

'''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					:			Tuple							=    logging.get_logger(__name__)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]    ):
  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_([^_]*)_([^_]*)$'							, SCREAMING_SNAKE_CASE    )
  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(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
  UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ) + 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(SCREAMING_SNAKE_CASE							, stream=SCREAMING_SNAKE_CASE    ).raw    )
  return im


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Union[str, Any]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int=False    ):
  UpperCAmelCase    				=						get_mobilenet_va_config(SCREAMING_SNAKE_CASE    )

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

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

  # 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(**SCREAMING_SNAKE_CASE    )
  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]							, SCREAMING_SNAKE_CASE							, atol=1E-4    )

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

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


if __name__ == "__main__":
  _a					:			List[str]							=    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					:			str							=    parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )





'''simple docstring'''




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
  print('Googling.....')
  _a					:			List[Any]							=    'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
  _a					:			Dict							=    requests.get(url, headers={'UserAgent': UserAgent().random})
  # res.raise_for_status()
  with open('project1a.html', 'wb') as out_file:  # only for knowing the class
    for data in res.iter_content(10_000):
      out_file.write(data)
  _a					:			Any							=    BeautifulSoup(res.text, 'html.parser')
  _a					:			Optional[Any]							=    list(soup.select('.eZt8xd'))[:5]

  print(len(links))
  for link in links:
    if link.text == "Maps":
      webbrowser.open(link.get('href'))
    else:
      webbrowser.open(F'''https://google.com{link.get('href')}''')


