"""
    #################################################################################################################################################################################################
    # 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 warnings
from typing import Optional, Tuple, Union
import torch
import torch.fx
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import (BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast)
from ...modeling_utils import PreTrainedModel
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, is_torch_fx_proxy, logging)
from ...utils.model_parallel_utils import assert_device_map, get_device_map
from .configuration_gptj import GPTJConfig
if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "hf-internal-testing/tiny-random-gptj"
_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"
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
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim))
    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float()
    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
@torch.fx.wrap
def get_embed_positions(embed_positions, position_ids): return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1)
def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
    x1 = x[:, :, :, ::2]
    x2 = x[:, :, :, 1::2]
    x = torch.stack((-x2, x1), dim=-1)
    return x.flatten(-2)
def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
    sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
    cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
    return (tensor * cos) + (rotate_every_two(tensor) * sin)
class GPTJAttention(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        self.config = config
        max_positions = config.max_position_embeddings
        self.attn_dropout = nn.Dropout(config.attn_pdrop)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)
        self.is_causal = True
        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.embed_dim = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_attention_heads
        if self.head_dim * self.num_attention_heads != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and `num_attention_heads`: {self.num_attention_heads}).")
        self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.rotary_dim = config.rotary_dim
        pos_embd_dim = self.rotary_dim or self.embed_dim
        self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
    def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary):
        new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        if rotary: return tensor
        if len(tensor.shape) == 5: return tensor.permute(0, 1, 3, 2, 4)
        elif len(tensor.shape) == 4: return tensor.permute(0, 2, 1, 3)
        else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
    def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
        if len(tensor.shape) == 5: tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
        elif len(tensor.shape) == 4: tensor = tensor.permute(0, 2, 1, 3).contiguous()
        else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
        new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
        return tensor.view(new_shape)
    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        query = query.to(torch.float32)
        key = key.to(torch.float32)
        attn_weights = torch.matmul(query, key.transpose(-1, -2))
        attn_weights = attn_weights / self.scale_attn
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key.shape[-2]]
            attn_weights = attn_weights + causal_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        attn_weights = attn_weights.to(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)
        if head_mask is not None: attn_weights = attn_weights * head_mask
        attn_output = torch.matmul(attn_weights, value)
        return attn_output, attn_weights
    def _get_embed_positions(self, position_ids):
        embed_positions = self.embed_positions
        if embed_positions.device != position_ids.device:
            embed_positions = embed_positions.to(position_ids.device)
            self.embed_positions = embed_positions
        return embed_positions.repeat(position_ids.shape[0], 1, 1)
    def forward(self, hidden_states: torch.FloatTensor, layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]]]:
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
        value = self.v_proj(hidden_states)
        query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
        key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
        value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
        if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing(): embed_positions = get_embed_positions(self.embed_positions, position_ids)
        else: embed_positions = self._get_embed_positions(position_ids)
        repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
        sincos = torch.gather(embed_positions, 1, repeated_position_ids)
        sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
        if self.rotary_dim is not None:
            k_rot = key[:, :, :, : self.rotary_dim]
            k_pass = key[:, :, :, self.rotary_dim :]
            q_rot = query[:, :, :, : self.rotary_dim]
            q_pass = query[:, :, :, self.rotary_dim :]
            k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
            q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
            key = torch.cat([k_rot, k_pass], dim=-1)
            query = torch.cat([q_rot, q_pass], dim=-1)
        else:
            key = apply_rotary_pos_emb(key, sin, cos)
            query = apply_rotary_pos_emb(query, sin, cos)
        key = key.permute(0, 2, 1, 3)
        query = query.permute(0, 2, 1, 3)
        if layer_past is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_dim, "cache_position": cache_position}
            key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
        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_dim)
        attn_output = self.out_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, layer_past)
