"""
    #################################################################################################################################################################################################
    # 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 functools import cached_property
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...cache_utils import Cache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import _flash_attention_forward
from ...modeling_outputs import (BaseModelOutputWithPast, CausalLMOutputWithPast)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings)
from .configuration_chameleon import ChameleonConfig, ChameleonVQVAEConfig
if is_flash_attn_2_available(): from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
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):
    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: causal_mask = torch.triu(causal_mask, diagonal=1)
        causal_mask *= torch.arange(target_length, device=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, :]
            padding_mask = padding_mask == 0
            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
    return causal_mask
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ChameleonConfig"
_CHECKPOINT_FOR_DOC = "meta/chameleon-7b"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 4096]
_SEQ_CLASS_EXPECTED_LOSS = 1.03
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
class ChameleonRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)
    def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
ALL_LAYERNORM_LAYERS.append(ChameleonRMSNorm)
class ChameleonRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        super().__init__()
        self.scaling_factor = scaling_factor
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.max_seq_len_cached = max_position_embeddings
    @torch.no_grad()
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class ChameleonLinearScalingRotaryEmbedding(ChameleonRotaryEmbedding):
    def forward(self, x, position_ids):
        position_ids = position_ids.float() / self.scaling_factor
        cos, sin = super().forward(x, position_ids)
        return cos, sin
class ChameleonDynamicNTKScalingRotaryEmbedding(ChameleonRotaryEmbedding):
    def forward(self, x, position_ids):
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_position_embeddings:
            base = self.base * ((self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
            self.register_buffer("inv_freq", inv_freq, persistent=False)
        cos, sin = super().forward(x, position_ids)
        return cos, sin
def rotate_half(x):
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed
class ChameleonMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
        self.act_fn = ACT2FN[config.hidden_act]
    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj
class ChameleonLayerNorm(nn.LayerNorm):
    def __init__(self, hidden_size, *args, **kwargs):
        super().__init__(hidden_size, *args, **kwargs)
        self.normalized_shape = (hidden_size[-1],)
    def forward(self, hidden_states):
        hidden_states = F.layer_norm(hidden_states, self.normalized_shape, None, None, eps=1e-5)
        hidden_states = hidden_states * self.weight + self.bias
        return hidden_states
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1: return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class ChameleonAttention(nn.Module):
    def __init__(self, config: ChameleonConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None: logger.warning_once(f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.")
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.model_parallel_size = config.model_parallel_size
        if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`: {self.num_heads}).")
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
        self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim))
        self.k_norm = ChameleonLayerNorm((self.num_key_value_heads, self.head_dim))
        self._init_rope()
    def _init_rope(self):
        if self.config.rope_scaling is None: self.rotary_emb = ChameleonRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta)
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "linear": self.rotary_emb = ChameleonLinearScalingRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta)
            elif scaling_type == "dynamic": self.rotary_emb = ChameleonDynamicNTKScalingRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta)
            else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)
        query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
        query_states = self.q_norm(query_states)
        key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
        key_states = self.k_norm(key_states)
        query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)
        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}")
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        if not output_attentions: attn_weights = None
        return attn_output, attn_weights, past_key_value
class ChameleonFlashAttention2(ChameleonAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if isinstance(past_key_value, StaticCache): raise ValueError("`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` make sure to use `sdpa` in the mean time.")
        output_attentions = False
        bsz, q_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)
        query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
        query_states = self.q_norm(query_states)
        key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
        key_states = self.k_norm(key_states)
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        dropout_rate = self.attention_dropout if self.training else 0.0
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype()
            elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype
            else: target_dtype = self.q_proj.weight.dtype
            logger.warning_once(f"The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in {target_dtype}.")
