"""
    #################################################################################################################################################################################################
    # 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 copy
import math
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_marian import MarianConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MarianConfig"
_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id
    if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.")
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
    return shifted_input_ids
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
        super().__init__(num_positions, embedding_dim)
        self.weight = self._init_weight(self.weight)
    @staticmethod
    def _init_weight(out: nn.Parameter) -> nn.Parameter:
        n_pos, dim = out.shape
        position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
        out.requires_grad = False
        sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
        out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
        out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
        out.detach_()
        return out
    @torch.no_grad()
    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
        bsz, seq_len = input_ids_shape[:2]
        positions = torch.arange(past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device)
        return super().forward(positions)
class MarianAttention(nn.Module):
    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[MarianConfig] = None):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config
        if (self.head_dim * 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`: {num_heads}).")
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=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 and past_key_value[0].shape[2] == key_value_states.shape[1]):
            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.reshape(*proj_shape)
        value_states = value_states.reshape(*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 = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        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 MarianEncoderLayer(nn.Module):
    def __init__(self, config: MarianConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, config=config)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
    def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool] = False) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
        residual = hidden_states
        hidden_states, attn_weights, _ = self.self_attn(hidden_states=hidden_states, 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
        hidden_states = self.self_attn_layer_norm(hidden_states)
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_weights,)
        return outputs
MARIAN_ATTENTION_CLASSES = {"eager": MarianAttention}
class MarianDecoderLayer(nn.Module):
    def __init__(self, config: MarianConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout,
        is_decoder=True, is_causal=True, config=config)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config)
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None,
    layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=self_attn_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
        hidden_states = self.self_attn_layer_norm(hidden_states)
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(hidden_states=hidden_states, key_value_states=encoder_hidden_states,
            attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions)
            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            present_key_value = present_key_value + cross_attn_present_key_value
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights, cross_attn_weights)
        if use_cache: outputs += (present_key_value,)
        return outputs
class MarianPreTrainedModel(PreTrainedModel):
    config_class = MarianConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
        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, MarianSinusoidalPositionalEmbedding): pass
        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_()
    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
        dummy_inputs = {"attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, "decoder_input_ids": input_ids}
        return dummy_inputs
MARIAN_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 ([`MarianConfig`]):
            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.
"""
model_generate = 'model.generate(**batch)'
MARIAN_GENERATION_EXAMPLE = r"""
    Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
    Examples:
    ```python
    >>> from sapiens_transformers import AutoTokenizer, MarianMTModel
    >>> src = "fr"  # source language
    >>> trg = "en"  # target language
    >>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
    >>> model = MarianMTModel.from_pretrained(model_name)
    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
    >>> sample_text = "où est l'arrêt de bus ?"
    >>> batch = tokenizer([sample_text], return_tensors="pt")
    >>> generated_ids = """+model_generate+r"""
    >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    "Where's the bus stop?"
    ```
"""
MARIAN_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)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids)
            Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        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*.
