from towhee import ops
import torch
import numpy
import onnx
import onnxruntime

import os
from pathlib import Path
import logging
import platform
import psutil

# full_models = AutoTransformers.supported_model_names()
# checked_models = AutoTransformers.supported_model_names(format='onnx')
# models = [x for x in full_models if x not in checked_models]
models = [
    'allenai/led-base-16384',
    'cl-tohoku/bert-base-japanese-char',
    'cl-tohoku/bert-base-japanese-char-whole-word-masking',
    'cl-tohoku/bert-base-japanese-whole-word-masking',
    'ctrl',
    'facebook/wmt19-ru-en',
    'funnel-transformer/intermediate',
    'funnel-transformer/intermediate-base',
    'funnel-transformer/large',
    'funnel-transformer/large-base',
    'funnel-transformer/medium',
    'funnel-transformer/medium-base',
    'funnel-transformer/small',
    'funnel-transformer/small-base',
    'funnel-transformer/xlarge',
    'funnel-transformer/xlarge-base',
    'google/bert_for_seq_generation_L-24_bbc_encoder',
    'google/canine-c',
    'google/canine-s',
    'google/fnet-base',
    'google/fnet-large',
    'google/reformer-crime-and-punishment',
    'microsoft/mpnet-base',
    'openai-gpt',
    'tau/splinter-base',
    'tau/splinter-base-qass',
    'tau/splinter-large',
    'tau/splinter-large-qass',
    'transfo-xl-wt103',
    'uw-madison/nystromformer-512',
    'uw-madison/yoso-4096',
    'xlnet-base-cased',
    'xlnet-large-cased'
]
test_txt = 'hello, world.'
atol = 1e-3
log_path = 'transformers_onnx.log'
f = open('onnx.csv', 'w+')
f.write('model,load_op,save_onnx,check_onnx,run_onnx,accuracy\n')

logger = logging.getLogger('transformers_onnx')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh = logging.FileHandler(log_path)
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
ch.setFormatter(formatter)
logger.addHandler(ch)

logger.debug(f'machine: {platform.platform()}-{platform.processor()}')
logger.debug(f'free/available/total mem: {round(psutil.virtual_memory().free / (1024.0 ** 3))}'
             f'/{round(psutil.virtual_memory().available / (1024.0 ** 3))}'
             f'/{round(psutil.virtual_memory().total / (1024.0 ** 3))} GB')
logger.debug(f'cpu: {psutil.cpu_count()}')


status = None
for name in models:
    logger.info(f'***{name}***')
    saved_name = name.replace('/', '-')
    onnx_path = f'saved/onnx/{saved_name}.onnx'
    if status:
        f.write(','.join(status) + '\n')
    status = [name] + ['fail'] * 5
    try:
        op = ops.text_embedding.transformers(model_name=name, device='cpu').get_op()
        out1 = op(test_txt)
        logger.info('OP LOADED.')
        status[1] = 'success'
    except Exception as e:
        logger.error(f'FAIL TO LOAD OP: {e}')
        continue
    try:
        inputs = op.tokenizer(test_txt, return_tensors='pt')
        input_names = list(inputs.keys())
        dynamic_axes = {}
        for i_n in input_names:
            dynamic_axes[i_n] = {0: 'batch_size', 1: 'sequence_length'}
        try:
            output_names = ['last_hidden_state']
            for o_n in output_names:
                dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
            torch.onnx.export(
                    op.model,
                    tuple(inputs.values()),
                    onnx_path,
                    input_names=input_names,
                    output_names=output_names,
                    dynamic_axes=dynamic_axes,
                    opset_version=14,
                    do_constant_folding=True,
                    )
        except Exception as e:
            print(e, '\nTrying with 2 outputs...')
            output_names = ['last_hidden_state', 'pooler_output']
            for o_n in output_names:
                dynamic_axes[o_n] = {0: 'batch_size', 1: 'sequence_length'}
            torch.onnx.export(
                    op.model,
                    tuple(inputs.values()),
                    onnx_path,
                    input_names=input_names,
                    output_names=output_names,
                    dynamic_axes=dynamic_axes,
                    opset_version=14,
                    do_constant_folding=True,
                    )
        logger.info('ONNX SAVED.')
        status[2] = 'success'
    except Exception as e:
        logger.error(f'FAIL TO SAVE ONNX: {e}')
        continue
    try:
        try:
            onnx_model = onnx.load(onnx_path)
            onnx.checker.check_model(onnx_model)
        except Exception:
            saved_onnx = onnx.load(onnx_path, load_external_data=True)
            onnx.checker.check_model(saved_onnx)
        logger.info('ONNX CHECKED.')
        status[3] = 'success'
    except Exception as e:
        logger.error(f'FAIL TO CHECK ONNX: {e}')
        pass
    try:
        sess = onnxruntime.InferenceSession(onnx_path,
                                            providers=onnxruntime.get_available_providers())
        inputs = op.tokenizer(test_txt, return_tensors='np')
        onnx_inputs = [x.name for x in sess.get_inputs()]
        new_inputs = {}
        for k in onnx_inputs:
            new_inputs[k] = inputs[k]
        out2 = sess.run(output_names=['last_hidden_state'], input_feed=dict(new_inputs))
        logger.info('ONNX WORKED.')
        status[4] = 'success'
        if numpy.allclose(out1, out2, atol=atol):
            logger.info('Check accuracy: OK')
            status[5] = 'success'
        else:
            logger.info(f'Check accuracy: atol is larger than {atol}.')
    except Exception as e:
        logger.error(f'FAIL TO RUN ONNX: {e}')
        continue

if status:
    f.write(','.join(status) + '\n')

print('Finished.')