            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)
        attn_output = _flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, sliding_window=getattr(self, "sliding_window", None),
        use_top_left_mask=self._flash_attn_uses_top_left_mask, is_causal=self.is_causal)
        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        if not output_attentions: attn_weights = None
        return attn_output, attn_weights, past_key_value
class ChameleonSdpaAttention(ChameleonAttention):
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            logger.warning_once("ChameleonModel is using ChameleonSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "+'but specifying the manual implementation will be required from sapiens_transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.')
            return super().forward(hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
        bsz, q_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)
        query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
        query_states = self.q_norm(query_states)
        key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
        key_states = self.k_norm(key_states)
        query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        causal_mask = attention_mask
        if attention_mask is not None and cache_position is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
        if query_states.device.type == "cuda" and causal_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()
        is_causal = True if causal_mask is None and q_len > 1 else False
        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        return attn_output, None, past_key_value
CHAMELEON_ATTENTION_CLASSES = {"eager": ChameleonAttention, "flash_attention_2": ChameleonFlashAttention2, "sdpa": ChameleonSdpaAttention}
class ChameleonDecoderLayer(nn.Module):
    def __init__(self, config: ChameleonConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
        self.mlp = ChameleonMLP(config)
        self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value,
        output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights,)
        if use_cache: outputs += (present_key_value,)
        return outputs
class ChameleonSwinDecoderLayer(nn.Module):
    def __init__(self, config: ChameleonConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
        self.mlp = ChameleonMLP(config)
        self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids,
        past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs)
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights,)
        if use_cache: outputs += (present_key_value,)
        return outputs
class ChameleonVQVAEVectorQuantizer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_embeddings = config.num_embeddings
        self.embedding_dim = config.embed_dim
        self.beta = getattr(config, "beta", 0.25)
        self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim)
        self.re_embed = self.num_embeddings
    def forward(self, hidden_state: torch.Tensor):
        hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
        hidden_state_flattened = hidden_state.view(-1, self.embedding_dim)
        distances = (torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) + torch.sum(self.embedding.weight**2, dim=1) - 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, self.embedding.weight.transpose(0, 1)))
        min_encoding_indices = torch.argmin(distances, dim=1)
        hidden_state_quant = self.embedding(min_encoding_indices).view(hidden_state.shape)
        loss = torch.mean((hidden_state_quant.detach() - hidden_state) ** 2) + self.beta * torch.mean((hidden_state_quant - hidden_state.detach()) ** 2)
        hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach()
        hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous()
        return hidden_state_quant, loss, min_encoding_indices
class ChameleonVQVAEEncoderConvDownsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
    def forward(self, hidden_states):
        hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0)
        hidden_states = self.conv(hidden_states)
        return hidden_states
class ChameleonVQVAEEncoderResnetBlock(nn.Module):
    def __init__(self, config, in_channels, out_channels=None, conv_shortcut=False):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.use_conv_shortcut = conv_shortcut
        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
        self.dropout = torch.nn.Dropout(config.dropout)
        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
            else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
    def forward(self, hidden_states):
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.norm2(hidden_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut: residual = self.conv_shortcut(residual)
            else: residual = self.nin_shortcut(residual)
        return residual + hidden_states
class ChameleonVQVAEEncoderAttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
    def forward(self, hidden_states):
        residual = hidden_states
        hidden_states = self.norm(hidden_states)
        query_states = self.q(hidden_states)
        key_states = self.k(hidden_states)
        value_states = self.v(hidden_states)
        batch_size, channels, height, width = query_states.shape
        query_states = query_states.reshape(batch_size, channels, height * width).permute(0, 2, 1)
        key_states = key_states.reshape(batch_size, channels, height * width)
        attn_weights = torch.bmm(query_states, key_states)
        attn_weights = attn_weights * (int(channels) ** (-0.5))
        attn_weights = F.softmax(attn_weights, dim=2)
        value_states = value_states.reshape(batch_size, channels, height * width)
        attn_weights = attn_weights.permute(0, 2, 1)
        attn_output = torch.bmm(value_states, attn_weights).reshape(batch_size, channels, height, width)
        attn_output = self.proj_out(attn_output)
        return residual + attn_output
class ChameleonVQVAEEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_resolutions = len(config.channel_multiplier)
        self.num_res_blocks = config.num_res_blocks
        base_channels = config.base_channels
        resolution = config.resolution
        in_channels = config.in_channels
