import json
import os
from pydub import AudioSegment
import tqdm
import hashlib


EXCERPT_LENGTH = 30 * 1000  # 30 seconds in milliseconds
MIN_LENGTH = 5 * 1000  # 5 seconds in milliseconds
PATH = "/work/fast_data_yinghao/GS_Tempo"
audio_list = os.listdir(f"{PATH}/giantsteps-tempo-dataset/audio")


existed_uuid_list = set()

data_samples = []
for audio in tqdm.tqdm(audio_list):
    audio_path = os.path.join(f"{PATH}/giantsteps-tempo-dataset/audio", audio)
                            #   f"new_audio/{os.path.basename(audio)}.wav")
    output = open(f"{PATH}/giantsteps-tempo-dataset/annotations_v2/tempo/{audio[:-4]}.LOFI.bpm", "r").readline().strip()
    data_sample = {
        "instruction": "Please estimate the tempo of the music. Such as 60.0bpm, 143.2bpm etc.",
        "input": f"<|SOA|>{audio}<|EOA|>",
        "output": output,
        "uuid": "",
        "audioid": audio,
        "split": ["train"],
        "task_type": {"major": ["global_MIR"], "minor": ["tempo_esitimation"]},
        "domain": "music",
        "source": "GS_Tempo",
        "other": {}
    }
    # change uuid
    uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}"
    unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位
    if unique_id in existed_uuid_list:
        sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID
        unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16]
    existed_uuid_list.add(unique_id)
    data_sample["uuid"] = f"{unique_id}"
    # data_samples.append(data_sample)
    
    # # change uuid
    # uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}"
    # unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位
    # if unique_id in existed_uuid_list:
    #     sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID
    #     unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16]
    # existed_uuid_list.add(unique_id)
    # data_sample["uuid"] = f"{unique_id}"

    if not os.path.exists(audio_path):
        print(f"File not found: {audio_path}")
        continue

    # Load the audio file
    audio = AudioSegment.from_file(audio_path)
        
    # Process and split the audio into 30-second excerpts
    for i in range(0, len(audio), EXCERPT_LENGTH):
        end = i + EXCERPT_LENGTH
        if end < len(audio):
            excerpt = audio[i:end]
        else:
            excerpt = audio[i:]
            # Discard short audio clips
            if len(excerpt) < MIN_LENGTH:
                break
            end = len(audio)
        
        # # Save the excerpt to the same directory with a new name
        excerpt_path = f"{PATH}/new_audio/{os.path.basename(audio_path)[:-4]}_{i//EXCERPT_LENGTH}.wav"
        excerpt.export(excerpt_path, format="wav")
        
        # Update the sample dictionary with the new audio path
        # new_sample = data_sample.copy()
        # new_sample["audioid"] = excerpt_path
        # new_sample["input"] = f"<|SOA|>{excerpt_path}<|EOA|>"
        data_samples.append({
            "instruction": "Please estimate the tempo of the music. Such as 60.0bpm, 143.2bpm etc.",
            "input": f"<|SOA|>{excerpt_path}<|EOA|>",
            "output": output,
            "uuid": "",
            "audioid": excerpt_path,
            "split": ["train"],
            "task_type": {"major": ["global_MIR"], "minor": ["tempo_esitimation"]},
            "domain": "music",
            "source": "GS_Tempo",
            "other": {}
        })
        # print(data_samples[-1])
    # break
    


# Save to JSONL format
output_file_path = f'{PATH}/GS-Tempo_train.jsonl'  # Replace with the desired output path
with open(output_file_path, 'w') as outfile:
    # for sample in data_samples:
    json.dump(data_samples, outfile)

    # outfile.write('\n')
outfile.close()