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"""Utils for tokenization."""

import warnings

__all__ = ["hf_tokenizer", "hf_processor"]


def set_pad_token_id(tokenizer):
    """Set pad_token_id to eos_token_id if it is None.

    Args:
        tokenizer (transformers.PreTrainedTokenizer): The tokenizer to be set.

    """
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token_id = tokenizer.eos_token_id
        warnings.warn(f"tokenizer.pad_token_id is None. Now set to {tokenizer.eos_token_id}", stacklevel=1)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
        warnings.warn(f"tokenizer.pad_token is None. Now set to {tokenizer.eos_token}", stacklevel=1)


def hf_tokenizer(name_or_path, correct_pad_token=True, correct_gemma2=True, **kwargs):
    """Create a huggingface pretrained tokenizer which correctness handles eos and pad tokens.

    Args:

        name (str): The name of the tokenizer.
        correct_pad_token (bool): Whether to correct the pad token id.
        correct_gemma2 (bool): Whether to correct the gemma2 tokenizer.

    Returns:

        transformers.PreTrainedTokenizer: The pretrained tokenizer.

    """
    from transformers import AutoTokenizer

    if correct_gemma2 and isinstance(name_or_path, str) and "gemma-2-2b-it" in name_or_path:
        # the EOS token in gemma2 is ambiguious, which may worsen RL performance.
        # https://huggingface.co/google/gemma-2-2b-it/commit/17a01657f5c87135bcdd0ec7abb4b2dece04408a
        warnings.warn("Found gemma-2-2b-it tokenizer. Set eos_token and eos_token_id to <end_of_turn> and 107.", stacklevel=1)
        kwargs["eos_token"] = "<end_of_turn>"
        kwargs["eos_token_id"] = 107
    tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs)
    if correct_pad_token:
        set_pad_token_id(tokenizer)
    
    # Fix for Llama models without chat_template
    if isinstance(name_or_path, str) and ("llama" in name_or_path.lower() or "llama-3" in name_or_path.lower()):
        if tokenizer.chat_template is None:
            # Llama 3 chat template
            llama_chat_template = """{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"""
            tokenizer.chat_template = llama_chat_template
            warnings.warn(f"Added chat_template for Llama model: {name_or_path}", stacklevel=1)
        # tokenizer.padding_side = "left"

    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    
    return tokenizer


def hf_processor(name_or_path, **kwargs):
    """Create a huggingface processor to process multimodal data.

    Args:
        name_or_path (str): The name of the processor.

    Returns:
        transformers.ProcessorMixin: The pretrained processor.
    """
    from transformers import AutoProcessor

    try:
        processor = AutoProcessor.from_pretrained(name_or_path, **kwargs)
    except Exception as e:
        processor = None
        # TODO(haibin.lin): try-catch should be removed after adding transformer version req to setup.py to avoid
        # silent failure
        warnings.warn(f"Failed to create processor: {e}. This may affect multimodal processing", stacklevel=1)
    # Avoid load tokenizer, see:
    # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/auto/processing_auto.py#L344
    if processor is not None and "Processor" not in processor.__class__.__name__:
        processor = None
    return processor
