"""
    #################################################################################################################################################################################################
    # 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 collections.abc
import math
from typing import Dict, List, Optional, Set, 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, ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, torch_int)
from .configuration_vit_msn import ViTMSNConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTMSNConfig"
_CHECKPOINT_FOR_DOC = "facebook/vit-msn-small"
class ViTMSNEmbeddings(nn.Module):
    def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False) -> None:
        super().__init__()
        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
        self.patch_embeddings = ViTMSNPatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.patch_size = config.patch_size
        self.config = config
    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width: return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]
        dim = embeddings.shape[-1]
        new_height = height // self.patch_size
        new_width = width // self.patch_size
        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_height, new_width), mode="bicubic", align_corners=False)
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        if bool_masked_pos is not None:
            seq_length = embeddings.shape[1]
            mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
        if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else: embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings
class ViTMSNPatchEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels: raise ValueError(f"Make sure that the channel dimension of the pixel values match with the one set in the configuration. Expected {self.num_channels} but got {num_channels}.")
        if not interpolate_pos_encoding:
            if height != self.image_size[0] or width != self.image_size[1]: raise ValueError(f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}).")
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings
class ViTMSNSelfAttention(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size {config.hidden_size,} is not a multiple of the number of attention heads {config.num_attention_heads}.")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        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)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        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 ViTMSNSdpaSelfAttention(ViTMSNSelfAttention):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__(config)
        self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
    def forward(self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, head_mask, self.attention_probs_dropout_prob if self.training else 0.0, is_causal=False, scale=None)
        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)
        return context_layer, None
class ViTMSNSelfOutput(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
class ViTMSNAttention(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.attention = ViTMSNSelfAttention(config)
        self.output = ViTMSNSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads: Set[int]) -> None:
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads)
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_outputs = self.attention(hidden_states, head_mask, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class ViTMSNSdpaAttention(ViTMSNAttention):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__(config)
        self.attention = ViTMSNSdpaSelfAttention(config)
class ViTMSNIntermediate(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class ViTMSNOutput(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = hidden_states + input_tensor
        return hidden_states
VITMSN_ATTENTION_CLASSES = {"eager": ViTMSNAttention, "sdpa": ViTMSNSdpaAttention}
class ViTMSNLayer(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = VITMSN_ATTENTION_CLASSES[config._attn_implementation](config)
        self.intermediate = ViTMSNIntermediate(config)
        self.output = ViTMSNOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_attention_outputs = self.attention(self.layernorm_before(hidden_states), head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        hidden_states = attention_output + hidden_states
        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)
        layer_output = self.output(layer_output, hidden_states)
        outputs = (layer_output,) + outputs
        return outputs
class ViTMSNEncoder(nn.Module):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([ViTMSNLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False,
    return_dict: bool = True) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, layer_head_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        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 ViTMSNPreTrainedModel(PreTrainedModel):
    config_class = ViTMSNConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ViTMSNAttention", "ViTMSNSdpaAttention"]
    _supports_sdpa = True
    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        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)
VIT_MSN_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 ([`ViTMSNConfig`]): 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.
"""
VIT_MSN_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 [`ViTImageProcessor.__call__`]
            for details.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        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.
        interpolate_pos_encoding (`bool`, *optional*):
            Whether to interpolate the pre-trained position encodings.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare ViTMSN Model outputting raw hidden-states without any specific head on top.", VIT_MSN_START_DOCSTRING)
class ViTMSNModel(ViTMSNPreTrainedModel):
    def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False):
        super().__init__(config)
        self.config = config
        self.embeddings = ViTMSNEmbeddings(config, use_mask_token=use_mask_token)
        self.encoder = ViTMSNEncoder(config)
        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.post_init()
    def get_input_embeddings(self) -> ViTMSNPatchEmbeddings: return self.embeddings.patch_embeddings
    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: 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")
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding)
        encoder_outputs = self.encoder(embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        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("ViTMSN Model with an image classification head on top e.g. for ImageNet.", VIT_MSN_START_DOCSTRING)
class ViTMSNForImageClassification(ViTMSNPreTrainedModel):
    def __init__(self, config: ViTMSNConfig) -> None:
        super().__init__(config)
        self.num_labels = config.num_labels
        self.vit = ViTMSNModel(config)
        self.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(VIT_MSN_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: 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.vit(pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output[:, 0, :])
        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)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
