import json

SYSTEM_PROMPT = "You are a helpful assistant that provide concise and accurate answers."

def set_cora_preset():
    return (
        "gsarti/cora_mgen",  # model_name_or_path
        "<Q>:{current} <P>:{context}",  # input_template
        "<Q>:{current}",  # input_current_text_template
    )


def set_default_preset():
    return (
        "gpt2",  # model_name_or_path
        "{current} {context}",  # input_template
        "{current}",  # output_template
        "{current}",  # contextless_input_template
        "{current}",  # contextless_output_template
        [],  # special_tokens_to_keep
        "",  # decoder_input_output_separator
        "{}",  # model_kwargs
        "{}",  # tokenizer_kwargs
        "{}",  # generation_kwargs
        "{}",  # attribution_kwargs
    )


def set_zephyr_preset():
    return (
        "stabilityai/stablelm-2-zephyr-1_6b",  # model_name_or_path
        "<|system|>{system_prompt}<|endoftext|>\n<|user|>\n{context}\n\n{current}<|endoftext|>\n<|assistant|>".format(system_prompt=SYSTEM_PROMPT),  # input_template
        "<|system|>{system_prompt}<|endoftext|>\n<|user|>\n{current}<|endoftext|>\n<|assistant|>".format(system_prompt=SYSTEM_PROMPT),  # input_current_text_template
        "\n",  # decoder_input_output_separator
        ["<|im_start|>", "<|im_end|>", "<|endoftext|>"],  # special_tokens_to_keep
    )


def set_chatml_preset():
    return (
        "Qwen/Qwen1.5-0.5B-Chat",  # model_name_or_path
        "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{context}\n\n{current}<|im_end|>\n<|im_start|>assistant".format(system_prompt=SYSTEM_PROMPT),  # input_template
        "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{current}<|im_end|>\n<|im_start|>assistant".format(system_prompt=SYSTEM_PROMPT),  # input_current_text_template
        "\n",  # decoder_input_output_separator
        ["<|im_start|>", "<|im_end|>"],  # special_tokens_to_keep
    )


def set_mmt_preset():
    return (
        "facebook/mbart-large-50-one-to-many-mmt",  # model_name_or_path
        "{context} {current}",  # input_template
        "{context} {current}",  # output_template
        '{\n\t"src_lang": "en_XX",\n\t"tgt_lang": "fr_XX"\n}',  # tokenizer_kwargs
    )


def set_towerinstruct_preset():
    return (
        "Unbabel/TowerInstruct-7B-v0.1",  # model_name_or_path
        "<|im_start|>user\nSource: {current}\nContext: {context}\nTranslate the above text into French. Use the context to guide your answer.\nTarget:<|im_end|>\n<|im_start|>assistant",  # input_template
        "<|im_start|>user\nSource: {current}\nTranslate the above text into French.\nTarget:<|im_end|>\n<|im_start|>assistant",  # input_current_text_template
        "\n",  # decoder_input_output_separator
        ["<|im_start|>", "<|im_end|>"],  # special_tokens_to_keep
    )

def set_gemma_preset():
    return (
        "google/gemma-2b-it", # model_name_or_path
        "<start_of_turn>user\n{context}\n{current}<end_of_turn>\n<start_of_turn>model", # input_template
        "<start_of_turn>user\n{current}<end_of_turn>\n<start_of_turn>model", # input_current_text_template
        "\n", # decoder_input_output_separator
        ["<start_of_turn>", "<end_of_turn>"], # special_tokens_to_keep
    )

def set_mistral_instruct_preset():
    return (
        "mistralai/Mistral-7B-Instruct-v0.2" # model_name_or_path
        "[INST]{context}\n{current}[/INST]" # input_template
        "[INST]{current}[/INST]" # input_current_text_template
        "\n" # decoder_input_output_separator
    )

