





'''simple docstring'''




import os
import time

import numpy as np
import onnxruntime as ort


lowercase__   :				Tuple      =   '''1'''
lowercase__   :				Any      =   '''0'''
lowercase__   :				Any      =   '''1'''

lowercase__   :				Any      =   ort.SessionOptions()
lowercase__   :				Optional[Any]      =   ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
lowercase__   :				List[Any]      =   ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
lowercase__   :				Optional[Any]      =   ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
lowercase__   :				List[str]      =   ort.RunOptions()

lowercase__   :				Any      =   1_28
lowercase__   :				Tuple      =   1
lowercase__   :				Tuple      =   np.ones((batch, sequence), dtype=np.intaa)
lowercase__   :				Any      =   np.ones((batch, sequence), dtype=np.intaa)
lowercase__   :				Optional[Any]      =   np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
lowercase__   :				Any      =   time.time()
lowercase__   :				List[Any]      =   20_00
lowercase__   :				int      =   {}
for iter in range(max_iters):
  lowercase__   :				Tuple      =   sess.run(
      None,
      {
          sess.get_inputs()[0].name: input_ids,
          sess.get_inputs()[1].name: attention_mask,
          sess.get_inputs()[2].name: token_type_ids,
      },
      run_options=run_opt,
  )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 10_00 / max_iters))






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)