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# SPDX-License-Identifier: Apache-2.0
#
# 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
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import List, Tuple

from transformers import PreTrainedTokenizer


def make_context(
    tokenizer: PreTrainedTokenizer,
    query: str,
    history: List[Tuple[str, str]] = None,
    system: str = "You are a helpful assistant.",
    max_input_length:
    int = 2048,  # if you want to change this, you need to change the max_input_len in tensorrt_llm_july-release-v1/examples/qwen/build.py
    max_window_size: int = 6144,
    chat_format: str = "chatml",
):
    if history is None:
        history = []

    if chat_format == "chatml":
        im_start, im_end = "<|im_start|>", "<|im_end|>"
        im_start_tokens = [tokenizer.im_start_id]
        im_end_tokens = [tokenizer.im_end_id]
        nl_tokens = tokenizer.encode("\n")

        def _tokenize_str(role, content):
            return (f"{role}\n{content}",
                    tokenizer.encode(
                        role,
                        allowed_special=set(),
                    ) + nl_tokens + tokenizer.encode(
                        content,
                        allowed_special=set(),
                    ))

        system_text, system_tokens_part = _tokenize_str("system", system)
        system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
        raw_text = ""
        context_tokens = []

        for turn_query, turn_response in reversed(history):
            query_text, query_tokens_part = _tokenize_str("user", turn_query)
            query_tokens = im_start_tokens + query_tokens_part + im_end_tokens

            response_text, response_tokens_part = _tokenize_str(
                "assistant", turn_response)
            response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
            next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
            prev_chat = (
                f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
            )

            current_context_size = (len(system_tokens) +
                                    len(next_context_tokens) +
                                    len(context_tokens))
            if current_context_size < max_window_size:
                context_tokens = next_context_tokens + context_tokens
                raw_text = prev_chat + raw_text
            else:
                break

        context_tokens = system_tokens + context_tokens
        raw_text = f"{im_start}{system_text}{im_end}" + raw_text
        context_tokens += (nl_tokens + im_start_tokens +
                           _tokenize_str("user", query)[1] + im_end_tokens +
                           nl_tokens + im_start_tokens +
                           tokenizer.encode("assistant") + nl_tokens)
        raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"

    elif chat_format == "raw":
        raw_text = query
        context_tokens = tokenizer.encode(raw_text)
    else:
        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
    # truncate to max_input_length, truncate from the front
    return raw_text, context_tokens[-max_input_length:]


def get_stop_words_ids(chat_format, tokenizer):
    if chat_format == "raw":
        stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
    elif chat_format == "chatml":
        stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
    else:
        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
    return stop_words_ids