        if output_attentions: outputs += (attn_weights,)
        return outputs
class GPTJFlashAttention2(GPTJAttention):
    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, layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False,
    cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]]]:
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
        value = self.v_proj(hidden_states)
        query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
        key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
        value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
        if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing(): embed_positions = get_embed_positions(self.embed_positions, position_ids)
        else: embed_positions = self._get_embed_positions(position_ids)
        repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
        sincos = torch.gather(embed_positions, 1, repeated_position_ids)
        sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
        if self.rotary_dim is not None:
            k_rot = key[:, :, :, : self.rotary_dim]
            k_pass = key[:, :, :, self.rotary_dim :]
            q_rot = query[:, :, :, : self.rotary_dim]
            q_pass = query[:, :, :, self.rotary_dim :]
            k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
            q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
            key = torch.cat([k_rot, k_pass], dim=-1)
            query = torch.cat([q_rot, q_pass], dim=-1)
        else:
            key = apply_rotary_pos_emb(key, sin, cos)
            query = apply_rotary_pos_emb(query, sin, cos)
        key = key.permute(0, 2, 1, 3)
        query = query.permute(0, 2, 1, 3)
        if layer_past is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_dim, "cache_position": cache_position}
            key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
        key = key.permute(0, 2, 1, 3).contiguous()
        query = query.permute(0, 2, 1, 3).contiguous()
        value = value.permute(0, 2, 1, 3).contiguous()
        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.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 = query.to(target_dtype)
            key = key.to(target_dtype)
            value = value.to(target_dtype)
        attention_dropout = self.config.attn_pdrop if self.training else 0.0
        query_length = query.shape[1]
        attn_weights = _flash_attention_forward(query, key, value, attention_mask, query_length, dropout=attention_dropout, 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], attn_weights.shape[2] * attn_weights.shape[3])
        attn_output = self.out_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, layer_past)
        if output_attentions: outputs += (attn_weights,)
        return outputs
GPTJ_ATTENTION_CLASSES = {"eager": GPTJAttention, "flash_attention_2": GPTJFlashAttention2}
class GPTJMLP(nn.Module):
    def __init__(self, intermediate_size, config):
        super().__init__()
        embed_dim = config.n_embd
        self.fc_in = nn.Linear(embed_dim, intermediate_size)
        self.fc_out = nn.Linear(intermediate_size, embed_dim)
        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(config.resid_pdrop)
    def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.fc_in(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc_out(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
class GPTJBlock(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
        self.mlp = GPTJMLP(inner_dim, config)
    def forward(self, hidden_states: Optional[torch.FloatTensor], layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False,
    cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask,
        use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position)
        attn_output = attn_outputs[0]
        outputs = attn_outputs[1:]
        feed_forward_hidden_states = self.mlp(hidden_states)
        hidden_states = attn_output + feed_forward_hidden_states + residual
        if use_cache: outputs = (hidden_states,) + outputs
        else: outputs = (hidden_states,) + outputs[1:]
        return outputs
class GPTJPreTrainedModel(PreTrainedModel):
    config_class = GPTJConfig
    base_model_prefix = "transformer"
    is_parallelizable = True
    supports_gradient_checkpointing = True
    _no_split_modules = ["GPTJBlock"]
    _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_param_buffer_assignment = False
    def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs)
    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)
GPTJ_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 ([`GPTJConfig`]): 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.
"""
GPTJ_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)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        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_attention_heads,)` or `(n_layer, num_attention_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_dim)`, *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 [`~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.
"""
PARALLELIZE_DOCSTRING = r"""
    This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute
    attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks
    across all devices.
    Args:
        device_map (`Dict[int, list]`, *optional*):
            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
            automatically mapped to the first device (for esoteric reasons). That means that the first device should
            have fewer attention modules mapped to it than other devices. For reference, the GPT-J models have the
            following number of attention modules:
                - gpt-j-6B: 28
    Example:
    ```python
    model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
    device_map = {0: [0, 1, 2, 3, 4, 5, 6], 1: [7, 8, 9, 10, 11, 12, 13], 2: [14, 15, 16, 17, 18, 19, 20], 3: [21, 22, 23, 24, 25, 26, 27]}
    model.parallelize(device_map)
    ```
"""
DEPARALLELIZE_DOCSTRING = r"""
    Moves the model to CPU from a model parallel state.
