"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import is_torch_greater_or_equal_than_2_2
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)
from .configuration_sapiens_code import SapiensCodeConfig
if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward
_CHECKPOINT_FOR_DOC = "sapiens/sapiens_code"
_CONFIG_FOR_DOC = "SapiensCodeConfig"
@torch.jit.script
def upcast_masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
    input_dtype = x.dtype
    x = x.to(softmax_dtype) * scale
    x = torch.where(mask, x, mask_value)
    x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
    return x
@torch.jit.script
def upcast_softmax(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
    input_dtype = x.dtype
    x = x.to(softmax_dtype) * scale
    x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
    return x
@torch.jit.script
def masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
    x = torch.where(mask, x, mask_value)
    x = torch.nn.functional.softmax(x, dim=-1)
    return x
class SapiensCodeAttention(nn.Module):
    def __init__(self, config, is_cross_attention=False, layer_idx=None):
        super().__init__()
        self.config = config
        self.mask_value = None
        self.multi_query = config.multi_query
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.kv_heads = 1 if self.multi_query else self.num_heads
        self.kv_dim = self.kv_heads * self.head_dim
        self.split_size = self.embed_dim
        self.is_causal = True
        if self.head_dim * self.num_heads != self.embed_dim: raise ValueError(f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).")
        self.scale_attn_weights = config.scale_attn_weights
        self.is_cross_attention = is_cross_attention
        self.layer_idx = layer_idx
        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
        self.scale_attention_softmax_in_fp32 = (config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32)
        self.attn_pdrop = config.attn_pdrop
        if self.is_cross_attention:
            if self.multi_query: raise NotImplementedError("Multi-Query Attention not supported for cross_attention")
            self.c_attn = nn.Linear(self.embed_dim, 2 * self.embed_dim)
            self.q_attn = nn.Linear(self.embed_dim, self.embed_dim)
        else: self.c_attn = nn.Linear(self.embed_dim, self.embed_dim + 2 * self.kv_dim)
        self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.attn_dropout = nn.Dropout(config.attn_pdrop)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)
    def _get_mask_value(self, device, dtype):
        if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device: self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
        return self.mask_value
    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        dtype = query.dtype
        softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
        upcast = dtype != softmax_dtype
        unscale = self.layer_idx + 1 if self.scale_attention_softmax_in_fp32 and upcast else 1
        scale_factor = unscale**-1
        if self.scale_attn_weights: scale_factor /= self.head_dim**0.5
        query_shape = query.shape
        batch_size = query_shape[0]
        key_length = key.size(-1)
        if self.multi_query:
            query_length = query_shape[1]
            attn_shape = (batch_size, query_length, self.num_heads, key_length)
            attn_view = (batch_size, query_length * self.num_heads, key_length)
            query = query.reshape(batch_size, query_length * self.num_heads, self.head_dim)
        else:
            query_length = query_shape[2]
            attn_shape = (batch_size, self.num_heads, query_length, key_length)
            attn_view = (batch_size * self.num_heads, query_length, key_length)
            query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
            key = key.reshape(batch_size * self.num_heads, self.head_dim, key_length)
        attn_weights = torch.empty(attn_view, device=query.device, dtype=query.dtype)
        if query.device.type == "cpu":
            attn_weights = torch.zeros_like(attn_weights)
            beta = 1
        else: beta = 0
        attn_weights = torch.baddbmm(attn_weights, query, key, beta=beta, alpha=scale_factor).view(attn_shape)
        if upcast:
            if attention_mask is None: attn_weights = upcast_softmax(attn_weights, unscale, softmax_dtype)
            else:
                mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)
                attn_weights = upcast_masked_softmax(attn_weights, attention_mask, mask_value, unscale, softmax_dtype)
        else:
            if attention_mask is not None:
                mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)
                attn_weights = torch.where(attention_mask, attn_weights, mask_value)
            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
        attn_weights = self.attn_dropout(attn_weights)
        if head_mask is not None:
            if self.multi_query: head_mask = head_mask.transpose(1, 2)
            attn_weights = attn_weights * head_mask
        if self.multi_query: attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)
        else: attn_output = torch.matmul(attn_weights, value)
        return attn_output, attn_weights
    def forward(self, hidden_states: torch.Tensor, layer_past: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]]]:
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn") or not self.is_cross_attention: raise ValueError("If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `SapiensCodeAttention(..., is_cross_attention=True)`.")
