#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
#    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.

from typing import Dict

import transformers

DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "</s>"
DEFAULT_UNK_TOKEN = "</s>"

def prepare_llama_tokenizer_and_embedding(
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
    special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN),
):
    """prepare llama tokenizer and embedding.

    """

    if tokenizer.pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )

    tokenizer.add_special_tokens(
        {
            "eos_token": DEFAULT_EOS_TOKEN,
            "bos_token": DEFAULT_BOS_TOKEN,
            "unk_token": DEFAULT_UNK_TOKEN,
        }
    )

    return tokenizer


def smart_tokenizer_and_embedding_resize(
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
    special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN),
):
    """Resize tokenizer and embedding.

    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
    """

    if tokenizer.pad_token is None:
        num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
        model.resize_token_embeddings(len(tokenizer))

        if num_new_tokens > 0:
            input_embeddings = model.get_input_embeddings().weight.data
            output_embeddings = model.get_output_embeddings().weight.data

            input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
            output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)

            input_embeddings[-num_new_tokens:] = input_embeddings_avg
            output_embeddings[-num_new_tokens:] = output_embeddings_avg
            