"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import 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 _prepare_4d_causal_attention_mask
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,
logging, replace_return_docstrings)
from .configuration_opt import OPTConfig
if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
_SEQ_CLASS_EXPECTED_LOSS = 1.71
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
class OPTLearnedPositionalEmbedding(nn.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int):
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim)
    def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
        attention_mask = attention_mask.long()
        positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
        positions = positions[:, past_key_values_length:]
        return super().forward(positions + self.offset)
class OPTAttention(nn.Module):
    def __init__(self, config: OPTConfig, is_decoder: bool = False, **kwargs):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.dropout = config.attention_dropout
        self.enable_bias = config.enable_bias
        self.head_dim = self.embed_dim // self.num_heads
        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.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
    def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states) * self.scaling
        if is_cross_attention and past_key_value is not None:
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        if self.is_decoder: past_key_value = (key_states, value_states)
        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)
        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError(f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}")
        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}")
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device))
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        if attn_weights.dtype == torch.float16: attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
        else: attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,): raise ValueError(f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}")
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        if output_attentions:
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else: attn_weights_reshaped = None
        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.bmm(attn_probs, value_states)
        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}")
        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights_reshaped, past_key_value
class OptFlashAttention2(OPTAttention):
    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, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        is_cross_attention = key_value_states is not None
        bsz, _, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states)
        if is_cross_attention and past_key_value is not None:
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        if self.is_decoder: past_key_value = (key_states, value_states)
        query_length = query_states.shape[1]
        tgt_len = key_states.shape[-2]
        query_states = query_states.view(bsz, query_length, self.num_heads, self.head_dim)
        key_states = key_states.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim)
        value_states = value_states.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim)
        attn_dropout = self.dropout if self.training else 0.0
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype()
            elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype
            else: target_dtype = self.q_proj.weight.dtype
            logger.warning_once(f"The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in {target_dtype}.")
            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)
        attn_output = _flash_attention_forward(query_states, key_states, value_states, attention_mask, 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(bsz, query_length, self.num_heads * self.head_dim)
        attn_output = self.out_proj(attn_weights_reshaped)
        if not output_attentions: attn_weights_reshaped = None
        return attn_output, attn_weights_reshaped, past_key_value
OPT_ATTENTION_CLASSES = {"eager": OPTAttention, "flash_attention_2": OptFlashAttention2}
class OPTDecoderLayer(nn.Module):
    def __init__(self, config: OPTConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = OPT_ATTENTION_CLASSES[config._attn_implementation](config=config, is_decoder=True)
        self.do_layer_norm_before = config.do_layer_norm_before
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
        self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
        self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask,
        layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states_shape = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = (residual + hidden_states).view(hidden_states_shape)
        if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights,)
        if use_cache: outputs += (present_key_value,)
        return outputs
OPT_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 ([`OPTConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings("The bare OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING)
class OPTPreTrainedModel(PreTrainedModel):
    config_class = OPTConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["OPTDecoderLayer"]
    _supports_flash_attn_2 = True
    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
OPT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class OPTDecoder(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
        self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
        if config.word_embed_proj_dim != config.hidden_size: self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
        else: self.project_out = None
        if config.word_embed_proj_dim != config.hidden_size: self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
        else: self.project_in = None
        if config.do_layer_norm_before and not config._remove_final_layer_norm: self.final_layer_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine)
        else: self.final_layer_norm = None
        self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, value): self.embed_tokens = value
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[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) -> 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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        batch_size, seq_length = input_shape
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        mask_seq_length = past_key_values_length + seq_length
        if self._use_flash_attention_2:
            causal_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
            attention_mask = (torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if attention_mask is None else attention_mask)
        else:
            if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
            elif attention_mask.shape[1] != mask_seq_length: raise ValueError(f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be {mask_seq_length} (sum of the lengths of current and past inputs)")
            causal_attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length)
        pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
        if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds)
        hidden_states = inputs_embeds + pos_embeds
        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
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None
        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != (len(self.layers)): raise ValueError(f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop: continue
            past_key_value = past_key_values[idx] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(decoder_layer.__call__, hidden_states, causal_attention_mask, head_mask[idx] if head_mask is not None else None,
                None, output_attentions, use_cache)
            else:
                layer_outputs = decoder_layer(hidden_states, attention_mask=causal_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
            if output_attentions: all_self_attns += (layer_outputs[1],)
        if self.final_layer_norm is not None: hidden_states = self.final_layer_norm(hidden_states)
        if self.project_out is not None: hidden_states = self.project_out(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        next_cache = next_decoder_cache if use_cache else None
        if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns)
@add_start_docstrings("The bare OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING)
class OPTModel(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.decoder = OPTDecoder(config)
        self.post_init()
    def get_input_embeddings(self): return self.decoder.embed_tokens
    def set_input_embeddings(self, value): self.decoder.embed_tokens = value
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[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) -> 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
        decoder_outputs = self.decoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        if not return_dict: return decoder_outputs
        return BaseModelOutputWithPast(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions)
class OPTForCausalLM(OPTPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.model = OPTModel(config)
        self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
        self.post_init()
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def set_decoder(self, decoder): self.model.decoder = decoder
    def get_decoder(self): return self.model.decoder
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[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, CausalLMOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.model.decoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        logits = self.lm_head(outputs[0]).contiguous()
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs):
        if past_key_values is not None:
            past_length = past_key_values[0][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 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"), "attention_mask": attention_mask})
        return model_inputs
    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
        return reordered_past
@add_start_docstrings("""
    The OPT Model transformer with a sequence classification head on top (linear layer).
    [`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) 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).
""", OPT_START_DOCSTRING)
class OPTForSequenceClassification(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = OPTModel(config)
        self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
        self.post_init()
    @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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.model(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, 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]
        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:
            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)
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value
@add_start_docstrings("The OPT 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`).", OPT_START_DOCSTRING)
class OPTForQuestionAnswering(OPTPreTrainedModel):
    def __init__(self, config: OPTConfig):
        super().__init__(config)
        self.model = OPTModel(config)
        self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
        self.post_init()
    @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None,
    end_positions: 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, QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        transformer_outputs = self.model(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, 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.qa_outputs(hidden_states)
        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)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index).to(logits.device)
            end_positions = end_positions.clamp(0, ignored_index).to(logits.device)
            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) + transformer_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=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