            query = self.q_attn(hidden_states)
            key_value = self.c_attn(encoder_hidden_states)
            attention_mask = encoder_attention_mask
        elif self.multi_query: query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
        else: query, key_value = (self.c_attn(hidden_states).view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim).transpose(1, 2).split((self.head_dim, 2 * self.head_dim), dim=3))
        if layer_past is not None: key_value = torch.cat((layer_past, key_value), dim=-2)
        present = key_value if use_cache else None
        key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
        attn_output, attn_weights = self._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)
        if not self.multi_query: attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, present)
        if output_attentions:
            if self.multi_query: attn_weights = attn_weights.transpose(1, 2)
            outputs += (attn_weights,)
        return outputs
class SapiensCodeFlashAttention2(SapiensCodeAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
    def forward(self, hidden_states: torch.Tensor, layer_past: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]]]:
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn") or not self.is_cross_attention: raise ValueError("If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `SapiensCodeAttention(..., is_cross_attention=True)`.")
            query = self.q_attn(hidden_states)
            key_value = self.c_attn(encoder_hidden_states)
            attention_mask = encoder_attention_mask
        elif self.multi_query: query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
        else: query, key_value = (self.c_attn(hidden_states).view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim).transpose(1, 2).split((self.head_dim, 2 * self.head_dim), dim=3))
        if layer_past is not None: key_value = torch.cat((layer_past, key_value), dim=-2)
        present = key_value if use_cache else None
        key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
        if self.multi_query:
            batch_size, query_length, _ = query.shape
            query = query.reshape(batch_size, query_length, self.num_heads, self.head_dim)
            key = key.unsqueeze(2)
            value = value.unsqueeze(2)
        else:
            query_length = query.shape[2]
            batch_size, _, tgt, _ = key.shape
            query = query.transpose(1, 2).reshape(batch_size, query_length, self.num_heads, self.head_dim)
            key = key.transpose(1, 2).reshape(batch_size, tgt, self.num_heads, self.head_dim)
            value = value.transpose(1, 2).reshape(batch_size, tgt, self.num_heads, self.head_dim)
        attn_dropout = self.attn_pdrop if self.training else 0.0
        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.c_attn.weight.dtype
            query = query.to(target_dtype)
            key = key.to(target_dtype)
            value = value.to(target_dtype)
        attn_output = _flash_attention_forward(query, key, value, attention_mask, query_length, dropout=attn_dropout, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask)
        attn_weights_reshaped = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
        attn_output = self.c_proj(attn_weights_reshaped)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, present)
        if output_attentions:
            if self.multi_query: attn_weights_reshaped = attn_weights_reshaped.transpose(1, 2)
        else: attn_weights_reshaped = None
        outputs += (attn_weights_reshaped,)
        return outputs
class SapiensCodeSdpaAttention(SapiensCodeAttention):
    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        if head_mask is not None: raise ValueError("PyTorch SDPA does not support head_mask. Please open an issue in Transformers repository.")
        scale = None
        if not self.scale_attn_weights: scale = 1
        query_shape = query.shape
        batch_size = query_shape[0]
        key.shape[-2]
        if self.multi_query:
            query_length = query_shape[1]
            query = query.view(batch_size, query_length, self.num_heads, self.head_dim).transpose(1, 2)
            key = key.unsqueeze(1)
            value = value.unsqueeze(1)
            if is_torch_greater_or_equal_than_2_2:
                key = key.expand(-1, self.num_heads, -1, -1)
                value = value.expand(-1, self.num_heads, -1, -1)
        else:
            query_length = query_shape[-1]
            if query.device.type == "cuda" and attention_mask is not None:
                query = query.contiguous()
                key = key.contiguous()
                value = value.contiguous()
        is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False
        sdpa_result = torch.nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=self.attn_pdrop if self.training else 0.0, is_causal=is_causal, scale=scale)
        if self.multi_query:
            sdpa_result = sdpa_result.transpose(1, 2)
            sdpa_result = sdpa_result.reshape(query_shape)
        return sdpa_result, None
    def forward(self, hidden_states: torch.Tensor, layer_past: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]]]:
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn") or not self.is_cross_attention: raise ValueError("If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `SapiensCodeAttention(..., is_cross_attention=True)`.")
