# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (BaseModelOutputWithPast,
                                           CausalLMOutputWithPast)

from lmdeploy.pytorch.modeling.convert_to_qmodules import convert_to_qmodules
from lmdeploy.utils import get_logger

from .configuration_baichuan import BaiChuanConfig

logger = get_logger('lmdeploy')


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: torch.Size,
                      dtype: torch.dtype,
                      device: torch.device,
                      past_key_values_length: int = 0):
    """Make causal mask used for bi-directional self-attention."""
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len),
                      torch.tensor(torch.finfo(dtype).min, device=device),
                      device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([
            torch.zeros(
                tgt_len, past_key_values_length, dtype=dtype, device=device),
            mask
        ],
                         dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len,
                                         tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor,
                 dtype: torch.dtype,
                 tgt_len: Optional[int] = None):
    """Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len,
    src_seq_len]`."""
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len,
                                                  src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool),
                                     torch.finfo(dtype).min)


class RMSNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-6):
        """RMSNorm is equivalent to T5LayerNorm."""
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1,
                                                               keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance +
                                                    self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


class RotaryEmbedding(torch.nn.Module):
    """RotaryEmbedding for Baichuan Model.

    This module generates sine and cosine positional encodings based on
    the paper "RoFormer: Enhanced Transformer with Rotary Position Embedding".
    The purpose of this class is to provide positional embeddings to the
    input tensors. It utilizes a cache mechanism to store precomputed
    sine and cosine values for speedup.

    Args:
        dim (int): The dimensionality of the embeddings.
        max_position_embeddings (int, optional): The maximum number of
            position embeddings. Default is 2048.
        base (int, optional): The base value for the inverse frequency
            calculation. Default is 10000.
        device (str, optional): The device to run operations on.
            If None, defaults to the device of the model.
    """

    def __init__(self,
                 dim,
                 max_position_embeddings=2048,
                 base=10000,
                 device=None):
        super().__init__()
        index = (torch.arange(0, dim, 2).float().to(device) / dim)
        inv_freq = 1.0 / (base**index)
        self.register_buffer('inv_freq', inv_freq)

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings
        t = torch.arange(self.max_seq_len_cached,
                         device=self.inv_freq.device,
                         dtype=self.inv_freq.dtype)
        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order
        # to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer('cos_cached',
                             emb.cos()[None, None, :, :],
                             persistent=False)
        self.register_buffer('sin_cached',
                             emb.sin()[None, None, :, :],
                             persistent=False)

    def forward(self, x, seq_len=None):
        """Forward propagation method for the embedding layer.

        Generates positional embeddings for the given input tensor.
        """
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in
        # `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            self.max_seq_len_cached = seq_len
            t = torch.arange(self.max_seq_len_cached,
                             device=x.device,
                             dtype=self.inv_freq.dtype)
            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
            # Different from paper, but it uses a different permutation in
            # order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
            self.register_buffer('cos_cached',
                                 emb.cos()[None, None, :, :],
                                 persistent=False)
            self.register_buffer('sin_cached',
                                 emb.sin()[None, None, :, :],
                                 persistent=False)
        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
        )


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., :x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2:]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
    """Apply rotary positional embeddings to query and key tensors.

    This function applies the cosine and sine positional embeddings on the
    input query (q) and key (k) tensors using element-wise multiplication and
    addition.
    """
    # The first two dimensions of cos and sin are always 1,
    # so we can `squeeze` them.
    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class MLP(nn.Module):
    """MLP for Baichuan Model."""

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
    ):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper."""

    def __init__(self, config: BaiChuanConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.max_position_embeddings

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError('hidden_size must be divisible by num_heads '
                             f'(got `hidden_size`: {self.hidden_size}'
                             f' and `num_heads`: {self.num_heads}).')
        self.W_pack = nn.Linear(self.hidden_size,
                                3 * self.hidden_size,
                                bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim,
                                self.hidden_size,
                                bias=False)
        self.rotary_emb = RotaryEmbedding(
            self.head_dim,
            max_position_embeddings=self.max_position_embeddings)

    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,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
               Optional[Tuple[torch.Tensor]]]:
        """Forward propagation method for the attention layer."""
        bsz, q_len, _ = hidden_states.size()

        proj = self.W_pack(hidden_states)
        proj = proj.unflatten(-1,
                              (3, self.hidden_size)).unsqueeze(0).transpose(
                                  0, -2).squeeze(-2)
        query_states = proj[0].view(
            bsz, q_len, self.num_heads, self.head_dim).transpose(
                1, 2)  # batch_size x source_len x hidden_size
        key_states = proj[1].view(bsz, q_len,
                                  self.num_heads, self.head_dim).transpose(
                                      1,
                                      2)  # batch_size x target_len x head_size
        value_states = proj[2].view(
            bsz, q_len, self.num_heads, self.head_dim).transpose(
                1, 2)  # batch_size x source_len x hidden_size

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        attn_weights = torch.matmul(query_states, key_states.transpose(
            2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                'Attention weights should be of size '
                f'{(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
                f' {attn_weights.size()}')

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError('Attention mask should be of size '
                                 f'{(bsz, 1, q_len, kv_seq_len)},'
                                 f' but is {attention_mask.size()}')
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(
                attn_weights,
                torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights,
                                             dim=-1,
                                             dtype=torch.float32).to(
                                                 query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                '`attn_output` should be of size '
                f'{(bsz, self.num_heads, q_len, self.head_dim)}, but is'
                f' {attn_output.size()}')