        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        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.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        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 MarianEncoder(MarianPreTrainedModel):
    def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop
        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        if embed_tokens is not None: self.embed_tokens = embed_tokens
        else: self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
        self.embed_positions = MarianSinusoidalPositionalEmbedding(config.max_position_embeddings, embed_dim, self.padding_idx)
        self.layers = nn.ModuleList([MarianEncoderLayer(config) for _ in range(config.encoder_layers)])
        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.LongTensor] = None, head_mask: Optional[torch.Tensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
        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
        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])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        embed_pos = self.embed_positions(input_shape)
        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        if attention_mask is not None: attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        if head_mask is not None: assert head_mask.size()[0] == (len(self.layers)), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop: to_drop = True
            if to_drop: layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), output_attentions)
                else: layer_outputs = encoder_layer(hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions)
                hidden_states = layer_outputs[0]
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
class MarianDecoder(MarianPreTrainedModel):
    def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        if embed_tokens is not None: self.embed_tokens = embed_tokens
        else: self.embed_tokens = nn.Embedding(config.decoder_vocab_size, config.d_model, self.padding_idx)
        self.embed_positions = MarianSinusoidalPositionalEmbedding(config.max_position_embeddings, config.d_model, self.padding_idx)
        self.layers = nn.ModuleList([MarianDecoderLayer(config) for _ in range(config.decoder_layers)])
        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, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None,
    past_key_values: Optional[Tuple[Tuple[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[torch.Tensor], 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 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")
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length)
        if encoder_hidden_states is not None and encoder_attention_mask is not None: encoder_attention_mask = _prepare_4d_attention_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
        positions = self.embed_positions(input_shape, past_key_values_length)
        hidden_states = inputs_embeds + positions
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        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
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None: assert attn_mask.size()[0] == (len(self.layers)), (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, attention_mask, encoder_hidden_states, encoder_attention_mask,
                head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, output_attentions, use_cache)
            else:
                layer_outputs = decoder_layer(hidden_states,cattention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
                layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_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[3 if output_attentions else 1],)
            if output_attentions:
                all_self_attns += (layer_outputs[1],)
                if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],)
        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, all_cross_attentions] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states,
        attentions=all_self_attns, cross_attentions=all_cross_attentions)
@add_start_docstrings("The bare Marian Model outputting raw hidden-states without any specific head on top.", MARIAN_START_DOCSTRING)
class MarianModel(MarianPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: MarianConfig):
        super().__init__(config)
        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
        if self.config.share_encoder_decoder_embeddings: encoder_embed_tokens = decoder_embed_tokens = self.shared
        else:
            encoder_embed_tokens = copy.deepcopy(self.shared)
            decoder_embed_tokens = copy.deepcopy(self.shared)
            self.shared = None
        self.encoder = MarianEncoder(config, encoder_embed_tokens)
        self.decoder = MarianDecoder(config, decoder_embed_tokens)
        self.post_init()
    def get_input_embeddings(self): return self.get_encoder().get_input_embeddings()
    def set_input_embeddings(self, value):
        if self.config.share_encoder_decoder_embeddings:
            self.shared = value
            self.encoder.embed_tokens = self.shared
            self.decoder.embed_tokens = self.shared
        else: self.encoder.embed_tokens = value
    def get_decoder_input_embeddings(self):
        if self.config.share_encoder_decoder_embeddings: raise ValueError("`get_decoder_input_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `get_input_embeddings` instead.")
        return self.get_decoder().get_input_embeddings()
    def set_decoder_input_embeddings(self, value):
        if self.config.share_encoder_decoder_embeddings: raise ValueError("`config.share_encoder_decoder_embeddings` is set to `True` meaning the decoder input embeddings are shared with the encoder. In order to set the decoder input embeddings, you should simply set the encoder input embeddings by calling `set_input_embeddings` with the appropriate embeddings.")
        self.decoder.embed_tokens = value
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
        if self.config.share_encoder_decoder_embeddings: raise ValueError("`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `resize_token_embeddings` instead.")
