"""
    #################################################################################################################################################################################################
    # 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 dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutputWithNoAttention, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, BaseModelOutputWithPoolingAndNoAttention)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "kakaobrain/align-base"
_CONFIG_FOR_DOC = "AlignConfig"
ALIGN_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also 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 ([`AlignConfig`]): 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.
"""
ALIGN_TEXT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        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*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        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.
"""
ALIGN_VISION_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__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.
"""
ALIGN_INPUTS_DOCSTRING = r"""
    Args:
       input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        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*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details.
        return_loss (`bool`, *optional*):
            Whether or not to return the contrastive loss.
        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.
"""
@dataclass
class AlignVisionModelOutput(ModelOutput):
    """Args:"""
    image_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class AlignTextModelOutput(ModelOutput):
    """Args:"""
    text_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class AlignOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits_per_image: torch.FloatTensor = None
    logits_per_text: torch.FloatTensor = None
    text_embeds: torch.FloatTensor = None
    image_embeds: torch.FloatTensor = None
    text_model_output: BaseModelOutputWithPoolingAndCrossAttentions = None
    vision_model_output: BaseModelOutputWithPoolingAndNoAttention = None
    def to_tuple(self) -> Tuple[Any]: return tuple(self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys())
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1)
def align_loss(similarity: torch.Tensor) -> torch.Tensor:
    caption_loss = contrastive_loss(similarity)
    image_loss = contrastive_loss(similarity.t())
    return (caption_loss + image_loss) / 2.0
def round_filters(config: AlignVisionConfig, 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 AlignVisionEmbeddings(nn.Module):
    def __init__(self, config: AlignVisionConfig):
        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 AlignVisionDepthwiseConv2d(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 AlignVisionExpansionLayer(nn.Module):
    def __init__(self, config: AlignVisionConfig, 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 AlignVisionDepthwiseLayer(nn.Module):
    def __init__(self, config: AlignVisionConfig, 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 = AlignVisionDepthwiseConv2d(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 AlignVisionSqueezeExciteLayer(nn.Module):
    def __init__(self, config: AlignVisionConfig, 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 AlignVisionFinalBlockLayer(nn.Module):
    def __init__(self, config: AlignVisionConfig, 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 AlignVisionBlock(nn.Module):
    def __init__(self, config: AlignVisionConfig, 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 = AlignVisionExpansionLayer(config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride)
        self.depthwise_conv = AlignVisionDepthwiseLayer(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 = AlignVisionSqueezeExciteLayer(config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand)
        self.projection = AlignVisionFinalBlockLayer(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 AlignVisionEncoder(nn.Module):
    def __init__(self, config: AlignVisionConfig):
        super().__init__()
        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 = AlignVisionBlock(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)
    def forward(self, hidden_states: torch.FloatTensor, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> BaseModelOutputWithPoolingAndNoAttention:
        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,)
        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 AlignTextEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
        self.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values_length: int = 0) -> torch.Tensor:
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class AlignTextSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=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)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
        self.is_decoder = config.is_decoder
    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: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention and past_key_value is not None:
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            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)
        use_cache = past_key_value is not None
        if self.is_decoder: past_key_value = (key_layer, value_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        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,)
        if self.is_decoder: outputs = outputs + (past_key_value,)
        return outputs
class AlignTextSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        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 = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
ALIGN_TEXT_SELF_ATTENTION_CLASSES = {"eager": AlignTextSelfAttention}
class AlignTextAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = ALIGN_TEXT_SELF_ATTENTION_CLASSES[config._attn_implementation](config, position_embedding_type=position_embedding_type)
        self.output = AlignTextSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_outputs = self.self(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class AlignTextIntermediate(nn.Module):
    def __init__(self, config):
        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 AlignTextOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        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 = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class AlignTextLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = AlignTextAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = AlignTextAttention(config, position_embedding_type="absolute")
        self.intermediate = AlignTextIntermediate(config)
        self.output = AlignTextOutput(config)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor]:
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value)
        attention_output = self_attention_outputs[0]
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else: outputs = self_attention_outputs[1:]
        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"): raise ValueError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions)
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        if self.is_decoder: outputs = outputs + (present_key_value,)
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class AlignTextEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([AlignTextLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False,
    output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False
        next_decoder_cache = () if use_cache 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
            past_key_value = past_key_values[i] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions)
class AlignTextPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class AlignPreTrainedModel(PreTrainedModel):
    config_class = AlignConfig
    base_model_prefix = "align"
    supports_gradient_checkpointing = True
    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, AlignModel):
            nn.init.xavier_uniform_(module.text_projection.weight)
            module.text_projection.bias.data.zero_()
            module.text_projection._is_hf_initialized = True
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        if isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
@add_start_docstrings("The text model from ALIGN without any head or projection on top.", ALIGN_START_DOCSTRING)
class AlignTextModel(AlignPreTrainedModel):
    config_class = AlignTextConfig
    _no_split_modules = ["AlignTextEmbeddings"]
    def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.config = config
        self.embeddings = AlignTextEmbeddings(config)
        self.encoder = AlignTextEncoder(config)
        self.pooler = AlignTextPooler(config) if add_pooling_layer else None
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
        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 input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device)
        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        encoder_outputs = self.encoder(embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions)
@add_start_docstrings("The vision model from ALIGN without any head or projection on top.", ALIGN_START_DOCSTRING)
class AlignVisionModel(AlignPreTrainedModel):
    config_class = AlignVisionConfig
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = False
    def __init__(self, config: AlignVisionConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = AlignVisionEmbeddings(config)
        self.encoder = AlignVisionEncoder(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()
    def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.convolution
    @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[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(ALIGN_START_DOCSTRING)
class AlignModel(AlignPreTrainedModel):
    config_class = AlignConfig
    def __init__(self, config: AlignConfig):
        super().__init__(config)
        if not isinstance(config.text_config, AlignTextConfig): raise TypeError(f"config.text_config is expected to be of type AlignTextConfig but is of type {type(config.text_config)}.")
        if not isinstance(config.vision_config, AlignVisionConfig): raise TypeError(f"config.vision_config is expected to be of type AlignVisionConfig but is of type {type(config.vision_config)}.")
        text_config = config.text_config
        vision_config = config.vision_config
        self.projection_dim = config.projection_dim
        self.text_embed_dim = text_config.hidden_size
        self.text_model = AlignTextModel(text_config)
        self.vision_model = AlignVisionModel(vision_config)
        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim)
        self.temperature = nn.Parameter(torch.tensor(self.config.temperature_init_value))
        self.post_init()
    @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
    def get_text_features(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> torch.FloatTensor:
        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
        text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        last_hidden_state = text_outputs[0][:, 0, :]
        text_features = self.text_projection(last_hidden_state)
        return text_features
    @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
    def get_image_features(self, pixel_values: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> torch.FloatTensor:
        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
        vision_outputs = self.vision_model(pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        image_features = vision_outputs[1]
        return image_features
    @add_start_docstrings_to_model_forward(ALIGN_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, AlignOutput]:
        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
        vision_outputs = self.vision_model(pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        image_embeds = vision_outputs[1]
        text_embeds = text_outputs[0][:, 0, :]
        text_embeds = self.text_projection(text_embeds)
        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) / self.temperature
        logits_per_image = logits_per_text.t()
        loss = None
        if return_loss: loss = align_loss(logits_per_text)
        if not return_dict:
            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
            return ((loss,) + output) if loss is not None else output
        return AlignOutput(loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs)
__all__ = ["AlignPreTrainedModel", "AlignTextModel", "AlignVisionModel", "AlignModel"]
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