            query = self.q_attn(hidden_states)
            key_value = self.c_attn(encoder_hidden_states)
            attention_mask = encoder_attention_mask
        elif self.multi_query: query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
        else: query, key_value = (self.c_attn(hidden_states).view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim).transpose(1, 2).split((self.head_dim, 2 * self.head_dim), dim=3))
        if layer_past is not None: key_value = torch.cat((layer_past, key_value), dim=-2)
        present = key_value if use_cache else None
        key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
        if not output_attentions and head_mask is None: attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
        else: attn_output, attn_weights = super()._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)
        if not self.multi_query: attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, present)
        if output_attentions:
            if self.multi_query: attn_weights = attn_weights.transpose(1, 2)
            outputs += (attn_weights,)
        return outputs
class SapiensCodeMLP(nn.Module):
    def __init__(self, intermediate_size, config):
        super().__init__()
        embed_dim = config.hidden_size
        self.c_fc = nn.Linear(embed_dim, intermediate_size)
        self.c_proj = 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[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
        hidden_states = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.c_proj(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
SAPIENSCODE_ATTENTION_CLASSES = {"eager": SapiensCodeAttention, "flash_attention_2": SapiensCodeFlashAttention2, "sdpa": SapiensCodeSdpaAttention}
class SapiensCodeBlock(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        hidden_size = config.hidden_size
        self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = SAPIENSCODE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        if config.add_cross_attention:
            if config.multi_query: raise NotImplementedError("Cross-attention not implemented for MQA")
            self.crossattention = SAPIENSCODE_ATTENTION_CLASSES[config._attn_implementation](config, is_cross_attention=True, layer_idx=layer_idx)
            self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = SapiensCodeMLP(self.inner_dim, config)
    def forward(self, hidden_states: Optional[Tuple[torch.Tensor]], layer_past: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False,
    **kwargs) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions)
        attn_output = attn_outputs[0]
        outputs = attn_outputs[1:]
        hidden_states = attn_output + residual
        if encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"): raise ValueError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
            residual = hidden_states
            hidden_states = self.ln_cross_attn(hidden_states)
            cross_attn_outputs = self.crossattention(hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions)
            attn_output = cross_attn_outputs[0]
            hidden_states = residual + attn_output
            outputs = outputs + cross_attn_outputs[2:]
        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        hidden_states = residual + feed_forward_hidden_states
        if use_cache: outputs = (hidden_states,) + outputs
        else: outputs = (hidden_states,) + outputs[1:]
        return outputs
class SapiensCodePreTrainedModel(PreTrainedModel):
    config_class = SapiensCodeConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["SapiensCodeBlock"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs)
    def _init_weights(self, module):
        if isinstance(module, (SapiensCodeMLP, SapiensCodeAttention)):
            module.c_proj.weight.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
            module.c_proj._is_hf_initialized = True
        elif 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)
SAPIENS_CODE_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`SapiensCodeConfig`]): 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.
"""
SAPIENS_CODE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
            sequence tokens in the vocabulary.
            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        past_key_values (`Tuple[torch.Tensor]` of length `config.n_layers`):
            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
            their past given to this model should not be passed as `input_ids` as they have already been computed.
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
            `past_key_values`. In other words, the `attention_mask` always has to have the length:
            `len(past_key_values) + len(input_ids)`
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`, *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.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.Tensor` 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.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
            `past_key_values`).
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare SAPIENS_CODE Model transformer outputting raw hidden-states without any specific head on top.", SAPIENS_CODE_START_DOCSTRING)
class SapiensCodeModel(SapiensCodePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.multi_query = config.multi_query
        self.embed_dim = config.hidden_size
        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([SapiensCodeBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
        max_positions = config.max_position_embeddings
        self.register_buffer("bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), persistent=False)
        self.gradient_checkpointing = False
        self._use_sdpa = config._attn_implementation == "sdpa"
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.post_init()
    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(SAPIENS_CODE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if batch_size <= 0: raise ValueError("batch_size has to be defined and > 0")
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1])
        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else: past_length = past_key_values[0].size(-2)
        if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_length > 0: position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
        elif position_ids is None:
            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0)
        query_length = input_shape[-1]
        key_length = past_length + query_length
        self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]
        if self._use_flash_attention_2:
            attention_mask = attention_mask.bool() if (attention_mask is not None and 0 in attention_mask) else None
            encoder_attention_mask = (encoder_attention_mask.bool() if (encoder_attention_mask is not None and 0 in encoder_attention_mask) else None)
        else:
