"""
    #################################################################################################################################################################################################
    # 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 typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from packaging import version
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...file_utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings)
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import (BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...utils import (get_torch_version, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging)
from .configuration_gpt_neox import GPTNeoXConfig
if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b"
_CONFIG_FOR_DOC = "GPTNeoXConfig"
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
class GPTNeoXPreTrainedModel(PreTrainedModel):
    config_class = GPTNeoXConfig
    base_model_prefix = "gpt_neox"
    supports_gradient_checkpointing = True
    _no_split_modules = ["GPTNeoXLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_sdpa = True
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
class GPTNeoXAttention(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        self.config = config
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        if self.hidden_size % self.num_attention_heads != 0: raise ValueError("The hidden size is not divisble by the number of attention heads! Make sure to update them")
        self.head_size = self.hidden_size // self.num_attention_heads
        self.rotary_ndims = int(self.head_size * config.rotary_pct)
        self.rope_theta = config.rotary_emb_base
        self._init_bias(config.max_position_embeddings)
        self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False)
        self.rotary_emb = GPTNeoXRotaryEmbedding(config=self.config)
        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.norm_factor = self.head_size**-0.5
        self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
        self.attention_dropout = nn.Dropout(config.attention_dropout)
        self.is_causal = True
        self.layer_idx = layer_idx
    def _init_bias(self, max_positions, device=None):
        self.register_buffer("bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(1, 1, max_positions, max_positions), persistent=False)
        if device is not None: self.bias = self.bias.to(device)
    def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None,
    layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, padding_mask: Optional[torch.Tensor] = None,
    cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        query, key, value, present = self._attn_projections_and_rope(hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache, position_embeddings=position_embeddings)
        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
        attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size)
        attn_output = self.dense(attn_output)
        outputs = (attn_output, present)
        if output_attentions: outputs += (attn_weights,)
        return outputs
    @classmethod
    def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
        new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        tensor = tensor.permute(0, 2, 1, 3)
        return tensor
    @classmethod
    def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
        tensor = tensor.permute(0, 2, 1, 3).contiguous()
        tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size)
        return tensor
    def _attn_projections_and_rope(self, hidden_states: torch.FloatTensor, position_ids: torch.LongTensor, layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False,
    cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        qkv = self.query_key_value(hidden_states)
        new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
        qkv = qkv.view(*new_qkv_shape)
        query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
        key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
        value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
        query_rot = query[..., : self.rotary_ndims]
        query_pass = query[..., self.rotary_ndims :]
        key_rot = key[..., : self.rotary_ndims]
        key_pass = key[..., self.rotary_ndims :]
        if position_embeddings is None:
            logger.warning_once("The attention layers in this model are transitioning from computing the RoPE embeddings internally through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed `position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be removed and `position_embeddings` will be mandatory.")
            cos, sin = self.rotary_emb(value, position_ids)
        else: cos, sin = position_embeddings
        query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
        query = torch.cat((query, query_pass), dim=-1)
        key = torch.cat((key, key_pass), dim=-1)
        if layer_past is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_ndims, "cache_position": cache_position}
            key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
        return query, key, value, layer_past
    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        batch_size, num_attention_heads, query_length, attn_head_size = query.size()
        key_length = key.size(-2)
        if key_length > self.bias.shape[-1]: self._init_bias(key_length, device=key.device)
        causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
        query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
        key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
        attn_scores = torch.zeros(batch_size * num_attention_heads, query_length, key_length, dtype=query.dtype, device=key.device)
        attn_scores = torch.baddbmm(attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=self.norm_factor)
        attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length)
        mask_value = torch.finfo(attn_scores.dtype).min
        mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device)
        attn_scores = torch.where(causal_mask, attn_scores, mask_value)
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key.shape[-2]]
            attn_scores = attn_scores + causal_mask
        attn_weights = nn.functional.softmax(attn_scores, dim=-1)
        attn_weights = attn_weights.to(value.dtype)
        if head_mask is not None: attn_weights = attn_weights * head_mask
        attn_weights = self.attention_dropout(attn_weights)
        attn_output = torch.matmul(attn_weights, value)
        return attn_output, attn_weights
class GPTNeoXFlashAttention2(GPTNeoXAttention):
    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.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None,
    layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        query, key, value, present = self._attn_projections_and_rope(hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings)
        query_length = query.shape[-2]
        target_dtype = value.dtype
        if query.dtype != target_dtype: query = query.to(target_dtype)
        if key.dtype != target_dtype: key = key.to(target_dtype)
        query = query.permute(0, 2, 1, 3)
        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)
        input_dtype = query.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.query_key_value.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 = query.to(target_dtype)
