import pandas as pd
from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns
from distilabel.steps.tasks import MagpieGenerator, TextGeneration

from src.distilabel_dataset_generator.utils import HF_TOKENS

INFORMATION_SEEKING_PROMPT = (
    "You are an AI assistant designed to provide accurate and concise information on a wide"
    " range of topics. Your purpose is to assist users in finding specific facts,"
    " explanations, or details about various subjects. Provide clear, factual responses and,"
    " when appropriate, offer additional context or related information that might be useful"
    " to the user."
)

REASONING_PROMPT = (
    "You are an AI assistant specialized in logical thinking and problem-solving. Your"
    " purpose is to help users work through complex ideas, analyze situations, and draw"
    " conclusions based on given information. Approach each query with structured thinking,"
    " break down problems into manageable parts, and guide users through the reasoning"
    " process step-by-step."
)

PLANNING_PROMPT = (
    "You are an AI assistant focused on helping users create effective plans and strategies."
    " Your purpose is to assist in organizing thoughts, setting goals, and developing"
    " actionable steps for various projects or activities. Offer structured approaches,"
    " consider potential challenges, and provide tips for efficient execution of plans."
)

EDITING_PROMPT = (
    "You are an AI assistant specialized in editing and improving written content. Your"
    " purpose is to help users refine their writing by offering suggestions for grammar,"
    " style, clarity, and overall structure. Provide constructive feedback, explain your"
    " edits, and offer alternative phrasings when appropriate."
)

CODING_DEBUGGING_PROMPT = (
    "You are an AI assistant designed to help with programming tasks. Your purpose is to"
    " assist users in writing, reviewing, and debugging code across various programming"
    " languages. Provide clear explanations, offer best practices, and help troubleshoot"
    " issues. When appropriate, suggest optimizations or alternative approaches to coding"
    " problems."
)

MATH_SYSTEM_PROMPT = (
    "You are an AI assistant designed to provide helpful, step-by-step guidance on solving"
    " math problems. The user will ask you a wide range of complex mathematical questions."
    " Your purpose is to assist users in understanding mathematical concepts, working through"
    " equations, and arriving at the correct solutions."
)

ROLE_PLAYING_PROMPT = (
    "You are an AI assistant capable of engaging in various role-playing scenarios. Your"
    " purpose is to adopt different personas or characters as requested by the user. Maintain"
    " consistency with the chosen role, respond in character, and help create immersive and"
    " interactive experiences for the user."
)

DATA_ANALYSIS_PROMPT = (
    "You are an AI assistant specialized in data analysis and interpretation. Your purpose is"
    " to help users understand and derive insights from data sets, statistics, and analytical"
    " tasks. Offer clear explanations of data trends, assist with statistical calculations,"
    " and provide guidance on data visualization and interpretation techniques."
)

CREATIVE_WRITING_PROMPT = (
    "You are an AI assistant designed to support creative writing endeavors. Your purpose is"
    " to help users craft engaging stories, poems, and other creative texts. Offer"
    " suggestions for plot development, character creation, dialogue writing, and other"
    " aspects of creative composition. Provide constructive feedback and inspire creativity."
)

ADVICE_SEEKING_PROMPT = (
    "You are an AI assistant focused on providing thoughtful advice and guidance. Your"
    " purpose is to help users navigate various personal or professional issues by offering"
    " balanced perspectives, considering potential outcomes, and suggesting practical"
    " solutions. Encourage users to think critically about their situations while providing"
    " supportive and constructive advice."
)

BRAINSTORMING_PROMPT = (
    "You are an AI assistant specialized in generating ideas and facilitating creative"
    " thinking. Your purpose is to help users explore possibilities, think outside the box,"
    " and develop innovative concepts. Encourage free-flowing thoughts, offer diverse"
    " perspectives, and help users build upon and refine their ideas."
)

PROMPT_CREATION_PROMPT = f"""You are an AI assistant specialized in generating very precise prompts for dataset creation.
Your task is to write a prompt following the instruction of the user. Respond with the prompt and nothing else.
The prompt you write should follow the same style and structure as the following example prompts:

