"""
    #################################################################################################################################################################################################
    # 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 dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import ModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_vipllava import VipLlavaConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VipLlavaConfig"
@dataclass
class VipLlavaCausalLMOutputWithPast(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[torch.FloatTensor] = None
class VipLlavaMultiModalProjector(nn.Module):
    def __init__(self, config: VipLlavaConfig):
        super().__init__()
        self.projector_layernorm = nn.LayerNorm(len(config.vision_feature_layers) * config.vision_config.hidden_size, eps=config.projector_layernorm_eps)
        self.linear_1 = nn.Linear(len(config.vision_feature_layers) * config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
        self.act = ACT2FN[config.projector_hidden_act]
        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
    def forward(self, hidden_states):
        hidden_states = self.projector_layernorm(hidden_states)
        hidden_states = self.linear_1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states
VIPLLAVA_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 ([`VipLlavaConfig`] or [`VipLlavaVisionConfig`]):
            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.
"""
@add_start_docstrings("The bare VipLlava Model outputting raw hidden-states without any specific head on top.", VIPLLAVA_START_DOCSTRING)
class VipLlavaPreTrainedModel(PreTrainedModel):
    config_class = VipLlavaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["VipLlavaVisionAttention"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_cache_class = True
    def _init_weights(self, module):
        std = (self.config.initializer_range if hasattr(self.config, "initializer_range") else self.config.text_config.initializer_range)
        if hasattr(module, "class_embedding"): module.class_embedding.data.normal_(mean=0.0, std=std)
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
    @property
    def _supports_sdpa(self): return self.language_model._supports_sdpa
VIPLLAVA_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)
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
            The tensors corresponding to the input images. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
            [`CLIPImageProcessor`] for processing images).
        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)
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        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.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""
@add_start_docstrings("The VIPLLAVA model which consists of a vision backbone and a language model.", VIPLLAVA_START_DOCSTRING)
class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin):
    def __init__(self, config: VipLlavaConfig):
        super().__init__(config)
        self.vision_tower = AutoModel.from_config(config.vision_config)
        self.multi_modal_projector = VipLlavaMultiModalProjector(config)
        self.vocab_size = config.text_config.vocab_size
        self.language_model = AutoModelForCausalLM.from_config(config.text_config, attn_implementation=config._attn_implementation)
        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
        self.post_init()
    def get_input_embeddings(self): return self.language_model.get_input_embeddings()
    def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value)
    def get_output_embeddings(self): return self.language_model.get_output_embeddings()
    def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings)
    def set_decoder(self, decoder): self.language_model.set_decoder(decoder)
    def get_decoder(self): return self.language_model.get_decoder()
    def tie_weights(self): return self.language_model.tie_weights()
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        self.config.text_config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds
    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
        num_images, num_image_patches, embed_dim = image_features.shape
        batch_size, sequence_length = input_ids.shape
        left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
        special_image_token_mask = input_ids == self.config.image_token_index
        num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
        max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
        batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
        new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
        nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
        if left_padding: new_token_positions += nb_image_pad[:, None]
        text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
        final_embedding = torch.zeros(batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device)
        final_attention_mask = torch.zeros(batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device)
        if labels is not None: final_labels = torch.full((batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device)
        target_device = inputs_embeds.device
        batch_indices, non_image_indices, text_to_overwrite = (batch_indices.to(target_device), non_image_indices.to(target_device), text_to_overwrite.to(target_device))
        attention_mask = attention_mask.to(target_device)
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
        if labels is not None: final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
        image_to_overwrite = torch.full((batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device)
        image_to_overwrite[batch_indices, text_to_overwrite] = False
        image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
        if image_to_overwrite.sum() != image_features.shape[:-1].numel(): raise ValueError(f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation.")
        final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
        final_attention_mask |= image_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
        batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
        indices_to_mask = new_token_positions[batch_indices, pad_indices]
        final_embedding[batch_indices, indices_to_mask] = 0
        if labels is None: final_labels = None
        return final_embedding, final_attention_mask, final_labels, position_ids
    @add_start_docstrings_to_model_forward(VIPLLAVA_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layers: Optional[List[int]] = None,
    labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, num_logits_to_keep: int = 0) -> Union[Tuple, VipLlavaCausalLMOutputWithPast]:
        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_feature_layers = (vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers)
        if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one")
        if pixel_values is not None and inputs_embeds is not None: raise ValueError("You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one")
        legacy_processing = False
        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)
            legacy_processing = ((input_ids == self.config.image_token_index).sum(1).max() < self.config.image_seq_length) or (input_ids.shape[-1] == 1 and pixel_values is not None)
        if pixel_values is not None:
            image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
            image_features = [image_outputs.hidden_states[index][:, 1:] for index in vision_feature_layers]
            image_features = torch.cat(image_features, dim=-1)
            image_features = self.multi_modal_projector(image_features)
            if legacy_processing:
                logger.warning_once("Expanding inputs for image tokens in VipLLaVa should be done in processing. Please add `patch_size` and `vision_feature_select_strategy` to the model's image processing config. Using processors without these attributes in the config is deprecated and will throw an error in v1.0.")
                if input_ids.shape[1] != 1:
                    inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(image_features, inputs_embeds, input_ids, attention_mask, labels)
                    cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)
                else:
                    first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
                    batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
                    target_length = input_ids.shape[1]
                    past_length = first_layer_past_key_value.shape[-1]
                    extended_attention_mask = torch.ones((attention_mask.shape[0], past_length), dtype=attention_mask.dtype, device=attention_mask.device)
                    valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                    new_batch_index = batch_index[valid_indices]
                    new_non_attended_tokens = non_attended_tokens[valid_indices]
                    extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
                    attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
                    position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
                    cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)[-target_length:]
            else:
                special_image_mask = ((input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds))
                image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
        outputs = self.language_model(attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, num_logits_to_keep=num_logits_to_keep)
        logits = outputs[0]
        loss = None
        if labels is not None:
            if attention_mask is not None:
                shift_attention_mask = attention_mask[..., 1:]
                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
            else:
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return VipLlavaCausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=image_features if pixel_values is not None else None)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, cache_position=None, num_logits_to_keep=None, **kwargs):
        legacy_processing = (input_ids is not None and (input_ids == self.config.image_token_index).sum(1).max() < self.config.image_seq_length)
        model_inputs = self.language_model.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
        cache_position=cache_position, num_logits_to_keep=num_logits_to_keep, **kwargs)
        if legacy_processing or cache_position[0] == 0: model_inputs["pixel_values"] = pixel_values
        return model_inputs
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