        double_latent = config.double_latent
        latent_channels = config.latent_channels
        channel_multiplier = config.channel_multiplier
        self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
        curr_res = resolution
        in_channel_multiplier = (1,) + tuple(channel_multiplier)
        self.in_channel_multiplier = in_channel_multiplier
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = base_channels * in_channel_multiplier[i_level]
            block_out = base_channels * channel_multiplier[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(ChameleonVQVAEEncoderResnetBlock(config=config, in_channels=block_in, out_channels=block_out))
                block_in = block_out
                if (config.attn_resolutions is not None and curr_res in config.attn_resolutions and config.attn_type == "vanilla"): attn.append(ChameleonVQVAEEncoderAttnBlock(block_in))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in)
                curr_res = curr_res // 2
            self.down.append(down)
        self.mid = nn.Module()
        self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock(config=config, in_channels=block_in, out_channels=block_in)
        self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock(block_in) if config.attn_type == "vanilla" else nn.Identity()
        self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock(config=config, in_channels=block_in, out_channels=block_in)
        self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
        self.conv_out = torch.nn.Conv2d(block_in, 2 * latent_channels if double_latent else latent_channels, kernel_size=3, stride=1, padding=1)
    def forward(self, pixel_values: torch.LongTensor):
        hidden_states = [self.conv_in(pixel_values)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                hidden_state = self.down[i_level].block[i_block](hidden_states[-1])
                if len(self.down[i_level].attn) > 0: hidden_state = self.down[i_level].attn[i_block](hidden_state)
                hidden_states.append(hidden_state)
            if i_level != self.num_resolutions - 1: hidden_states.append(self.down[i_level].downsample(hidden_states[-1]))
        last_hidden_state = hidden_states[-1]
        last_hidden_state = self.mid.block_1(last_hidden_state)
        last_hidden_state = self.mid.attn_1(last_hidden_state)
        last_hidden_state = self.mid.block_2(last_hidden_state)
        last_hidden_state = self.norm_out(last_hidden_state)
        last_hidden_state *= torch.sigmoid(last_hidden_state)
        last_hidden_state = self.conv_out(last_hidden_state)
        return last_hidden_state
CHAMELEON_VQ_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 ([`ChameleonVQVAEConfig`]):
            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 VQ-VAE model used in Chameleon for encoding/decoding images into discrete tokens.
    This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
    [ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman](https://arxiv.org/abs/2203.13131).
    """, CHAMELEON_VQ_START_DOCSTRING)
class ChameleonVQVAE(PreTrainedModel):
    config_class = ChameleonVQVAEConfig
    _no_split_modules = ["ChameleonVQVAEVectorQuantizer"]
    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std)
        elif isinstance(module, nn.GroupNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif 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_()
    def __init__(self, config: ChameleonVQVAEConfig):
        super().__init__(config)
        self.encoder = ChameleonVQVAEEncoder(config)
        self.quantize = ChameleonVQVAEVectorQuantizer(config)
        self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(config.embed_dim, config.latent_channels, 1)
        self.eval()
    def encode(self, pixel_values: torch.LongTensor):
        hidden_states = self.encoder(pixel_values)
        hidden_states = self.quant_conv(hidden_states)
        quant, emb_loss, indices = self.quantize(hidden_states)
        return quant, emb_loss, indices
class ChameleonImageVocabularyMapping:
    def __init__(self, vocab_map):
        self.vocab_map = vocab_map
        self.image_token_id = vocab_map.get("<image>")
    @cached_property
    def val2name(self): return {v: k for k, v in self.vocab_map.items()}
    @cached_property
    def image_tokens(self): return sorted([val for name, val in self.vocab_map.items() if name.startswith("IMGIMG")])
    @cached_property
    def bpe2img(self):
        img_tkn_chr_mapping = {chr(ord("A") + i): str(i) for i in range(10)}
        def remap(old_name: str) -> str: return "".join(img_tkn_chr_mapping.get(c, c) for c in old_name[len("IMGIMG") : -1])
        return {tok: int(remap(self.val2name[tok])) for tok in self.image_tokens}
    @cached_property
    def img2bpe(self): return {v: k for k, v in self.bpe2img.items()}
    @cached_property
    def bpe2img_search_tensors(self): return torch.tensor(sorted(self.bpe2img.keys())), torch.tensor(sorted(self.bpe2img.values()))
    @cached_property
    def img2bpe_mapping_tensor(self):
        mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int)
        for k, v in self.img2bpe.items(): mapping[k] = v
        return mapping
    def convert_img2bpe(self, img_batch: torch.Tensor) -> torch.Tensor:
        device = img_batch.device
        img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
        return img_tokens.to(device)
CHAMELEON_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 ([`ChameleonConfig`]):
            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 chameleon Model outputting raw hidden-states without any specific head on top.", CHAMELEON_START_DOCSTRING)
class ChameleonPreTrainedModel(PreTrainedModel):
    config_class = ChameleonConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ChameleonDecoderLayer", "ChameleonSwinDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values", "causal_mask"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_quantized_cache = True
    _supports_cache_class = True
    _supports_static_cache = True
    _supports_param_buffer_assignment = False
    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, ChameleonVQVAE): module.apply(module._init_weights)
        elif 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_()
CHAMELEON_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 [`ChameleonImageProcessor.__call__`] for details.
