"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_fsmt import FSMTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/wmt19-ru-en"
_CONFIG_FOR_DOC = "FSMTConfig"
FSMT_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 ([`FSMTConfig`]): 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.
"""
FSMT_GENERATION_EXAMPLE = r"""
    Translation example::
    ```python
    >>> from sapiens_transformers import AutoTokenizer, FSMTForConditionalGeneration
    >>> mname = "facebook/wmt19-ru-en"
    >>> model = FSMTForConditionalGeneration.from_pretrained(mname)
    >>> tokenizer = AutoTokenizer.from_pretrained(mname)
    >>> src_text = "Машинное обучение - это здорово, не так ли?"
    >>> input_ids = tokenizer(src_text, return_tensors="pt").input_ids
    >>> outputs = model.generate(input_ids, num_beams=5, num_return_sequences=3)
    >>> tokenizer.decode(outputs[0], skip_special_tokens=True)
    "Machine learning is great, isn't it?"
    ```
"""
FSMT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`FSTMTokenizer`]. 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)
            FSMT uses the `eos_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.BoolTensor` 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(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)` 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(torch.FloatTensor)` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up 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*, defaults to `True`):
            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.
"""
def invert_mask(attention_mask):
    assert attention_mask.dim() == 2
    return attention_mask.eq(0)
def triu_onnx(x, diagonal=0):
    l = x.shape[0]
    arange = torch.arange(l, device=x.device)
    mask = arange.expand(l, l)
    arange = arange.unsqueeze(-1)
    if diagonal: arange = arange + diagonal
    mask = mask >= arange
    return x.masked_fill(mask == 0, 0)
def _prepare_fsmt_decoder_inputs(config, input_ids, decoder_input_ids=None, decoder_padding_mask=None, causal_mask_dtype=torch.float32):
    pad_token_id = config.pad_token_id
    if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(input_ids, pad_token_id)
    bsz, tgt_len = decoder_input_ids.size()
    if decoder_padding_mask is None: decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id)
    else: decoder_padding_mask = invert_mask(decoder_padding_mask)
    causal_mask = triu_onnx(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len, dtype=causal_mask_dtype)), 1).to(device=decoder_input_ids.device)
    return decoder_input_ids, decoder_padding_mask, causal_mask
class PretrainedFSMTModel(PreTrainedModel):
    config_class = FSMTConfig
    base_model_prefix = "model"
    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, SinusoidalPositionalEmbedding): 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}
        return dummy_inputs
def _make_linear_from_emb(emb):
    vocab_size, emb_size = emb.weight.shape
    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
    lin_layer.weight.data = emb.weight.data
    return lin_layer
def _check_shapes(shape_1, shape2):
    if shape_1 != shape2: raise AssertionError(f"shape mismatch: {shape_1} != {shape2}")
def shift_tokens_right(input_ids, pad_token_id):
    input_ids.masked_fill_(input_ids == -100, pad_token_id)
    prev_output_tokens = input_ids.clone()
    index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
    prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
    prev_output_tokens[:, 1:] = input_ids[:, :-1]
    return prev_output_tokens
def make_padding_mask(input_ids, padding_idx=1):
    padding_mask = input_ids.eq(padding_idx)
    if not padding_mask.any(): padding_mask = None
    return padding_mask
class EncoderLayer(nn.Module):
    def __init__(self, config: FSMTConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = Attention(self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout)
        self.self_attn_layer_norm = 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 = LayerNorm(self.embed_dim)
    def forward(self, x, encoder_padding_mask, layer_head_mask, output_attentions=False):
        residual = x
        x, attn_weights = self.self_attn(query=x, key=x, key_padding_mask=encoder_padding_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.self_attn_layer_norm(x)
        residual = x
        x = self.activation_fn(self.fc1(x))
        x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.final_layer_norm(x)
        return x, attn_weights
class FSMTEncoder(nn.Module):
    def __init__(self, config: FSMTConfig, embed_tokens):
        super().__init__()
        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop
        self.padding_idx = embed_tokens.padding_idx
        self.embed_tokens = embed_tokens
        embed_dim = embed_tokens.embedding_dim
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        self.embed_positions = SinusoidalPositionalEmbedding(config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx)
        self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.encoder_layers)])
    def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: torch.Tensor = None, head_mask: Optional[torch.Tensor] = None,
    output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        if attention_mask is not None: attention_mask = invert_mask(attention_mask)
        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:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
            embed_pos = self.embed_positions(input_ids)
        elif inputs_embeds is not None:
            inputs_embeds = inputs_embeds * self.embed_scale
            position_ids = inputs_embeds[:, :, 0].masked_fill(inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx)
            embed_pos = self.embed_positions(position_ids)
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        x = inputs_embeds + embed_pos
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = x.transpose(0, 1)
        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:
                x = x.transpose(0, 1)
                encoder_states += (x,)
                x = x.transpose(0, 1)
            dropout_probability = torch.rand([])
            if self.training and (dropout_probability < self.layerdrop): attn = None
            else: x, attn = encoder_layer(x, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions)
            if output_attentions: all_attentions = all_attentions + (attn,)
        x = x.transpose(0, 1)
        if output_hidden_states: encoder_states += (x,)
        if not return_dict: return tuple(v for v in [x, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions)
class DecoderLayer(nn.Module):
    def __init__(self, config: FSMTConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = Attention(embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
        self.encoder_attn = Attention(self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, encoder_decoder_attention=True)
        self.encoder_attn_layer_norm = 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 = LayerNorm(self.embed_dim)
    def forward(self, x, encoder_hidden_states, encoder_attn_mask=None, layer_state=None, causal_mask=None, layer_head_mask=None, cross_attn_layer_head_mask=None, decoder_padding_mask=None, output_attentions=False):
        residual = x
        if layer_state is None: layer_state = {}
        x, self_attn_weights = self.self_attn(query=x, key=x, layer_state=layer_state, key_padding_mask=decoder_padding_mask, attn_mask=causal_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.self_attn_layer_norm(x)
        residual = x
        assert self.encoder_attn.cache_key != self.self_attn.cache_key
        x, cross_attn_weights = self.encoder_attn(query=x, key=encoder_hidden_states, key_padding_mask=encoder_attn_mask, layer_state=layer_state, layer_head_mask=cross_attn_layer_head_mask, output_attentions=output_attentions)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.encoder_attn_layer_norm(x)
        residual = x
        x = self.activation_fn(self.fc1(x))
        x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.final_layer_norm(x)
        return (x, self_attn_weights, layer_state, cross_attn_weights)
class FSMTDecoder(nn.Module):
    def __init__(self, config: FSMTConfig, embed_tokens: nn.Embedding):
        super().__init__()
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = embed_tokens.padding_idx
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.embed_tokens = embed_tokens
        embed_dim = embed_tokens.embedding_dim
        self.embed_positions = SinusoidalPositionalEmbedding(config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx)
        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.decoder_layers)])
        if is_deepspeed_zero3_enabled():
            import deepspeed
            with deepspeed.zero.GatheredParameters(self.embed_tokens.weight, modifier_rank=None): embed_tokens_weight_shape = self.embed_tokens.weight.shape
        else: embed_tokens_weight_shape = self.embed_tokens.weight.shape
        self.output_projection = nn.Linear(embed_tokens_weight_shape[1], embed_tokens_weight_shape[0], bias=False)
        self.output_projection.weight = self.embed_tokens.weight
    def _tie_weights(self): self.embed_tokens.weight = self.output_projection.weight
    def forward(self, input_ids: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_padding_mask: torch.Tensor, decoder_padding_mask: torch.Tensor, decoder_causal_mask: torch.Tensor,
    head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None,
    use_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        if encoder_padding_mask is not None: encoder_padding_mask = invert_mask(encoder_padding_mask)
        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:
            positions = self.embed_positions(input_ids)
            if use_cache:
                input_ids = input_ids[:, -1:]
                positions = positions[:, -1:]
            x = self.embed_tokens(input_ids) * self.embed_scale
        elif inputs_embeds is not None:
            position_ids = inputs_embeds[:, :, 0].masked_fill(inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx)
            positions = self.embed_positions(position_ids)
            x = inputs_embeds * self.embed_scale
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        x += positions
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = x.transpose(0, 1)
        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attns = () if output_attentions else None
        next_decoder_cache = []
        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:
                x = x.transpose(0, 1)
                all_hidden_states += (x,)
                x = x.transpose(0, 1)
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop: continue
            layer_state = past_key_values[idx] if past_key_values is not None else None
            x, layer_self_attn, layer_past, layer_cross_attn = decoder_layer(x, encoder_hidden_states, encoder_attn_mask=encoder_padding_mask, decoder_padding_mask=decoder_padding_mask,
            layer_state=layer_state, causal_mask=decoder_causal_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), output_attentions=output_attentions)
            if use_cache: next_decoder_cache.append(layer_past.copy())
            if output_attentions:
                all_self_attns += (layer_self_attn,)
                all_cross_attns += (layer_cross_attn,)
        if output_hidden_states:
            x = x.transpose(0, 1)
            all_hidden_states += (x,)
            x = x.transpose(0, 1)
        x = x.transpose(0, 1)
        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
        x = self.output_projection(x)
        next_cache = next_decoder_cache if use_cache else None
        if not return_dict: return tuple(v for v in [x, next_cache, all_hidden_states, all_self_attns, all_cross_attns] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=x, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attns)
def _reorder_buffer(attn_cache, new_order):