        old_embeddings = self.get_decoder_input_embeddings()
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
        self.set_decoder_input_embeddings(new_embeddings)
        model_embeds = self.get_decoder_input_embeddings()
        if new_num_tokens is None: return model_embeds
        self.config.decoder_vocab_size = new_num_tokens
        self.tie_weights()
        return model_embeds
    @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None,
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    decoder_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) -> Seq2SeqModelOutput:
        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 encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions,
            output_hidden_states=output_hidden_states, return_dict=return_dict)
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
            attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask,
        head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_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 + encoder_outputs
        return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
@add_start_docstrings("The Marian Model with a language modeling head. Can be used for summarization.", MARIAN_START_DOCSTRING)
class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
    base_model_prefix = "model"
    _keys_to_ignore_on_load_missing = ["final_logits_bias", "encoder.embed_positions.weight", "decoder.embed_positions.weight"]
    _keys_to_ignore_on_save = ["model.encoder.embed_positions.weight", "model.decoder.embed_positions.weight"]
    _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
    def __init__(self, config: MarianConfig):
        super().__init__(config)
        self.model = MarianModel(config)
        target_vocab_size = config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
        self.register_buffer("final_logits_bias", torch.zeros((1, target_vocab_size)))
        self.lm_head = nn.Linear(config.d_model, target_vocab_size, bias=False)
        self.post_init()
    def get_encoder(self): return self.model.get_encoder()
    def get_decoder(self): return self.model.get_decoder()
    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        if self.config.share_encoder_decoder_embeddings: self._resize_final_logits_bias(new_num_tokens)
        return new_embeddings
    def _resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of=None) -> nn.Embedding:
        old_embeddings = self.get_input_embeddings()
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
        self.set_input_embeddings(new_embeddings)
        new_num_tokens = new_embeddings.weight.shape[0]
        if self.config.share_encoder_decoder_embeddings: self.config.decoder_vocab_size = new_num_tokens
        if (self.config.share_encoder_decoder_embeddings and self.get_output_embeddings() is not None and not self.config.tie_word_embeddings):
            old_lm_head = self.get_output_embeddings()
            new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
            self.set_output_embeddings(new_lm_head)
        return self.get_input_embeddings()
    def resize_decoder_token_embeddings(self, new_num_tokens):
        if self.config.share_encoder_decoder_embeddings: raise ValueError("`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `resize_token_embeddings` instead.")
        old_embeddings = self.model.get_decoder_input_embeddings()
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
        self.model.set_decoder_input_embeddings(new_embeddings)
        if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
            old_lm_head = self.get_output_embeddings()
            new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
            self.set_output_embeddings(new_lm_head)
        model_embeds = self.model.get_decoder_input_embeddings()
        if new_num_tokens is None: return model_embeds
        self.config.decoder_vocab_size = new_num_tokens
        self.tie_weights()
        self._resize_final_logits_bias(new_num_tokens)
        return model_embeds
    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
        old_num_tokens = self.final_logits_bias.shape[-1]
        if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens]
        else:
            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
        self.register_buffer("final_logits_bias", new_bias)
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings: nn.Embedding): self.lm_head = new_embeddings
    def tie_weights(self):
        output_embeddings = self.get_output_embeddings()
        if output_embeddings is not None and getattr(self.config, "tie_word_embeddings", True):
            word_embeddings = self.get_decoder().get_input_embeddings()
            self._tie_or_clone_weights(output_embeddings, word_embeddings)
        if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
            if hasattr(self, self.base_model_prefix): self = getattr(self, self.base_model_prefix)
            tied_weights = self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix, "encoder")
            self._dynamic_tied_weights_keys = tied_weights
        for module in self.modules():
            if hasattr(module, "_tie_weights"): module._tie_weights()
    @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
    @add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None,
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    decoder_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) -> Seq2SeqLMOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.decoder_vocab_size), labels.view(-1))
        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return Seq2SeqLMOutput(loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions)
    def prepare_inputs_for_generation(self, decoder_input_ids: torch.LongTensor, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None,
    head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None,
    encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None, **kwargs) -> Dict:
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            if decoder_input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = decoder_input_ids.shape[1] - 1
            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
        return {"input_ids": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask,
        "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache}
    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
    @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[:2]) + layer_past[2:],)
        return reordered_past
class MarianDecoderWrapper(MarianPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.decoder = MarianDecoder(config)
    def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs)
class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        config = copy.deepcopy(config)
        config.is_decoder = True
        config.is_encoder_decoder = False
        super().__init__(config)
        self.model = MarianDecoderWrapper(config)
        self.lm_head = nn.Linear(config.hidden_size, 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, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_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, CausalLMOutputWithCrossAttentions]:
        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, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask,
        head_mask=head_mask, cross_attn_head_mask=cross_attn_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])
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs):
        if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape)
        if past_key_values:
            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:]
        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache}
    @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
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
