





'''simple docstring'''


import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_   :   int       =					logging.get_logger(__name__)







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Dict:
   a__     = MobileNetVaConfig(layer_norm_eps=0.001     )

   if "_quant" in model_name:
      raise ValueError("Quantized models are not supported."     )

   a__     = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"					,  __lowerCamelCase     )
   if matches:
      a__     = float(matches[1]     )
      a__     = int(matches[2]     )

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   a__     = 1001
   a__     = "imagenet-1k-id2label.json"
   a__     = "huggingface/label-files"
   a__     = json.load(open(hf_hub_download(__lowerCamelCase					,  __lowerCamelCase					,  repo_type="dataset"     )					,  "r"     )     )
   a__     = {int(__lowerCamelCase     ) + 1: v for k, v in idalabel.items()}
   a__     = "background"
   a__     = idalabel
   a__     = {v: k for k, v in idalabel.items()}

   return config







def    _lowerCamelCase  ()   ->    List[Any]:
   a__     = "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__     = Image.open(requests.get(__lowerCamelCase					,  stream=__lowerCamelCase     ).raw     )
   return im







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     int=False     )   ->    List[Any]:
   a__     = get_mobilenet_va_config(__lowerCamelCase     )

   # Load 🤗 model
   a__     = MobileNetVaForImageClassification(__lowerCamelCase     ).eval()

   # Load weights from TensorFlow checkpoint
   load_tf_weights_in_mobilenet_va(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   a__     = MobileNetVaImageProcessor(
       crop_size={"width": config.image_size, "height": config.image_size}					,  size={"shortest_edge": config.image_size + 32}					,  )
   a__     = image_processor(images=prepare_img()					,  return_tensors="pt"     )
   a__     = model(**__lowerCamelCase     )
   a__     = outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      a__     = torch.tensor([-4.1_739, -1.1_233, 3.1_205]     )
   elif model_name == "mobilenet_v1_0.75_192":
      a__     = torch.tensor([-3.9_440, -2.3_141, -0.3_333]     )
   else:
      a__     = None

   if expected_logits is not None:
      assert torch.allclose(logits[0, :3]					,  __lowerCamelCase					,  atol=1e-4     )

   Path(__lowerCamelCase     ).mkdir(exist_ok=__lowerCamelCase     )
   print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
   model.save_pretrained(__lowerCamelCase     )
   print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
   image_processor.save_pretrained(__lowerCamelCase     )

   if push_to_hub:
      print("Pushing to the hub..."     )
      a__     = "google/" + model_name
      image_processor.push_to_hub(__lowerCamelCase     )
      model.push_to_hub(__lowerCamelCase     )


if __name__ == "__main__":
  lowerCAmelCase_   :   List[Any]       =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="mobilenet_v1_1.0_224",
      type=str,
      help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
  )
  parser.add_argument(
      "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  lowerCAmelCase_   :   Any       =					parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )










'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

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

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					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...")
lowerCAmelCase_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					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) * 1000 / max_iters))