    Example:
    ```python
    model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
    device_map = {0: [0, 1, 2, 3, 4, 5, 6], 1: [7, 8, 9, 10, 11, 12, 13], 2: [14, 15, 16, 17, 18, 19, 20], 3: [21, 22, 23, 24, 25, 26, 27]}
    model.parallelize(device_map)
    model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
    ```
"""
@add_start_docstrings("The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.", GPTJ_START_DOCSTRING)
class GPTJModel(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.embed_dim = config.n_embd
        self.vocab_size = config.vocab_size
        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPTJBlock(config, layer_idx=i) for i in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
        self.model_parallel = False
        self.device_map = None
        self.gradient_checkpointing = False
        self.post_init()
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.h))
        self.model_parallel = True
        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
        self.last_device = "cuda:" + str(max(self.device_map.keys()))
        self.wte = self.wte.to(self.first_device)
        for k, v in self.device_map.items():
            for block in v:
                cuda_device = "cuda:" + str(k)
                self.h[block] = self.h[block].to(cuda_device)
        self.ln_f = self.ln_f.to(self.last_device)
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.model_parallel = False
        self.device_map = None
        self.first_device = "cpu"
        self.last_device = "cpu"
        self.wte = self.wte.to("cpu")
        for index in range(len(self.h)): self.h[index] = self.h[index].to("cpu")
        self.ln_f = self.ln_f.to("cpu")
        torch.cuda.empty_cache()
    def get_input_embeddings(self): return self.wte
    def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    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,
    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 (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.wte(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_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(past_key_values_length, past_key_values_length + 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.n_layer)
        hidden_states = inputs_embeds
        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, seq_length)
            token_type_embeds = self.wte(token_type_ids)
            hidden_states = hidden_states + token_type_embeds
        hidden_states = self.drop(hidden_states)
        output_shape = (-1, seq_length, hidden_states.size(-1))
        next_decoder_cache = None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, block in enumerate(self.h):
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                if past_key_values is not None:
                    past_key_values.key_cache = past_key_values.key_cache.to(hidden_states.device)
                    past_key_values.value_cache = past_key_values.value_cache.to(hidden_states.device)
                if causal_mask is not None: causal_mask = causal_mask.to(hidden_states.device)
                if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device)
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func(block.__call__, hidden_states, None, causal_mask, position_ids, head_mask[i], use_cache, output_attentions, cache_position)
            else:
                outputs = block(hidden_states=hidden_states, layer_past=past_key_values, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i],
                use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position)
            hidden_states = outputs[0]
            if use_cache is True: next_decoder_cache = outputs[1]
            if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1))
        hidden_states = self.ln_f(hidden_states)
        hidden_states = hidden_states.view(output_shape)
        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_self_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_self_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("The GPT-J Model transformer with a language modeling head on top.", GPTJ_START_DOCSTRING)
class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.transformer = GPTJModel(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
        self.model_parallel = False
        self.device_map = None
        self.post_init()
    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn("`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0': 0, 'transformer.h.1': 1, ...}", FutureWarning)
        self.device_map = (get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.transformer.h))
        self.transformer.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.transformer.first_device)
        self.model_parallel = True
    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn("Like `parallelize`, `deparallelize` is deprecated and will be removed in v1 of Sapiens Transformers.", FutureWarning)
        self.transformer.deparallelize()
        self.transformer = self.transformer.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        torch.cuda.empty_cache()
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, past_key_values=None, inputs_embeds=None, cache_position=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 token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
        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.lm_head.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, "token_type_ids": token_type_ids, "attention_mask": attention_mask})
        return model_inputs
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    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, 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
        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, cache_position=cache_position)
        hidden_states = transformer_outputs[0]
        if self.model_parallel:
            torch.cuda.set_device(self.transformer.first_device)
            hidden_states = hidden_states.to(self.lm_head.weight.device)
        lm_logits = self.lm_head(hidden_states).to(torch.float32)
        loss = None
        if labels is not None:
            labels = labels.to(lm_logits.device)
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
            loss = loss.to(hidden_states.dtype)
        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    @staticmethod
    def _reorder_cache(past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: return tuple(tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values)
@add_start_docstrings("""
    The GPT-J Model transformer with a sequence classification head on top (linear layer).
    [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT, GPT-2, GPT-Neo) 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).
    """, GPTJ_START_DOCSTRING)
class GPTJForSequenceClassification(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPTJModel(config)
        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
        self.model_parallel = False
        self.device_map = None
        self.post_init()
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[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, SequenceClassifierOutputWithPast]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, 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 = transformer_outputs[0]
        logits = self.score(hidden_states)
        if input_ids is not None: batch_size = input_ids.shape[0]
        else: batch_size = inputs_embeds.shape[0]
        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(pooled_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,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutputWithPast(loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
@add_start_docstrings("""
    The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """, GPTJ_START_DOCSTRING)
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPTJModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.model_parallel = False
        self.device_map = None
        self.post_init()
    @add_start_docstrings_to_model_forward(GPTJ_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.transformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        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.                                                                 #
    #################################################################################################################################################################################################
"""
