# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, TypeVar, Union

from huggingface_hub import ModelHubMixin, hf_hub_download

# Generic variable that is either ModelHubMixin or a subclass thereof
T = TypeVar("T", bound="ModelHubMixin")

TEMPLATE_FILENAME = "dialogue_template.json"
IGNORE_INDEX = -100


@dataclass
class DialogueTemplate(ModelHubMixin):
    """Converts all turns of a dialogue between a user and assistant to a standardized format.

    Adapted from OpenAI's ChatML (https://github.com/openai/openai-python/blob/main/chatml.md) and Vicuna (https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py)
    """

    system: str
    messages: List[Dict[str, str]] = None
    system_token: str = "<|system|>"
    user_token: str = "<|user|>"
    assistant_token: str = "<|assistant|>"
    end_token: str = "<|end|>"

    def get_training_prompt(self) -> str:
        prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
        if self.messages is None:
            raise ValueError("Dialogue template must have at least one message.")
        for message in self.messages:
            if message["role"] == "user":
                prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
            else:
                prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
        return prompt

    def get_inference_prompt(self) -> str:
        prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
        if self.messages is None:
            raise ValueError("Dialogue template must have at least one message.")
        for message in self.messages:
            if message["role"] == "user":
                prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
            else:
                prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
        prompt += self.assistant_token + "\n"
        return prompt

    def get_dialogue(self):
        """Helper function to format the messages as an easy-to-read dialogue."""
        prompt = ""
        if self.messages is None:
            raise ValueError("Dialogue template must have at least one message.")
        for message in self.messages:
            if message["role"] == "user":
                prompt += "\n\nHuman: " + message["content"]
            else:
                prompt += "\n\nAssistant: " + message["content"]
        return prompt

    def get_special_tokens(self) -> List[str]:
        return [self.system_token, self.user_token, self.assistant_token, self.end_token]

    def copy(self):
        return DialogueTemplate(
            system=self.system,
            messages=self.messages,
            system_token=self.system_token,
            user_token=self.user_token,
            assistant_token=self.assistant_token,
            end_token=self.end_token,
        )

    def to_dict(self) -> Dict[str, Any]:
        return {k: v for k, v in asdict(self).items()}

    @classmethod
    def from_dict(cls, data):
        return DialogueTemplate(
            system=data["system"] if "system" in data else "",
            messages=data["messages"] if "messages" in data else None,
            system_token=data["system_token"] if "system_token" in data else "<|system|>",
            user_token=data["user_token"] if "user_token" in data else "<|user|>",
            assistant_token=data["assistant_token"] if "assistant_token" in data else "<|assistant|>",
            end_token=data["end_token"] if "end_token" in data else "<|end|>",
        )

    def _save_pretrained(self, save_directory: Union[str, Path]) -> None:
        save_directory = Path(save_directory)
        save_directory.mkdir(exist_ok=True)
        with open(save_directory / "dialogue_template.json", "w") as f:
            json.dump(self.to_dict(), f, indent=2)

    @classmethod
    def _from_pretrained(
        cls: Type[T],
        *,
        model_id: str,
        revision: Optional[str],
        cache_dir: Optional[Union[str, Path]],
        force_download: bool,
        proxies: Optional[Dict],
        resume_download: bool,
        local_files_only: bool,
        token: Optional[Union[str, bool]],
        **model_kwargs,
    ) -> T:
        """Loads the dialogue template from a local directory or the Huggingface Hub.

        Args:
            model_id (`str`):
                ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
            revision (`str`, *optional*):
                Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
                latest commit on `main` branch.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
                the existing cache.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`).
            token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
                cached when running `huggingface-cli login`.
            cache_dir (`str`, `Path`, *optional*):
                Path to the folder where cached files are stored.
            local_files_only (`bool`, *optional*, defaults to `False`):
                If `True`, avoid downloading the file and return the path to the local cached file if it exists.
            model_kwargs:
                Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
        """
        if os.path.isdir(model_id):  # Can either be a local directory
            print("Loading dialogue template from local directory")
            template_file = os.path.join(model_id, TEMPLATE_FILENAME)
        else:  # Or a template on the Hub
            template_file = hf_hub_download(  # Download from the hub, passing same input args
                repo_id=model_id,
                filename=TEMPLATE_FILENAME,
                revision=revision,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                resume_download=resume_download,
                token=token,
                local_files_only=local_files_only,
            )

        # Load template
        with open(template_file, "r") as f:
            data = json.load(f)
        return cls.from_dict(data=data)


# A shortened version of the system message in Anthropic's HHH prompt: https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt
default_template = DialogueTemplate(
    system="Below is a dialogue between a human user and an AI assistant. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.",
)

# OpenAI and OpenAssistant train on few to no system messages.
# TODO: consider defining this as the `default` template
no_system_template = DialogueTemplate(
    system="",
)

alpaca_template = DialogueTemplate(
    system="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
    user_token="### Instruction:",
    assistant_token="### Response:",
)

SUPPORTED_DIALOGUE_TEMPLATES = {
    "default": default_template,
    "no_system": no_system_template,
    "alpaca": alpaca_template,
}


def get_dialogue_template(template: str) -> DialogueTemplate:
    if template not in SUPPORTED_DIALOGUE_TEMPLATES.keys():
        raise ValueError(f"Template {template} is not supported!")
    return SUPPORTED_DIALOGUE_TEMPLATES[template].copy()


def prepare_dialogue(example, dialogue_template, is_train=True):
    """Format example to single- or multi-turn dialogue."""
    # TODO: make this simpler by just ensuring every dataset has a messages column
    if "messages" in example.keys() and example["messages"] is not None:
        dialogue_template.messages = example["messages"]
    elif all(k in example.keys() for k in ("prompt", "completion")):
        # Construct single-turn dialogue from prompt and completion
        dialogue_template.messages = [
            {"role": "user", "content": example["prompt"]},
            {"role": "assistant", "content": example["completion"]},
        ]
    elif "prompt" in example.keys():
        # Construct single-turn dialogue from prompt (inference only)
        dialogue_template.messages = [
            {"role": "user", "content": example["prompt"]},
        ]
    else:
        raise ValueError(
            f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}"
        )
    if is_train:
        example["text"] = dialogue_template.get_training_prompt()
    else:
        example["text"] = dialogue_template.get_inference_prompt()
    return example


def mask_user_labels(tokenizer, dialogue_template, labels):
    """Masks the user turns of a dialogue from the loss"""
    user_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.user_token)
    assistant_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.assistant_token)
    for idx, label_id in enumerate(labels):
        if label_id == user_token_id:
            current_idx = idx
            while labels[current_idx] != assistant_token_id and current_idx < len(labels):
                labels[current_idx] = IGNORE_INDEX
                current_idx += 1