"""
    #################################################################################################################################################################################################
    # 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 ...cache_utils import Cache, StaticCache
from ...generation import GenerationMixin
from ...modeling_utils import PreTrainedModel
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, logging, replace_return_docstrings)
from .configuration_paligemma import PaliGemmaConfig
if is_flash_attn_2_available(): from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
from ..auto import AutoModel, AutoModelForCausalLM
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "PaliGemmaConfig"
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device,
min_dtype: float, cache_position: torch.Tensor, batch_size: int, is_training: bool, token_type_ids: torch.Tensor):
    if attention_mask is not None and attention_mask.dim() == 4: causal_mask = attention_mask
    else:
        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
        if sequence_length != 1:
            if is_training: causal_mask = torch.triu(causal_mask, diagonal=1)
            else: causal_mask = torch.zeros_like(causal_mask)
        causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
        if attention_mask is not None:
            causal_mask = causal_mask.clone()
            mask_length = attention_mask.shape[-1]
            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
            padding_mask = padding_mask == 0
            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
            if is_training: causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0)
    return causal_mask
@dataclass
class PaliGemmaCausalLMOutputWithPast(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[torch.FloatTensor] = None
class PaliGemmaMultiModalProjector(nn.Module):
    def __init__(self, config: PaliGemmaConfig):
        super().__init__()
        self.linear = nn.Linear(config.vision_config.hidden_size, config.vision_config.projection_dim, bias=True)
    def forward(self, image_features):
        hidden_states = self.linear(image_features)
        return hidden_states
PALIGEMMA_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 ([`PaliGemmaConfig`] or [`PaliGemmaVisionConfig`]):
            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 LLaMA Model outputting raw hidden-states without any specific head on top.", PALIGEMMA_START_DOCSTRING)
class PaliGemmaPreTrainedModel(PreTrainedModel):
    config_class = PaliGemmaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["PaliGemmaMultiModalProjector"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = False
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_sdpa = 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
PALIGEMMA_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 [`SiglipImageProcessor.__call__`] for details ([]`PaliGemmaProcessor`] uses
            [`SiglipImageProcessor`] 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 PALIGEMMA model which consists of a vision backbone and a language model.", PALIGEMMA_START_DOCSTRING)
class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixin):
    def __init__(self, config: PaliGemmaConfig):
        super().__init__(config)
        self.vision_tower = AutoModel.from_config(config=config.vision_config)
        self.multi_modal_projector = PaliGemmaMultiModalProjector(config)
        self.vocab_size = config.text_config.vocab_size
        self._attn_implementation = config._attn_implementation
        language_model = AutoModelForCausalLM.from_config(config=config.text_config, attn_implementation=self._attn_implementation)
        if language_model._tied_weights_keys is not None: self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys]
        self.language_model = language_model
        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 _update_causal_mask(self, attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training: bool = False):
        using_static_cache = isinstance(past_key_values, StaticCache)
        dtype = inputs_embeds.dtype
        min_dtype = torch.finfo(dtype).min
        sequence_length = inputs_embeds.shape[1]
        if using_static_cache: target_length = past_key_values.get_max_length()
        else: target_length = (attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else cache_position[0] + sequence_length + 1)
        if attention_mask is not None and attention_mask.dim() == 4: return attention_mask
        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device)
        if sequence_length != 1:
            if is_training: causal_mask = torch.triu(causal_mask, diagonal=1)
            else: causal_mask = torch.zeros_like(causal_mask)
        causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
        causal_mask = causal_mask[None, None, :, :].expand(inputs_embeds.shape[0], 1, -1, -1)
        if attention_mask is not None:
            causal_mask = causal_mask.clone()
            mask_length = attention_mask.shape[-1]
            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
            padding_mask = padding_mask == 0
            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
            if is_training: causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0)
        return causal_mask
    @add_start_docstrings_to_model_forward(PALIGEMMA_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[Union[List[torch.FloatTensor], Cache]] = None, token_type_ids: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = 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, num_logits_to_keep: int = 0) -> Union[Tuple, PaliGemmaCausalLMOutputWithPast]:
        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")
        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
        is_training = token_type_ids is not None and labels is not None
        if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids)
        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device)
        if position_ids is None: position_ids = cache_position.unsqueeze(0) + 1
        if pixel_values is not None:
            image_outputs = self.vision_tower(pixel_values.to(inputs_embeds.dtype))
            selected_image_feature = image_outputs.last_hidden_state
            image_features = self.multi_modal_projector(selected_image_feature)
            image_features = image_features / (self.config.hidden_size**0.5)
            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
            if inputs_embeds[special_image_mask].numel() != image_features.numel():
                image_tokens_in_text = torch.sum(input_ids == self.config.image_token_index)
                raise ValueError(f"Number of images does not match number of special image tokens in the input text. Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} tokens from image embeddings.")
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
        if labels is not None and self.pad_token_id in labels:
            logger.warning_once("`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. ", "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.")
            labels = torch.where(input_ids == self.pad_token_id, self.config.ignore_index, labels)
        causal_mask = self._update_causal_mask(attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training)
        outputs = self.language_model(attention_mask=causal_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.logits
        logits = logits.float()
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            if attention_mask is not None:
                shift_attention_mask = attention_mask[..., 1:]
                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
            else:
                shift_logits = shift_logits.contiguous()
                shift_labels = shift_labels.contiguous()
            loss_fct = nn.CrossEntropyLoss()
            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
            flat_labels = shift_labels.view(-1).to(shift_logits.device)
            loss = loss_fct(flat_logits, flat_labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return PaliGemmaCausalLMOutputWithPast(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, cache_position=None, position_ids=None, pixel_values=None, attention_mask=None,
    token_type_ids=None, use_cache=True, num_logits_to_keep=None, **kwargs):
        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,
        position_ids=position_ids, cache_position=cache_position, use_cache=use_cache, num_logits_to_keep=num_logits_to_keep, **kwargs)
        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
            if model_inputs["inputs_embeds"] is not None:
                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
                device = model_inputs["inputs_embeds"].device
            else:
                batch_size, sequence_length = model_inputs["input_ids"].shape
                device = model_inputs["input_ids"].device
            dtype = self.get_output_embeddings().weight.dtype
            min_dtype = torch.finfo(dtype).min
            is_training = token_type_ids is not None and kwargs.get("labels", None) is not None
            model_inputs["attention_mask"] = _prepare_4d_causal_attention_mask_with_cache_position(attention_mask, sequence_length=sequence_length, target_length=past_key_values.get_max_length(),
            dtype=dtype, device=device, min_dtype=min_dtype, cache_position=cache_position, batch_size=batch_size, is_training=is_training, token_type_ids=token_type_ids)
        model_inputs["token_type_ids"] = token_type_ids
        if model_inputs.get("position_ids") is not None: model_inputs["position_ids"] += 1
        if 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.                                                                 #
    #################################################################################################################################################################################################
"""