        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 `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 (`Cache`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
            Should always be a [`~cache_utils.Cache`] instance and the model will output the same cache instance.
            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 bare chameleon Model outputting raw hidden-states without any specific head on top.", CHAMELEON_START_DOCSTRING)
class ChameleonModel(ChameleonPreTrainedModel):
    def __init__(self, config: ChameleonConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.vocabulary_mapping = ChameleonImageVocabularyMapping(config.vocabulary_map)
        decoder_layer = ChameleonDecoderLayer if not self.config.swin_norm else ChameleonSwinDecoderLayer
        self.layers = nn.ModuleList([decoder_layer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
        self.norm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.vqmodel = ChameleonVQVAE(config.vq_config)
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, value): self.embed_tokens = value
    def get_image_tokens(self, pixel_values: torch.FloatTensor):
        batch_size = pixel_values.shape[0]
        _, _, image_toks = self.vqmodel.encode(pixel_values)
        bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks)
        bpe_toks = bpe_toks.view(batch_size, -1)
        return bpe_toks
    @add_start_docstrings_to_model_forward(CHAMELEON_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[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = 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) -> Union[Tuple, BaseModelOutputWithPast]:
        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)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.")
            use_cache = False
        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")
        if pixel_values is not None:
            image_tokens = self.get_image_tokens(pixel_values)
            special_image_mask = input_ids == self.vocabulary_mapping.image_token_id
            image_tokens = image_tokens.to(input_ids.device, input_ids.dtype)
            input_ids = input_ids.masked_scatter(special_image_mask, image_tokens)
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(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)
        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions)
        hidden_states = inputs_embeds
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None
        for decoder_layer in self.layers:
            if output_hidden_states: all_hidden_states += (hidden_states,)
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(decoder_layer.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position)
            else: layer_outputs = decoder_layer(hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1]
            if output_attentions: all_self_attns += (layer_outputs[1],)
        hidden_states = self.norm(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        next_cache = None
        if use_cache: next_cache = next_decoder_cache
        if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns)
    def _update_causal_mask(self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask: return attention_mask
            return None
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training): return None
        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.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 past_seen_tokens + sequence_length + 1)
        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype,
        device=device, min_dtype=min_dtype, cache_position=cache_position, batch_size=input_tensor.shape[0])
        if (self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda" and not output_attentions): causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
        return causal_mask
@add_start_docstrings("Chameleon Model with a head on top used for outputting logits for next token prediction.", CHAMELEON_START_DOCSTRING)
class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.model = ChameleonModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()
    def get_input_embeddings(self): return self.model.embed_tokens
    def set_input_embeddings(self, value): self.model.embed_tokens = value
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def set_decoder(self, decoder): self.model = decoder
    def get_decoder(self): return self.model
    @add_start_docstrings_to_model_forward(CHAMELEON_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[Cache] = 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, cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple, CausalLMOutputWithPast]:
        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
        outputs = self.model(input_ids=input_ids, pixel_values=pixel_values, 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)
        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        image_tokens = self.model.vocabulary_mapping.image_tokens
        logits[:, :, image_tokens] = torch.finfo(logits.dtype).min
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def prepare_inputs_for_generation(self, input_ids, pixel_values=None, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, **kwargs):
        if past_key_values is not None:
            if inputs_embeds is not None: input_ids = input_ids[:, -cache_position.shape[0] :]
            elif input_ids.shape[1] != cache_position.shape[0]: input_ids = input_ids[:, cache_position]
        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :]
        if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds}
        else: model_inputs = {"input_ids": input_ids.contiguous()}
        if cache_position[0] == 0: model_inputs["pixel_values"] = pixel_values
        model_inputs.update({"position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask})
        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.                                                                 #
    #################################################################################################################################################################################################
"""