    for k, input_buffer_k in attn_cache.items():
        if input_buffer_k is not None: attn_cache[k] = input_buffer_k.index_select(0, new_order)
    return attn_cache
class Attention(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True, encoder_decoder_attention=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
        self.scaling = self.head_dim**-0.5
        self.encoder_decoder_attention = encoder_decoder_attention
        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)
        self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"
    def _shape(self, tensor, seq_len, bsz): return tensor.contiguous().view(seq_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
    def forward(self, query, key: Optional[Tensor], key_padding_mask: Optional[Tensor] = None, layer_state: Optional[Dict[str, Optional[Tensor]]] = None, attn_mask: Optional[Tensor] = None,
    layer_head_mask: Optional[Tensor] = None, output_attentions=False) -> Tuple[Tensor, Optional[Tensor]]:
        static_kv: bool = self.encoder_decoder_attention
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        if layer_state is not None:
            saved_state = layer_state.get(self.cache_key, {})
            if "prev_key" in saved_state and static_kv: key = None
        else:
            saved_state = None
            layer_state = {}
        q = self.q_proj(query) * self.scaling
        if static_kv:
            if key is None: k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)
        else:
            k = self.k_proj(query)
            v = self.v_proj(query)
        q = self._shape(q, tgt_len, bsz)
        if k is not None: k = self._shape(k, -1, bsz)
        if v is not None: v = self._shape(v, -1, bsz)
        if saved_state is not None: k, v, key_padding_mask = self._use_saved_state(k, v, saved_state, key_padding_mask, static_kv, bsz)
        layer_state[self.cache_key] = {"prev_key": k.view(bsz, self.num_heads, -1, self.head_dim), "prev_value": v.view(bsz, self.num_heads, -1, self.head_dim), "prev_key_padding_mask": key_padding_mask if not static_kv else None}
        assert k is not None
        src_len = k.size(1)
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)
        if attn_mask is not None:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        if key_padding_mask is not None and key_padding_mask.dim() == 0: key_padding_mask = None
        assert key_padding_mask is None or key_padding_mask.size()[:2] == (bsz, src_len)
        if key_padding_mask is not None:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
            attn_weights = attn_weights.masked_fill(reshaped, torch.finfo(attn_weights.dtype).min)
            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:
            assert layer_head_mask.size() == (self.num_heads), 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)
        assert v is not None
        attn_output = torch.bmm(attn_probs, v)
        assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights_reshaped
    def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
        if "prev_key" in saved_state:
            _prev_key = saved_state["prev_key"]
            assert _prev_key is not None
            prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
            if static_kv: k = prev_key
            else:
                assert k is not None
                k = torch.cat([prev_key, k], dim=1)
        if "prev_value" in saved_state:
            _prev_value = saved_state["prev_value"]
            assert _prev_value is not None
            prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
            if static_kv: v = prev_value
            else:
                assert v is not None
                v = torch.cat([prev_value, v], dim=1)
        assert k is not None and v is not None
        prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
        if prev_key_padding_mask is not None:
            if static_kv: new_key_padding_mask = prev_key_padding_mask
            else: new_key_padding_mask = torch.cat([prev_key_padding_mask, key_padding_mask], dim=1)
        else: new_key_padding_mask = key_padding_mask
        return k, v, new_key_padding_mask
def fill_with_neg_inf(t): return t.float().fill_(torch.finfo(t.dtype).min).type_as(t)
def _get_shape(t): return getattr(t, "shape", None)
@add_start_docstrings("The bare FSMT Model outputting raw hidden-states without any specific head on top.", FSMT_START_DOCSTRING)
class FSMTModel(PretrainedFSMTModel):
    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
    def __init__(self, config: FSMTConfig):
        super().__init__(config)
        padding_idx = config.pad_token_id
        encoder_embed_tokens = nn.Embedding(config.src_vocab_size, config.d_model, padding_idx)
        decoder_embed_tokens = nn.Embedding(config.tgt_vocab_size, config.d_model, padding_idx)
        self.encoder = FSMTEncoder(config, encoder_embed_tokens)
        self.decoder = FSMTDecoder(config, decoder_embed_tokens)
        self.post_init()
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    def _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
            self._tie_or_clone_weights(self.decoder.output_projection, self.get_input_embeddings())
    @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = 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[Tuple[torch.FloatTensor]] = None,
    past_key_values: Optional[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
        if decoder_input_ids is None: use_cache = False
        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 not use_cache and input_ids is not None: decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_fsmt_decoder_inputs(self.config, input_ids, decoder_input_ids=decoder_input_ids, decoder_padding_mask=decoder_attention_mask, causal_mask_dtype=self.decoder.embed_tokens.weight.dtype)
        else: decoder_padding_mask, causal_mask = None, None
        if decoder_input_ids is None and decoder_inputs_embeds is None: raise ValueError("Make sure that `decoder_input_ids` or `decoder_inputs_embeds` are passed.")