def update_code_snippets_fn(
    input_current_text: str,
    input_context_text: str,
    output_current_text: str,
    output_context_text: str,
    model_name_or_path: str,
    attribution_method: str,
    attributed_fn: str | None,
    context_sensitivity_metric: str,
    context_sensitivity_std_threshold: float,
    context_sensitivity_topk: int,
    attribution_std_threshold: float,
    attribution_topk: int,
    input_template: str,
    output_template: str,
    contextless_input_template: str,
    contextless_output_template: str,
    special_tokens_to_keep: str | list[str] | None,
    decoder_input_output_separator: str,
    model_kwargs: str,
    tokenizer_kwargs: str,
    generation_kwargs: str,
    attribution_kwargs: str,
) -> tuple[str, str]:
    if not input_current_text:
        input_current_text = "<MISSING INPUT CURRENT TEXT, REQUIRED>"
    nl = "\n"
    tq = "\"\"\""
    def escape_quotes(s: str) -> str:
        return s.replace('"', '\\"')
    def py_get_kwargs_str(kwargs: str, name: str, pad: str = " " * 4) -> str:
        kwargs_dict = json.loads(kwargs)
        return nl + pad + name + '=' + str(kwargs_dict) + ',' if kwargs_dict else ''
    def py_get_if_specified(arg: str | int | float | list | None, name: str, pad: str = " " * 4) -> str:
        if arg is None or (isinstance(arg, (str, list)) and not arg) or (isinstance(arg, (int, float)) and arg <= 0):
            return ""
        elif isinstance(arg, str):
            return nl + pad + name + "=" + tq + arg + tq + ","
        elif isinstance(arg, list):
            return nl + pad + name + "=" + str(arg) + ","
        else:
            return nl + pad + name + "=" + str(arg) + ","
    def sh_get_kwargs_str(kwargs: str, name: str, pad: str = " " * 4) -> str:
        return nl + pad + f"--{name} " + '"' + escape_quotes("".join(x.strip() for x in str(kwargs).split("\n"))) + '"' + " \\" if json.loads(kwargs) else ''
    def sh_get_if_specified(arg: str | int | float | list | None, name: str, pad: str = " " * 4) -> str:
        if arg is None or (isinstance(arg, (str, list)) and not arg) or (isinstance(arg, (int, float)) and arg <= 0):
            return ""
        elif isinstance(arg, str):
            return nl + pad + f"--{name} " + '"' + escape_quotes(arg) + '"' + " \\" 
        elif isinstance(arg, list):
            return nl + pad + f"--{name} " + " ".join(str(arg)) + " \\"
        else:
            return nl + pad + f"--{name} " + str(arg) + " \\"
    # Python
    python = f"""#!pip install inseq
import inseq
from inseq.commands.attribute_context import attribute_context_with_model

inseq_model = inseq.load_model(
    "{model_name_or_path}",
    "{attribution_method}",{py_get_kwargs_str(model_kwargs, "model_kwargs")}{py_get_kwargs_str(tokenizer_kwargs, "tokenizer_kwargs")}
)

pecore_args = AttributeContextArgs(
    model_name_or_path="{model_name_or_path}",
    attribution_method="{attribution_method}",
    attributed_fn="{attributed_fn}",
    context_sensitivity_metric="{context_sensitivity_metric}",
    context_sensitivity_std_threshold={context_sensitivity_std_threshold},{py_get_if_specified(context_sensitivity_topk, "context_sensitivity_topk")}
    attribution_std_threshold={attribution_std_threshold},{py_get_if_specified(attribution_topk, "attribution_topk")}
    input_current_text=\"\"\"{input_current_text}\"\"\",{py_get_if_specified(input_context_text, "input_context_text")}
    contextless_input_current_text=\"\"\"{contextless_input_template}\"\"\",
    input_template=\"\"\"{input_template}\"\"\",{py_get_if_specified(output_current_text, "output_current_text")}{py_get_if_specified(output_context_text, "output_context_text")}
    contextless_output_current_text=\"\"\"{contextless_output_template}\"\"\",
    output_template="{output_template}",{py_get_if_specified(special_tokens_to_keep, "special_tokens_to_keep")}{py_get_if_specified(decoder_input_output_separator, "decoder_input_output_separator")}
    save_path="pecore_output.json",
    viz_path="pecore_output.html",{py_get_kwargs_str(model_kwargs, "model_kwargs")}{py_get_kwargs_str(tokenizer_kwargs, "tokenizer_kwargs")}{py_get_kwargs_str(generation_kwargs, "generation_kwargs")}{py_get_kwargs_str(attribution_kwargs, "attribution_kwargs")}
)

out = attribute_context_with_model(pecore_args, loaded_model)"""
    # Bash
    bash = f"""# pip install inseq
inseq attribute-context \\
    --model_name_or_path "{model_name_or_path}" \\
    --attribution_method "{attribution_method}" \\
    --attributed_fn "{attributed_fn}" \\
    --context_sensitivity_metric "{context_sensitivity_metric}" \\
    --context_sensitivity_std_threshold {context_sensitivity_std_threshold} \\{sh_get_if_specified(context_sensitivity_topk, "context_sensitivity_topk")}
    --attribution_std_threshold {attribution_std_threshold} \\{sh_get_if_specified(attribution_topk, "attribution_topk")}
    --input_current_text "{escape_quotes(input_current_text)}" \\{sh_get_if_specified(input_context_text, "input_context_text")}
    --contextless_input_current_text "{escape_quotes(contextless_input_template)}" \\
    --input_template "{escape_quotes(input_template)}" \\{sh_get_if_specified(output_current_text, "output_current_text")}{sh_get_if_specified(output_context_text, "output_context_text")}
    --contextless_output_current_text "{escape_quotes(contextless_output_template)}" \\
    --output_template "{escape_quotes(output_template)}" \\{sh_get_if_specified(special_tokens_to_keep, "special_tokens_to_keep")}{sh_get_if_specified(decoder_input_output_separator, "decoder_input_output_separator")}
    --save_path "pecore_output.json" \\
    --viz_path "pecore_output.html" \\{sh_get_kwargs_str(model_kwargs, "model_kwargs")}{sh_get_kwargs_str(tokenizer_kwargs, "tokenizer_kwargs")}{sh_get_kwargs_str(generation_kwargs, "generation_kwargs")}{sh_get_kwargs_str(attribution_kwargs, "attribution_kwargs")}""".strip("\\")
    return python, bash