            key = key.to(target_dtype)
            value = value.to(target_dtype)
        attention_dropout = self.config.attention_dropout if self.training else 0.0
        attn_weights = _flash_attention_forward(query, key, value, attention_mask, query_length, dropout=attention_dropout, softmax_scale=self.norm_factor, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask)
        attn_output = attn_weights.reshape(attn_weights.shape[0], attn_weights.shape[1], self.num_attention_heads * self.head_size)
        attn_output = self.dense(attn_output)
        outputs = (attn_output, layer_past)
        if output_attentions: outputs += (attn_weights,)
        return outputs
class GPTNeoXSdpaAttention(GPTNeoXAttention):
    def __init__(self, config, layer_idx=None):
        super().__init__(config, layer_idx=layer_idx)
        self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0")
    def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None,
    layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        if output_attentions or head_mask is not None:
            logger.warning_once("`GPTNeoXSdpaAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `head_mask`. 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, head_mask=head_mask, layer_past=layer_past,
            use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position)
        bsz, q_len, _ = hidden_states.size()
        query, key, value, present = self._attn_projections_and_rope(hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache,
        cache_position=cache_position, position_embeddings=position_embeddings)
        causal_mask = attention_mask
        if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key.shape[-2]]
        target_dtype = value.dtype
        if query.dtype != target_dtype: query = query.to(target_dtype)
        if key.dtype != target_dtype: key = key.to(target_dtype)
        if self.require_contiguous_qkv and query.device.type == "cuda" and attention_mask is not None:
            query = query.contiguous()
            key = key.contiguous()
            value = value.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=query, key=key, value=value, attn_mask=causal_mask, dropout_p=self.attention_dropout.p 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.dense(attn_output)
        return attn_output, present, None
def attention_mask_func(attention_scores, ltor_mask):
    attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
    return attention_scores
class GPTNeoXRotaryEmbedding(nn.Module):
    def __init__(self, dim=None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, rope_type="default", config: Optional[GPTNeoXConfig] = None):
        super().__init__()
        self.rope_kwargs = {}
        if config is None:
            logger.warning_once("`GPTNeoXRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46")
            self.rope_kwargs = {"rope_type": rope_type, "factor": scaling_factor, "dim": dim, "base": base, "max_position_embeddings": max_position_embeddings}
            self.rope_type = rope_type
            self.max_seq_len_cached = max_position_embeddings
            self.original_max_seq_len = max_position_embeddings
        else:
            if config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
            else: self.rope_type = "default"
            self.max_seq_len_cached = config.max_position_embeddings
            self.original_max_seq_len = config.max_position_embeddings
        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq
    def _dynamic_frequency_update(self, position_ids, device):
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:
            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len, **self.rope_kwargs)
            self.register_buffer("inv_freq", inv_freq, persistent=False)
            self.max_seq_len_cached = seq_len
        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len
    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type: self._dynamic_frequency_update(position_ids, device=x.device)
        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()
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling
        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class GPTNeoXLinearScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
    def __init__(self, *args, **kwargs):
        logger.warning_once("`GPTNeoXLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use `GPTNeoXRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__).")
        kwargs["rope_type"] = "linear"
        super().__init__(*args, **kwargs)
class GPTNeoXDynamicNTKScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
    def __init__(self, *args, **kwargs):
        logger.warning_once("`GPTNeoXDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use `GPTNeoXRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to __init__).")
        kwargs["rope_type"] = "dynamic"
        super().__init__(*args, **kwargs)
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 GPTNeoXMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
        self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
        self.act = ACT2FN[config.hidden_act]
    def forward(self, hidden_states):
        hidden_states = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dense_4h_to_h(hidden_states)
        return hidden_states
GPT_NEOX_ATTENTION_CLASSES = {"eager": GPTNeoXAttention, "flash_attention_2": GPTNeoXFlashAttention2, "sdpa": GPTNeoXSdpaAttention}
class GPTNeoXLayer(nn.Module):
    def __init__(self, config, layer_idx):
        super().__init__()
        self.use_parallel_residual = config.use_parallel_residual
        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.post_attention_dropout = nn.Dropout(config.hidden_dropout)
        self.post_mlp_dropout = nn.Dropout(config.hidden_dropout)
        self.attention = GPT_NEOX_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
        self.mlp = GPTNeoXMLP(config)
    def forward(self, hidden_states: Optional[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, layer_past: Optional[Cache] = None, output_attentions: Optional[bool] = False,
    cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        attention_layer_outputs = self.attention(self.input_layernorm(hidden_states), attention_mask=attention_mask, position_ids=position_ids, layer_past=layer_past,
        head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, position_embeddings=position_embeddings)
        attn_output = attention_layer_outputs[0]
        attn_output = self.post_attention_dropout(attn_output)
        outputs = attention_layer_outputs[1:]
        if self.use_parallel_residual:
            mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
            mlp_output = self.post_mlp_dropout(mlp_output)
            hidden_states = mlp_output + attn_output + hidden_states
        else:
            attn_output = attn_output + hidden_states
            mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
            mlp_output = self.post_mlp_dropout(mlp_output)
            hidden_states = mlp_output + attn_output
        if use_cache: outputs = (hidden_states,) + outputs
        else: outputs = (hidden_states,) + outputs[1:]
        return outputs
GPT_NEOX_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`~GPTNeoXConfig`]): 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.
