"""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()
_a				:		List[str]	   =       logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]     )	->				Optional[Any]:
  _lowerCAmelCase						:	Tuple        =					MobileNetVaConfig(layer_norm_eps=0.0_01     )

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

  _lowerCAmelCase						:	List[Any]        =					re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""      ,lowercase__     )
  if matches:
    _lowerCAmelCase						:	Optional[int]        =					float(matches[1]     )
    _lowerCAmelCase						:	Dict        =					int(matches[2]     )

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

  return config





def 							SCREAMING_SNAKE_CASE					(      )	->				Union[str, Any]:
  _lowerCAmelCase						:	Dict        =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _lowerCAmelCase						:	Optional[int]        =					Image.open(requests.get(lowercase__      ,stream=lowercase__     ).raw     )
  return im





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	str=False     )	->				Union[str, Any]:
  _lowerCAmelCase						:	Dict        =					get_mobilenet_va_config(lowercase__     )

  # Load 🤗 model
  _lowerCAmelCase						:	str        =					MobileNetVaForImageClassification(lowercase__     ).eval()

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

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  _lowerCAmelCase						:	Optional[int]        =					MobileNetVaImageProcessor(
      crop_size={"""width""": config.image_size, """height""": config.image_size}      ,size={"""shortest_edge""": config.image_size + 32}      ,)
  _lowerCAmelCase						:	List[str]        =					image_processor(images=prepare_img()      ,return_tensors="""pt"""     )
  _lowerCAmelCase						:	int        =					model(**lowercase__     )
  _lowerCAmelCase						:	List[Any]        =					outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    _lowerCAmelCase						:	str        =					torch.tensor([-4.17_39, -1.12_33, 3.12_05]     )
  elif model_name == "mobilenet_v1_0.75_192":
    _lowerCAmelCase						:	Optional[int]        =					torch.tensor([-3.94_40, -2.31_41, -0.33_33]     )
  else:
    _lowerCAmelCase						:	List[str]        =					None

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

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

  if push_to_hub:
    print("""Pushing to the hub..."""     )
    _lowerCAmelCase						:	Union[str, Any]        =					"""google/""" + model_name
    image_processor.push_to_hub(lowercase__     )
    model.push_to_hub(lowercase__     )


if __name__ == "__main__":
     _a				:		Tuple	   =       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.'
     )

     _a				:		Optional[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 socket





def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					socket.socket(socket.AF_INET      ,socket.SOCK_STREAM     )
  _lowerCAmelCase						:	Optional[int]        =					socket.gethostname()
  _lowerCAmelCase						:	Tuple        =					12312

  sock.connect((host, port)     )
  sock.send(b"""Hello server!"""     )

  with open("""Received_file"""      ,"""wb"""     ) as out_file:
    print("""File opened"""     )
    print("""Receiving data..."""     )
    while True:
      _lowerCAmelCase						:	List[Any]        =					sock.recv(1024     )
      if not data:
        break
      out_file.write(_lowerCamelCase     )

  print("""Successfully received the file"""     )
  sock.close()
  print("""Connection closed"""     )


if __name__ == "__main__":
     main()