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, 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(decoder_input_ids, encoder_outputs[0], attention_mask, decoder_padding_mask, decoder_causal_mask=causal_mask, inputs_embeds=decoder_inputs_embeds, head_mask=decoder_head_mask,
        cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, 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)
    def get_input_embeddings(self): return self.encoder.embed_tokens
    def set_input_embeddings(self, value): self.encoder.embed_tokens = value
    def get_output_embeddings(self): return self.decoder.embed_tokens
    def set_output_embeddings(self, value): self.decoder.embed_tokens = value
@add_start_docstrings("The FSMT Model with a language modeling head. Can be used for summarization.", FSMT_START_DOCSTRING)
class FSMTForConditionalGeneration(PretrainedFSMTModel, GenerationMixin):
    base_model_prefix = "model"
    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
    def __init__(self, config: FSMTConfig):
        super().__init__(config)
        base_model = FSMTModel(config)
        self.model = base_model
        self.post_init()
    @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
    @add_end_docstrings(FSMT_GENERATION_EXAMPLE)
    def forward(self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = 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[Tuple[torch.FloatTensor]] = None,
    past_key_values: Optional[Tuple[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None: use_cache = False
        outputs = self.model(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_inputs_embeds=decoder_inputs_embeds,
        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, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        lm_logits = outputs[0]
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.tgt_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, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None,
    use_cache=None, encoder_outputs=None, **kwargs):
        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)
    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = []
        for layer_past in past_key_values:
            layer_past_new = {attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()}
            reordered_past.append(layer_past_new)
        return reordered_past
    def get_encoder(self): return self.model.encoder
    def get_decoder(self): return self.model.decoder
    def get_output_embeddings(self): return self.model.decoder.embed_tokens
    def set_output_embeddings(self, value): self.model.decoder.embed_tokens = value
class SinusoidalPositionalEmbedding(nn.Embedding):
    def __init__(self, num_positions, embedding_dim, padding_idx): self.make_weight(num_positions, embedding_dim, padding_idx)
    def make_weight(self, num_positions, embedding_dim, padding_idx):
        weight = self.get_embedding(num_positions, embedding_dim, padding_idx)
        if not hasattr(self, "weight"): super().__init__(num_positions, embedding_dim, padding_idx, _weight=weight)
        else:
            weight = weight.to(dtype=self.weight.dtype, device=self.weight.device)
            self.weight = nn.Parameter(weight)
        self.weight.detach_()
        self.weight.requires_grad = False
    @staticmethod
    def get_embedding(num_embeddings, embedding_dim, padding_idx):
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1: emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None: emb[padding_idx, :] = 0
        return emb
    @staticmethod
    def make_positions(tensor, padding_idx: int):
        mask = tensor.ne(padding_idx).int()
        return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
    def forward(self, input, incremental_state: Optional[Any] = None, timestep: Optional[Tensor] = None):
        bsz, seq_len = input.shape[:2]
        max_pos = self.padding_idx + 1 + seq_len
        if max_pos > self.weight.size(0): self.make_weight(max_pos, self.embedding_dim, self.padding_idx)
        positions = self.make_positions(input, self.padding_idx)
        return super().forward(positions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