            if attention_mask is not None: self_attention_mask = self_attention_mask * attention_mask.view(batch_size, 1, -1).to(dtype=torch.bool, device=self_attention_mask.device)
            self_attention_mask = self_attention_mask.unsqueeze(2 if self.multi_query else 1)
            if self._use_sdpa and head_mask is None and not output_attentions:
                dtype = self.wte.weight.dtype
                min_dtype = torch.finfo(dtype).min
                self_attention_mask = torch.where(self_attention_mask, torch.full([], 0.0, dtype=dtype, device=self_attention_mask.device), torch.full([], min_dtype, dtype=dtype, device=self_attention_mask.device))
                if self.multi_query: self_attention_mask = self_attention_mask.transpose(1, 2)
                if query_length > 1 and attention_mask is not None and attention_mask.device.type == "cuda": self_attention_mask = AttentionMaskConverter._unmask_unattended(self_attention_mask, min_dtype=min_dtype)
            attention_mask = self_attention_mask
            if (self.config.add_cross_attention and encoder_hidden_states is not None and encoder_attention_mask is not None):
                if encoder_attention_mask.dim() == 2: encoder_attention_mask.unsqueeze(1)
                assert encoder_attention_mask.dim() == 3
                encoder_attention_mask = encoder_attention_mask.bool().unsqueeze(2 if self.multi_query else 1)
            else: encoder_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
        if inputs_embeds is None: inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds
        if token_type_ids is not None:
            token_type_embeds = self.wte(token_type_ids)
            hidden_states = hidden_states + token_type_embeds
        hidden_states = self.drop(hidden_states)
        output_shape = input_shape + (hidden_states.size(-1),)
        presents = [] if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            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, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)
            else: outputs = block(hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions)
            hidden_states = outputs[0]
            if use_cache: presents.append(outputs[1])
            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
                if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
        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,)
        if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions)
@add_start_docstrings("The SAPIENS_CODE Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", SAPIENS_CODE_START_DOCSTRING)
class SapiensCodeForCausalLM(SapiensCodePreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.transformer = SapiensCodeModel(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.post_init()
    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, past_key_values=None, inputs_embeds=None, **kwargs):
        token_type_ids = kwargs.get("token_type_ids", None)
        if past_key_values:
            if self.config.multi_query: past_length = past_key_values[0].shape[1]
            else: past_length = past_key_values[0].shape[2]
            if input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = input_ids.shape[1] - 1
            input_ids = input_ids[:, remove_prefix_length:]
            if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)
        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] :]
        else: position_ids = None
        if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds}
        else: model_inputs = {"input_ids": input_ids}
        model_inputs.update({"past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids})
        return model_inputs
    def _get_initial_cache_position(self, input_ids, model_kwargs):
        past_length = 0
        if "past_key_values" in model_kwargs:
            if self.config.multi_query: past_length = model_kwargs["past_key_values"][0].shape[1]
            else: past_length = model_kwargs["past_key_values"][0].shape[2]
        if "inputs_embeds" in model_kwargs: cur_len = model_kwargs["inputs_embeds"].shape[1]
        else: cur_len = input_ids.shape[-1]
        model_kwargs["cache_position"] = torch.arange(past_length, cur_len, device=input_ids.device)
        return model_kwargs
    @add_start_docstrings_to_model_forward(SAPIENS_CODE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        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, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = transformer_outputs[0]
        lm_logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states,
        attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions)
    @staticmethod
    def _reorder_cache(past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: return tuple(layer_past.index_select(0, beam_idx.to(layer_past.device)) for layer_past in past_key_values)
@add_start_docstrings("""
    The SapiensCode Model transformer with a sequence classification head on top (linear layer).
    [`SapiensCodeForSequenceClassification`] 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).
    """, SAPIENS_CODE_START_DOCSTRING)
class SapiensCodeForSequenceClassification(SapiensCodePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = SapiensCodeModel(config)
        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
        self.post_init()
    @add_start_docstrings_to_model_forward(SAPIENS_CODE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = 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, sequence_length = input_ids.shape[:2]
        else: batch_size, sequence_length = inputs_embeds.shape[:2]
        assert (self.config.pad_token_id is not None or batch_size == 1), "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
        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,) + 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("SAPIENS_CODE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", SAPIENS_CODE_START_DOCSTRING)
class SapiensCodeForTokenClassification(SapiensCodePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = SapiensCodeModel(config)
        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: classifier_dropout = config.hidden_dropout
        else: classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(SAPIENS_CODE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = 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
        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]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1).to(logits.device))
        if not return_dict:
            output = (logits,) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_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.                                                                 #
    #################################################################################################################################################################################################
"""
