

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__       = logging.get_logger(__name__)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  MobileNetVaConfig(layer_norm_eps=0.001			)

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

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

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

  return config
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase					      =  Image.open(requests.get(__A   ,       stream=__A			).raw			)
  return im
@torch.no_grad()
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A=False			):
  UpperCAmelCase					      =  get_mobilenet_va_config(__A			)

  # Load 🤗 model
  UpperCAmelCase					      =  MobileNetVaForImageClassification(__A			).eval()

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

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

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    UpperCAmelCase					      =  torch.tensor([-4.1739, -1.1233, 3.1205]			)
  elif model_name == "mobilenet_v1_0.75_192":
    UpperCAmelCase					      =  torch.tensor([-3.9440, -2.3141, -0.3333]			)
  else:
    UpperCAmelCase					      =  None

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

  Path(__A			).mkdir(exist_ok=__A			)
  print(F"Saving model {model_name} to {pytorch_dump_folder_path}"			)
  model.save_pretrained(__A			)
  print(F"Saving image processor to {pytorch_dump_folder_path}"			)
  image_processor.save_pretrained(__A			)

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


if __name__ == "__main__":
      lowerCAmelCase__       = 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__       = parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






def      _lowerCAmelCase(	__A			):
  assert column_title.isupper()
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  len(__A			) - 1
  UpperCAmelCase					      =  0

  while index >= 0:
    UpperCAmelCase					      =  (ord(column_title[index]			) - 64) * pow(26   ,       __A			)
    answer += value
    power += 1
    index -= 1

  return answer


if __name__ == "__main__":
      from doctest import testmod

      testmod()



