states: torch.FloatTensor, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> BaseModelOutputWithNoAttention:
        all_hidden_states = (hidden_states,) if output_hidden_states else None
        for block in self.blocks:
            hidden_states = block(hidden_states)
            if output_hidden_states: all_hidden_states += (hidden_states,)
        hidden_states = self.top_conv(hidden_states)
        hidden_states = self.top_bn(hidden_states)
        hidden_states = self.top_activation(hidden_states)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
class EfficientNetPreTrainedModel(PreTrainedModel):
    config_class = EfficientNetConfig
    base_model_prefix = "efficientnet"
    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.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
@add_start_docstrings("The bare EfficientNet model outputting raw features without any specific head on top.", EFFICIENTNET_START_DOCSTRING)
class EfficientNetModel(EfficientNetPreTrainedModel):
    def __init__(self, config: EfficientNetConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = EfficientNetEmbeddings(config)
        self.encoder = EfficientNetEncoder(config)
        if config.pooling_type == "mean": self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
        elif config.pooling_type == "max": self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
        else: raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")
        self.post_init()
    @add_start_docstrings_to_model_forward(EFFICIENTNET_INPUTS_DOCSTRING)
    def forward(self, pixel_values: torch.FloatTensor = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
        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.embeddings(pixel_values)
        encoder_outputs = self.encoder(embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict)
        last_hidden_state = encoder_outputs[0]
        pooled_output = self.pooler(last_hidden_state)
        pooled_output = pooled_output.reshape(pooled_output.shape[:2])
        if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPoolingAndNoAttention(last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states)
@add_start_docstrings("EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.", EFFICIENTNET_START_DOCSTRING)
class EfficientNetForImageClassification(EfficientNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.efficientnet = EfficientNetModel(config)
        self.dropout = nn.Dropout(p=config.dropout_rate)
        self.classifier = nn.Linear(config.hidden_dim, self.num_labels) if self.num_labels > 0 else nn.Identity()
        self.post_init()
    @add_start_docstrings_to_model_forward(EFFICIENTNET_INPUTS_DOCSTRING)
    def forward(self, pixel_values: torch.FloatTensor = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.efficientnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs.pooler_output if return_dict else outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_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[2:]
            return ((loss,) + output) if loss is not None else output
        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