"""
GPT_NEOX_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        position_ids (`torch.LongTensor` of shape `(0)`, *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)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *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`.
            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance, see our;
            - 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)`. This is also known as the legacy
            cache format.
            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.
            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)`.
        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 [`~file_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 GPTNeoX Model transformer outputting raw hidden-states without any specific head on top.", GPT_NEOX_START_DOCSTRING)
class GPTNeoXModel(GPTNeoXPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
        self.emb_dropout = nn.Dropout(config.hidden_dropout)
        self.layers = nn.ModuleList([GPTNeoXLayer(config, i) for i in range(config.num_hidden_layers)])
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.rotary_emb = GPTNeoXRotaryEmbedding(config=config)
        self._attn_implementation = config._attn_implementation
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.embed_in
    def set_input_embeddings(self, value): self.embed_in = value
    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[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)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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 self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False
        if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids)
        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            if past_key_values is None: past_key_values = DynamicCache()
            else:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                logger.warning_once("We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and will be removed in v1.0.")
        seq_length = inputs_embeds.shape[1]
        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 + seq_length, 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)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        hidden_states = self.emb_dropout(inputs_embeds)
        position_embeddings = self.rotary_emb(hidden_states, position_ids)
        next_decoder_cache = None
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func(layer.__call__, hidden_states, causal_mask, position_ids, head_mask[i], use_cache, None, output_attentions, cache_position, position_embeddings)
            else: outputs = layer(hidden_states, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], layer_past=past_key_values, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, position_embeddings=position_embeddings)
            hidden_states = outputs[0]
            if use_cache is True: next_decoder_cache = outputs[1]
            if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
        hidden_states = self.final_layer_norm(hidden_states)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache: next_cache = next_cache.to_legacy_cache()
        if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None)
        return BaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_attentions)
    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("GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.", GPT_NEOX_START_DOCSTRING)
class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["embed_out.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.gpt_neox = GPTNeoXModel(config)
        self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()
    def get_output_embeddings(self): return self.embed_out
    def set_output_embeddings(self, new_embeddings): self.embed_out = new_embeddings
    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[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]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.gpt_neox(input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values,
        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]
        lm_logits = self.embed_out(hidden_states)
        lm_loss = None
        if labels is not None:
            labels = labels.to(lm_logits.device)
            shift_logits = lm_logits[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((lm_loss,) + output) if lm_loss is not None else output
        return CausalLMOutputWithPast(loss=lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def prepare_inputs_for_generation(self, input_ids, 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] :]
                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
        if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
        else: model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
        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.embed_out.weight.dtype
            min_dtype = torch.finfo(dtype).min
            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)
        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
    def _reorder_cache(self, past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:],)
        return reordered_past
@add_start_docstrings("""
    The GPTNeoX Model transformer with a sequence classification head on top (linear layer).
    [`GPTNeoXForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-1) do.
    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """, GPT_NEOX_START_DOCSTRING)
class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.gpt_neox = GPTNeoXModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
        self.post_init()
    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[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) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.gpt_neox(input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.score(hidden_states)
        if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2]
        else: batch_size, sequence_length = inputs_embeds.shape[:2]
        if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None: sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else:
                sequence_lengths = -1
                logger.warning_once(f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be unexpected if using padding tokens in conjunction with `inputs_embeds.`")
        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutputWithPast(loss=loss, logits=pooled_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.gpt_neox = GPTNeoXModel(config)
        self.dropout = nn.Dropout(config.classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None,
    token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = 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) -> Union[Tuple, TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.gpt_neox(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("""
    The GPT-NeoX Model transformer with a span classification head on top for extractive question-answering tasks like
    SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """, GPT_NEOX_START_DOCSTRING)
class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.gpt_neox = GPTNeoXModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, 2)
        self.post_init()
    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None,
    end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.gpt_neox(input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1).to(start_logits.device)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
