from preprocessor.SerialPreprocessor import SerialConfig, SerialPreprocessor
from const import (
    DIALOGUE_SUMMARY,
    EMOTION_RECOGNITION,
    DIALOGUE_CONTEXT_TO_TEXT_GENERATION,
    ABSA_TERM_OPINION_SENTIMENT,
    ABSA_TERM_SENTIMENT,
    ABSA_CATEGORY_SENTIMENT,
    ABSA_TERM_CATEGORY_SENTIMENT,
    CHARACTER_IDENTIFICATION,
    DIALOGUE_STATE_TRACKING,
    DOCUMENT_GROUNDED_CONVERSATION,
    TEXT2SQL,
    SLOT_FILLING,
)
from preprocessor.prompt_funcs import const_prompt_func_wrapper
from preprocessor.knowledge_funcs import (
    None_knowledge,
    concat_list_knowledge_wrapper,
    extract_turn_knowledge_wrapper,
    origin_knowledge,
    extract_schema_knowledge_wrapper,
)
from preprocessor.label_funs import (
    extract_summary,
    extract_turn_emotion_wrapper,
    extract_turn_utterance,
    extract_aspects_wrapper,
    rebuild_utterance_with_characters,
    extract_belief_state_wrapper,
    extract_sql,
    extract_slots_without_intents_wrapper,
)
import os

if __name__ == "__main__":
    # 1. Dialogue Summary
    TASK = DIALOGUE_SUMMARY
    input_path = r"E:\research\processed\DialogueSummary"
    output_path = r"E:\research\seq\DialogueSummary"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "Give a summary of this dialogue."
                ),
                knowledge_func=None_knowledge,
                label_func=extract_summary,
            )
        )

        serial_proc.launch()

    # 2. Emotion Recognition
    TASK = EMOTION_RECOGNITION
    input_path = r"E:\research\processed\EmotionRecognition"
    output_path = r"E:\research\seq\EmotionRecognition"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given possible emotions, select the correct answer."
                ),
                knowledge_func=concat_list_knowledge_wrapper(
                    "possible choices: ", " | "
                ),
                label_func=extract_turn_emotion_wrapper(", "),
            )
        )

        serial_proc.launch()

    # 3. Dialogue Context-to-Text Generation
    TASK = DIALOGUE_CONTEXT_TO_TEXT_GENERATION
    input_path = r"E:\research\processed\Dialogue-Context-to-Text Generation"
    output_path = r"E:\research\seq\Dialogue-Context-to-Text Generation"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given dialogue context, give the response."
                ),
                knowledge_func=None_knowledge,
                label_func=extract_turn_utterance,
                roles_to_build_example=[["Listener"], ["third-person"]],
            )
        )

        serial_proc.launch()

    # 4. Aspect Sentiment Analysis
    # 4.1 ABSA: term opinion sentiment
    TASK = ABSA_TERM_OPINION_SENTIMENT
    input_path = r"E:\research\processed\ABSA-term opinion sentiment\ASTE"
    output_path = r"E:\research\seq\Aspect-based Sentiment Analysis\ASTE"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper("Give all the aspects."),
                knowledge_func=None_knowledge,
                label_func=extract_aspects_wrapper(" | ", ", "),
            )
        )

        serial_proc.launch()

    # 4.2 ABSA: term sentiment
    TASK = ABSA_TERM_SENTIMENT
    input_path = r"E:\research\processed\ABSA-term sentiment"
    output_path = r"E:\research\seq\Aspect-based Sentiment Analysis"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper("Give all the aspects."),
                knowledge_func=None_knowledge,
                label_func=extract_aspects_wrapper(" | ", ", "),
            )
        )

        serial_proc.launch()

    # 4.3 ABSA: category sentiment
    TASK = ABSA_CATEGORY_SENTIMENT
    input_path = r"E:\research\processed\ABSA-category sentiment"
    output_path = r"E:\research\seq\Aspect-based Sentiment Analysis"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper("Give all the aspects."),
                knowledge_func=None_knowledge,
                label_func=extract_aspects_wrapper(" | ", ", "),
            )
        )

        serial_proc.launch()

    # 4.4 ABSA: term category sentiment
    TASK = ABSA_TERM_CATEGORY_SENTIMENT
    input_path = r"E:\research\processed\ABSA-term category sentiment"
    output_path = r"E:\research\seq\Aspect-based Sentiment Analysis"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper("Give all the aspects."),
                knowledge_func=None_knowledge,
                label_func=extract_aspects_wrapper(" | ", ", "),
            )
        )

        serial_proc.launch()

    # 5. Character Identification
    TASK = CHARACTER_IDENTIFICATION
    input_path = r"E:\research\processed\CharacterIdentification"
    output_path = r"E:\research\seq\CharacterIdentification"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper("Generate with all characters."),
                knowledge_func=concat_list_knowledge_wrapper("all speakers: ", " | "),
                label_func=rebuild_utterance_with_characters,
            )
        )

        serial_proc.launch()


    # 6. Dialogue State Tracking
    TASK = DIALOGUE_STATE_TRACKING
    input_path = r"E:\research\processed\DialogueStateTracking"
    output_path = r"E:\research\seq\DialogueStateTracking"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given dialogue context, give the dialogue state."
                ),
                knowledge_func=None_knowledge,
                label_func=extract_belief_state_wrapper(", ", " | ", "; ", ": "),
                roles_to_build_example=[["USER"]],
            )
        )

        serial_proc.launch()

    # 7. Document Grounded Conversation
    TASK = DOCUMENT_GROUNDED_CONVERSATION
    input_path = r"E:\research\processed\DocumentGroundedConversations"
    output_path = r"E:\research\seq\DocumentGroundedConversation"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given dialogue context, give the response."
                ),
                knowledge_func=origin_knowledge,
                turn_knowledge_func=extract_turn_knowledge_wrapper(": ", " | ", "; "),
                label_func=extract_turn_utterance,
            )
        )

        serial_proc.launch()
    # 8. Text2SQL
    TASK = TEXT2SQL
    input_path = r"E:\research\processed\Text2SQL"
    output_path = r"E:\research\seq\Text2SQL"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given dialogue context, give the sql."
                ),
                knowledge_func=origin_knowledge,
                turn_knowledge_func=extract_schema_knowledge_wrapper(),
                label_func=extract_sql,
            )
        )

        serial_proc.launch()

    TASK = SLOT_FILLING
    input_path = r"E:\research\processed\SlotFilling\MultiDoGo"
    output_path = r"E:\research\seq\SlotFilling\MultiDoGo"

    for dataset in os.listdir(input_path):
        input_data_path = os.path.join(input_path, dataset)
        output_data_path = os.path.join(output_path, dataset)

        serial_proc = SerialPreprocessor(
            SerialConfig(
                input_data_path,
                output_data_path,
                TASK,
                logger_name=TASK,
                task_bos_token=f"[{TASK}]",
                prompt_func=const_prompt_func_wrapper(
                    "With given utterance, fill the slots."
                ),
                knowledge_func=None_knowledge,
                label_func=extract_slots_without_intents_wrapper(", ", " | "),
            )
        )

        serial_proc.launch()