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class DecoderLayer(nn.Module):
    """Decoder layer for Baichuan Model."""

    def __init__(self, config: BaiChuanConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Attention(config=config)
        self.mlp = MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
        )
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor,
                                                 torch.FloatTensor]]]:
        """  # noqa: E501
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
            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`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states, )

        if output_attentions:
            outputs += (self_attn_weights, )

        if use_cache:
            outputs += (present_key_value, )

        return outputs


class PreTrainedModel(PreTrainedModel):
    config_class = BaiChuanConfig
    base_model_prefix = 'model'
    supports_gradient_checkpointing = True
    _no_split_modules = ['DecoderLayer']
    _keys_to_ignore_on_load_unexpected = [r'decoder\.version']

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, Model):
            module.gradient_checkpointing = value


class Model(PreTrainedModel):
    """Transformer decoder consisting of *config.num_hidden_layers* layers.
    Each layer is a [`DecoderLayer`]

    Args:
        config: BaiChuanConfig
    """

    def __init__(self, config: BaiChuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size,
                                         self.padding_idx)
        self.layers = nn.ModuleList(
            [DecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder.
    # prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
                                        inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask,
                                              inputs_embeds.dtype,
                                              tgt_len=input_shape[-1]).to(
                                                  inputs_embeds.device)
            combined_attention_mask = (expanded_attn_mask
                                       if combined_attention_mask is None else
                                       expanded_attn_mask +
                                       combined_attention_mask)

        return combined_attention_mask

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = (output_attentions if output_attentions is not None
                             else self.config.output_attentions)
        output_hidden_states = (output_hidden_states
                                if output_hidden_states is not None else
                                self.config.output_hidden_states)
        use_cache = (use_cache
                     if use_cache is not None else self.config.use_cache)

        return_dict = (return_dict if return_dict is not None else
                       self.config.use_return_dict)

        # retrieve input_ids and inputs_embeds
        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:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError('You have to specify either decoder_input_ids '
                             'or decoder_inputs_embeds')

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = (seq_length_with_past +
                                    past_key_values_length)

        if position_ids is None:
            device = (input_ids.device
                      if input_ids is not None else inputs_embeds.device)
            position_ids = torch.arange(past_key_values_length,
                                        seq_length + past_key_values_length,
                                        dtype=torch.long,
                                        device=device)
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past),
                                        dtype=torch.bool,
                                        device=inputs_embeds.device)
        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds,
            past_key_values_length)

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    '`use_cache=True` is incompatible with gradient '
                    'checkpointing. Setting `use_cache=False`...')
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states, )

            past_key_value = past_key_values[
                idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):

                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (
                    layer_outputs[2 if output_attentions else 1], )

            if output_attentions:
                all_self_attns += (layer_outputs[1], )

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states, )

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v for v in
                [hidden_states, next_cache, all_hidden_states, all_self_attns]
                if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class BaiChuanForCausalLM(PreTrainedModel):
    """This class extends the `PreTrainedModel` to enable causal language
    modeling.

    It wraps the basic Baichuan model (`Model`) and includes a linear layer as
    a language model head (`lm_head`). The purpose is to predict token
    probabilities, given the previous tokens in the sequence.
    """

    def __init__(self, config):
        super().__init__(config)
        self.model = Model(config)

        self.lm_head = nn.Linear(config.hidden_size,
                                 config.vocab_size,
                                 bias=False)

        # Initialize weights and apply final processing
        self.post_init()
        convert_to_qmodules(self)

    def get_input_embeddings(self):
        """Get the token embedding layer."""
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        """Set the token embedding layer."""
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        """Get the output embedding layer."""
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        """Set the output embedding layer."""
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        """Set the decoder model."""
        self.model = decoder

    def get_decoder(self):
        """Get the decoder model."""
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""  # noqa: E501
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, ModelForCausalLM

        >>> model = ModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you consciours? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
        ```"""

        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)

        # decoder outputs consists of
        # (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            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,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits, ) + outputs[1:]
            return (loss, ) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(self,
                                      input_ids,
                                      past_key_values=None,
                                      attention_mask=None,
                                      inputs_embeds=None,
                                      **kwargs):
        """Prepare inputs for generating sequences using the model.

        Args:
            input_ids (torch.Tensor): Input token ids.
            past_key_values (list[torch.Tensor], optional): List of past key
                and value states.
            attention_mask (torch.Tensor, optional): Mask indicating which
                tokens should be attended to.
            inputs_embeds (torch.FloatTensor, optional): Optionally,
                the input embeddings instead of token ids.

        Returns:
            dict: Dictionary containing prepared inputs for model generation.
        """
        if past_key_values:
            input_ids = input_ids[:, -1:]

        position_ids = kwargs.get('position_ids', None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed,
        # we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {'inputs_embeds': inputs_embeds}
        else:
            model_inputs = {'input_ids': input_ids}

        model_inputs.update({
            'position_ids': position_ids,
            'past_key_values': past_key_values,
            'use_cache': kwargs.get('use_cache'),
            'attention_mask': attention_mask,
        })
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        """Reorder cached past key-values during generation using beam search.

        This function reorders the cached past key-values according to the
        given indices. It's useful in beam search where the order of hypotheses
        can change from one time-step to another.
        """
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (tuple(
                past_state.index_select(0, beam_idx)
                for past_state in layer_past), )
        return reordered_past
