#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from datasets import load_dataset
from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
from transformers import WhisperProcessor
import torch
from evaluate import load
import time
import argparse
import pandas as pd
import os
import csv
from datetime import date

current_dir = os.path.dirname(os.path.realpath(__file__))
 
def get_args():
    parser = argparse.ArgumentParser(description="Evaluate Whisper performance and accuracy")
    parser.add_argument('--model_path', required=True, help='pretrained model path')
    parser.add_argument('--data_type', required=True, help='clean, other')
    parser.add_argument('--device', required=False, help='cpu, xpu')
    parser.add_argument('--load_in_low_bit', default='sym_int4', help='Specify whether to load data in low bit format (e.g., 4-bit)')
    parser.add_argument('--save_result', action='store_true', help='Save the results to a CSV file')
 
    args = parser.parse_args()
    return args
 
if __name__ == '__main__':
    args = get_args()
    if args.device == "":
        args.device = "cpu"
 
    speech_dataset = load_dataset('./librispeech_asr.py', name=args.data_type, split='test').select(range(500))
    processor = WhisperProcessor.from_pretrained(args.model_path)
    forced_decoder_ids = processor.get_decoder_prompt_ids(language='en', task='transcribe')
   
    model = AutoModelForSpeechSeq2Seq.from_pretrained(args.model_path, load_in_low_bit=args.load_in_low_bit, optimize_model=True).eval().to(args.device)
    model.config.forced_decoder_ids = None
   
    def map_to_pred(batch):
        audio = batch["audio"]
        start_time = time.time()
        input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
        batch["reference"] = processor.tokenizer._normalize(batch['text'])
 
        with torch.no_grad():
            predicted_ids = model.generate(input_features.to(args.device), forced_decoder_ids=forced_decoder_ids, use_cache=True)[0]
            if args.device == "xpu":
                torch.xpu.synchronize()
 
        infer_time = time.time() - start_time
        transcription = processor.decode(predicted_ids)
        batch["prediction"] = processor.tokenizer._normalize(transcription)
        batch["length"] = len(audio["array"])/audio["sampling_rate"]
        batch["time"] = infer_time
        print(batch["reference"])
        print(batch["prediction"])
        return batch
   
    result = speech_dataset.map(map_to_pred, keep_in_memory=True)
    wer = load("./wer")
    speech_length = sum(result["length"][1:])
    prc_time = sum(result["time"][1:])

    MODEL = args.model_path.split('/')[-2]
    RTF = prc_time/speech_length
    RTX = speech_length/prc_time
    WER = 100 * wer.compute(references=result["reference"], predictions=result["prediction"])

    today = date.today()
    if args.save_result:
        csv_name = f'{current_dir}/results/{MODEL}-{args.data_type}-{args.device}-{args.load_in_low_bit}-{today}.csv'
        os.makedirs(os.path.dirname(csv_name), exist_ok=True)
        with open(csv_name, mode='a', newline='') as file:
            csv_writer = csv.writer(file)
            file.seek(0, os.SEEK_END)
            if file.tell() == 0:
                csv_writer.writerow(["models","precision","WER","RTF"])
            csv_writer.writerow([MODEL, args.load_in_low_bit, WER, RTF])
        print(f'Results saved to {csv_name}')

    print("Realtime Factor(RTF) is : %.4f" % RTF)
    print("Realtime X(RTX) is : %.2f" % RTX)
    print(f'WER is {WER}')