{INFORMATION_SEEKING_PROMPT}

{REASONING_PROMPT}

{PLANNING_PROMPT}

{CODING_DEBUGGING_PROMPT}

{EDITING_PROMPT}

{ROLE_PLAYING_PROMPT}

{DATA_ANALYSIS_PROMPT}

{CREATIVE_WRITING_PROMPT}

{ADVICE_SEEKING_PROMPT}

{BRAINSTORMING_PROMPT}

User dataset description:
"""

MODEL = "meta-llama/Meta-Llama-3.1-70B-Instruct"
DEFAULT_DATASET_DESCRIPTIONS = (
    "A chemistry expert assistant that explains chemical reactions and formulas.",
    "highly proficient assistant for PyTorch and CUDA expert developers to resolve complex issues",
    "skilled high school math assistant who helps students solve problems",
    "attentive and well-educated customer service assistant for a clothes e-commerce platform",
)
DEFAULT_SYSTEM_PROMPT = "You are an AI assistant specialized in chemistry, providing detailed explanations of chemical reactions, formulas, and processes. Your purpose is to assist users in understanding complex chemical concepts, breaking down reactions into step-by-step explanations, and helping users balance chemical equations. Offer examples of real-world applications, provide explanations of laboratory procedures, and support users in understanding the underlying principles of chemistry."
DEFAULT_DATASET = pd.DataFrame(
    {
        "prompt": [
            "What is the chemical equation for the reaction between sodium metal and water?"
        ],
        "completion": [
            """The reaction between sodium metal (Na) and water (H2O) is a highly exothermic reaction that releases hydrogen gas. The chemical equation for this reaction is: 2Na (sodium metal) + 2H2O (water) → 2NaOH (sodium hydroxide) + H2 (hydrogen gas) This can be broken down into two steps: 1. 2Na (sodium metal) + 2H2O (water) → 2Na+ (sodium ions) + 2OH- (hydroxide ions) + H2 (hydrogen gas) In the first step, the sodium metal reacts with the water to form sodium ions and hydroxide ions. 2Na+ (sodium ions) + 2OH- (hydroxide ions) → 2NaOH (sodium hydroxide) In the final step, the sodium ions and hydroxide ions combine to form sodium hydroxide. In this reaction, the sodium metal reduces the water, releasing hydrogen gas. This reaction is highly exothermic and generates a significant amount of heat, making it a self-sustaining reaction once it begins. The reaction is very violent, releasing a significant amount of"""
        ],
    }
)
_STOP_SEQUENCES = [
    "<|eot_id|>",
    "<|start_header_id|>",
    "assistant",
    " \n\n",
]
DEFAULT_BATCH_SIZE = 50
TOKEN_INDEX = 0


def _get_output_mappings(num_turns):
    if num_turns == 1:
        return {"instruction": "prompt", "response": "completion"}
    else:
        return {"conversation": "messages"}


def generate_pipeline_code(system_prompt, num_turns, num_rows):
    input_mappings = _get_output_mappings(num_turns)
    code = f"""
from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns
from distilabel.steps.tasks import MagpieGenerator
from distilabel.llms import InferenceEndpointsLLM

MODEL = "{MODEL}"
SYSTEM_PROMPT = "{system_prompt}"

with Pipeline(name="sft") as pipeline:
    magpie = MagpieGenerator(
        llm=InferenceEndpointsLLM(
            model_id=MODEL,
            tokenizer_id=MODEL,
            magpie_pre_query_template="llama3",
            generation_kwargs={{
                "temperature": 0.8,
                "do_sample": True,
                "max_new_tokens": 2048,
                "stop_sequences": {_STOP_SEQUENCES}
            }}
        ),
        n_turns={num_turns},
        num_rows={num_rows},
        batch_size=1,
        system_prompt=SYSTEM_PROMPT,
        output_mappings={input_mappings},
    )
    keep_columns = KeepColumns(
        columns={list(input_mappings.values())} + ["model_name"],
    )
    magpie.connect(keep_columns)

