"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
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, 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, replace_return_docstrings)
from ...utils.backbone_utils import BackboneMixin
from .configuration_convnext import ConvNextConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ConvNextConfig"
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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 ConvNextDropPath(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 ConvNextLayerNorm(nn.Module):
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]: raise NotImplementedError(f"Unsupported data format: {self.data_format}")
        self.normalized_shape = (normalized_shape,)
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.data_format == "channels_last": x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            input_dtype = x.dtype
            x = x.float()
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = x.to(dtype=input_dtype)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x
class ConvNextEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.patch_embeddings = nn.Conv2d(config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size)
        self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first")
        self.num_channels = config.num_channels
    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        num_channels = pixel_values.shape[1]
        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.patch_embeddings(pixel_values)
        embeddings = self.layernorm(embeddings)
        return embeddings
class ConvNextLayer(nn.Module):
    def __init__(self, config, dim, drop_path=0):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
        self.layernorm = ConvNextLayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.act = ACT2FN[config.hidden_act]
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.layer_scale_parameter = (nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True) if config.layer_scale_init_value > 0 else None)
        self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        input = hidden_states
        x = self.dwconv(hidden_states)
        x = x.permute(0, 2, 3, 1)
        x = self.layernorm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.layer_scale_parameter is not None: x = self.layer_scale_parameter * x
        x = x.permute(0, 3, 1, 2)
        x = input + self.drop_path(x)
        return x
class ConvNextStage(nn.Module):
    def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None):
        super().__init__()
        if in_channels != out_channels or stride > 1: self.downsampling_layer = nn.Sequential(ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"), nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride))
        else: self.downsampling_layer = nn.Identity()
        drop_path_rates = drop_path_rates or [0.0] * depth
        self.layers = nn.Sequential(*[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)])
    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        hidden_states = self.downsampling_layer(hidden_states)
        hidden_states = self.layers(hidden_states)
        return hidden_states
class ConvNextEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.stages = nn.ModuleList()
        drop_path_rates = [x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)]
        prev_chs = config.hidden_sizes[0]
        for i in range(config.num_stages):
            out_chs = config.hidden_sizes[i]
            stage = ConvNextStage(config, in_channels=prev_chs, out_channels=out_chs, stride=2 if i > 0 else 1, depth=config.depths[i], drop_path_rates=drop_path_rates[i])
            self.stages.append(stage)
            prev_chs = out_chs
    def forward(self, hidden_states: torch.FloatTensor, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple, BaseModelOutputWithNoAttention]:
        all_hidden_states = () if output_hidden_states else None
        for i, layer_module in enumerate(self.stages):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            hidden_states = layer_module(hidden_states)
        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] if v is not None)
        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
class ConvNextPreTrainedModel(PreTrainedModel):
    config_class = ConvNextConfig
    base_model_prefix = "convnext"
    main_input_name = "pixel_values"
    _no_split_modules = ["ConvNextLayer"]
    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)
CONVNEXT_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 ([`ConvNextConfig`]): 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.
"""
CONVNEXT_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
            [`ConvNextImageProcessor.__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.
"""
@add_start_docstrings("The bare ConvNext model outputting raw features without any specific head on top.", CONVNEXT_START_DOCSTRING)
class ConvNextModel(ConvNextPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.embeddings = ConvNextEmbeddings(config)
        self.encoder = ConvNextEncoder(config)
        self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
        self.post_init()
    @add_start_docstrings_to_model_forward(CONVNEXT_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.layernorm(last_hidden_state.mean([-2, -1]))
        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("ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.", CONVNEXT_START_DOCSTRING)
class ConvNextForImageClassification(ConvNextPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.convnext = ConvNextModel(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(CONVNEXT_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.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs.pooler_output if return_dict else outputs[1]
        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)
@add_start_docstrings("ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer.", CONVNEXT_START_DOCSTRING)
class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
    def __init__(self, config):
        super().__init__(config)
        super()._init_backbone(config)
        self.embeddings = ConvNextEmbeddings(config)
        self.encoder = ConvNextEncoder(config)
        self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
        hidden_states_norms = {}
        for stage, num_channels in zip(self._out_features, self.channels): hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first")
        self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
        self.post_init()
    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
    def forward(self, pixel_values: torch.Tensor, 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)
        embedding_output = self.embeddings(pixel_values)
        outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=return_dict)
        hidden_states = outputs.hidden_states if return_dict else outputs[1]
        feature_maps = ()
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                hidden_state = self.hidden_states_norms[stage](hidden_state)
                feature_maps += (hidden_state,)
        if not return_dict:
            output = (feature_maps,)
            if output_hidden_states: output += (hidden_states,)
            return output
        return BackboneOutput(feature_maps=feature_maps, hidden_states=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.                                                                 #
    #################################################################################################################################################################################################
"""
