
"""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()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					List[Any]:
  _snake_case					     =       MobileNetVaConfig(layer_norm_eps=0.001		)

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

  _snake_case					     =       re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$''' ,					__lowerCamelCase		)
  if matches:
    _snake_case					     =       float(matches[1]		)
    _snake_case					     =       int(matches[2]		)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  _snake_case					     =       10_01
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		) + 1: v for k, v in idalabel.items()}
  _snake_case					     =       '''background'''
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}

  return config
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       List[str]=False		)     ->					Any:
  _snake_case					     =       get_mobilenet_va_config(__lowerCamelCase		)

  # Load 🤗 model
  _snake_case					     =       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
  _snake_case					     =       MobileNetVaImageProcessor(
      crop_size={'''width''': config.image_size, '''height''': config.image_size} ,					size={'''shortest_edge''': config.image_size + 32} ,					)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       model(**__lowerCamelCase		)
  _snake_case					     =       outputs.logits

  assert logits.shape == (1, 10_01)

  if model_name == "mobilenet_v1_1.0_224":
    _snake_case					     =       torch.tensor([-4.1_739, -1.1_233, 3.1_205]		)
  elif model_name == "mobilenet_v1_0.75_192":
    _snake_case					     =       torch.tensor([-3.9_440, -2.3_141, -0.3_333]		)
  else:
    _snake_case					     =       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...'''		)
    _snake_case					     =       '''google/''' + model_name
    image_processor.push_to_hub(__lowerCamelCase		)
    model.push_to_hub(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = 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.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


if __name__ == "__main__":
 print(F"{solution() = }")