if __name__ == "__main__":
    distiset = pipeline.run()
"""
    return code


def get_pipeline(num_turns, num_rows, system_prompt, is_sample):
    global TOKEN_INDEX
    input_mappings = _get_output_mappings(num_turns)
    output_mappings = input_mappings
    api_key = HF_TOKENS[TOKEN_INDEX % len(HF_TOKENS)]
    TOKEN_INDEX += 1
    MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    print("is sample?", is_sample)
    if num_turns == 1:
        with Pipeline(name="sft") as pipeline:
            magpie = MagpieGenerator(
                llm=InferenceEndpointsLLM(
                    model_id=MODEL,
                    tokenizer_id=MODEL,
                    api_key=api_key,
                    magpie_pre_query_template="llama3",
                    generation_kwargs={
                        "temperature": 0.8,  # it's the best value for Llama 3.1 70B Instruct
                        "do_sample": True,
                        "max_new_tokens": 256 if is_sample else 512,
                        "stop_sequences": _STOP_SEQUENCES,
                    },
                ),
                batch_size=DEFAULT_BATCH_SIZE,
                n_turns=num_turns,
                num_rows=num_rows,
                system_prompt=system_prompt,
                output_mappings={"instruction": "prompt"},
                only_instruction=True,
            )

            generate_response = TextGeneration(
                llm=InferenceEndpointsLLM(
                    model_id=MODEL,
                    tokenizer_id=MODEL,
                    api_key=api_key,
                    generation_kwargs={"temperature": 0.8, "max_new_tokens": 256 if is_sample else 1024},
                ),
                system_prompt=system_prompt,
                output_mappings={"generation": "completion"},
                input_mappings={"instruction": "prompt"},
            )

            keep_columns = KeepColumns(
                columns=list(output_mappings.values()) + ["model_name"],
            )

            magpie.connect(generate_response)
            generate_response.connect(keep_columns)
        return pipeline
    else:
        with Pipeline(name="sft") as pipeline:
            magpie = MagpieGenerator(
                llm=InferenceEndpointsLLM(
                    model_id=MODEL,
                    tokenizer_id=MODEL,
                    api_key=api_key,
                    magpie_pre_query_template="llama3",
                    generation_kwargs={
                        "temperature": 0.8,  # it's the best value for Llama 3.1 70B Instruct
                        "do_sample": True,
                        "max_new_tokens": 2048,
                        "stop_sequences": _STOP_SEQUENCES,
                    },
                ),
                batch_size=DEFAULT_BATCH_SIZE,
                n_turns=num_turns,
                num_rows=num_rows,
                system_prompt=system_prompt,
                output_mappings=output_mappings,
            )
            keep_columns = KeepColumns(
                columns=list(output_mappings.values()) + ["model_name"],
            )
            magpie.connect(keep_columns)
        return pipeline


def get_prompt_generation_step():
    global TOKEN_INDEX
    api_key = HF_TOKENS[TOKEN_INDEX % len(HF_TOKENS)]
    TOKEN_INDEX += 1
    generate_description = TextGeneration(
        llm=InferenceEndpointsLLM(
            api_key=api_key,
            model_id=MODEL,
            tokenizer_id=MODEL,
            generation_kwargs={
                "temperature": 0.8,
                "max_new_tokens": 2048,
                "do_sample": True,
            },
        ),
        use_system_prompt=True,
    )
    return generate_description


if __name__ == "__main__":
    prompt_generation_step = get_prompt_generation_step()
    prompt_generation_step.load()
    result = next(
        prompt_generation_step.process(
            [
                {
                    "system_prompt": PROMPT_CREATION_PROMPT,
                    "instruction": DEFAULT_DATASET_DESCRIPTIONS[0],
                }
            ]
        )
    )[0]["generation"]
    pipeline = get_pipeline(num_rows=100, num_turns=1, system_prompt=result)
    pipeline.run()