


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[Any]					)     ->    List[Any]:
   lowerCamelCase_						:   Union[str, Any]		= old_name

   if "patch_embed" in old_name:
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   str		= old_name.split("""."""					)

      if layer == "0":
         lowerCamelCase_						:   str		= old_name.replace("""0"""						,						"""convolution1"""					)
      elif layer == "1":
         lowerCamelCase_						:   int		= old_name.replace("""1"""						,						"""batchnorm_before"""					)
      elif layer == "3":
         lowerCamelCase_						:   Optional[int]		= old_name.replace("""3"""						,						"""convolution2"""					)
      else:
         lowerCamelCase_						:   str		= old_name.replace("""4"""						,						"""batchnorm_after"""					)

   if "network" in old_name and re.search(R"""\d\.\d"""						,						A__					):
      lowerCamelCase_						:   Dict		= R"""\b\d{2}\b"""
      if bool(re.search(A__						,						A__					)					):
         lowerCamelCase_						:   Optional[int]		= re.search(R"""\d\.\d\d."""						,						A__					).group()
      else:
         lowerCamelCase_						:   List[Any]		= re.search(R"""\d\.\d."""						,						A__					).group()
      if int(match[0]					) < 6:
         lowerCamelCase_						:   str		= old_name.replace(A__						,						""""""					)
         lowerCamelCase_						:   Optional[Any]		= trimmed_name.replace("""network"""						,						match[0] + """.meta4D_layers.blocks.""" + match[2:-1]					)
         lowerCamelCase_						:   str		= """intermediate_stages.""" + trimmed_name
      else:
         lowerCamelCase_						:   Any		= old_name.replace(A__						,						""""""					)
         if int(match[2]					) < num_meta4D_last_stage:
            lowerCamelCase_						:   str		= trimmed_name.replace("""network"""						,						"""meta4D_layers.blocks.""" + match[2]					)
         else:
            lowerCamelCase_						:   Union[str, Any]		= str(int(match[2]					) - num_meta4D_last_stage					)
            lowerCamelCase_						:   Any		= trimmed_name.replace("""network"""						,						"""meta3D_layers.blocks.""" + layer_index					)
            if "norm1" in old_name:
               lowerCamelCase_						:   int		= trimmed_name.replace("""norm1"""						,						"""layernorm1"""					)
            elif "norm2" in old_name:
               lowerCamelCase_						:   List[str]		= trimmed_name.replace("""norm2"""						,						"""layernorm2"""					)
            elif "fc1" in old_name:
               lowerCamelCase_						:   List[Any]		= trimmed_name.replace("""fc1"""						,						"""linear_in"""					)
            elif "fc2" in old_name:
               lowerCamelCase_						:   Any		= trimmed_name.replace("""fc2"""						,						"""linear_out"""					)

         lowerCamelCase_						:   Optional[int]		= """last_stage.""" + trimmed_name

   elif "network" in old_name and re.search(R""".\d."""						,						A__					):
      lowerCamelCase_						:   Tuple		= old_name.replace("""network"""						,						"""intermediate_stages"""					)

   if "fc" in new_name:
      lowerCamelCase_						:   Union[str, Any]		= new_name.replace("""fc"""						,						"""convolution"""					)
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      lowerCamelCase_						:   Dict		= new_name.replace("""norm1"""						,						"""batchnorm_before"""					)
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      lowerCamelCase_						:   Union[str, Any]		= new_name.replace("""norm2"""						,						"""batchnorm_after"""					)
   if "proj" in new_name:
      lowerCamelCase_						:   Optional[Any]		= new_name.replace("""proj"""						,						"""projection"""					)
   if "dist_head" in new_name:
      lowerCamelCase_						:   str		= new_name.replace("""dist_head"""						,						"""distillation_classifier"""					)
   elif "head" in new_name:
      lowerCamelCase_						:   str		= new_name.replace("""head"""						,						"""classifier"""					)
   elif "patch_embed" in new_name:
      lowerCamelCase_						:   Optional[Any]		= """efficientformer.""" + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      lowerCamelCase_						:   Optional[int]		= new_name.replace("""norm"""						,						"""layernorm"""					)
      lowerCamelCase_						:   List[Any]		= """efficientformer.""" + new_name
   else:
      lowerCamelCase_						:   int		= """efficientformer.encoder.""" + new_name

   return new_name


def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: Optional[int]					)     ->    str:
   for key in checkpoint.copy().keys():
      lowerCamelCase_						:   Any		= checkpoint.pop(A__					)
      lowerCamelCase_						:   Optional[Any]		= val

   return checkpoint


def    __lowerCamelCase   (      )     ->    int:
   lowerCamelCase_						:   Any		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   Optional[int]		= Image.open(requests.get(A__						,						stream=A__					).raw					)

   return image


def    __lowerCamelCase   (      A__						: Path						,						A__						: Path						,						A__						: Path						,						A__						: bool					)     ->    Optional[Any]:
   lowerCamelCase_						:   Dict		= torch.load(A__						,						map_location="""cpu"""					)["""model"""]
   lowerCamelCase_						:   Union[str, Any]		= EfficientFormerConfig.from_json_file(A__					)
   lowerCamelCase_						:   Optional[Any]		= EfficientFormerForImageClassificationWithTeacher(A__					)
   lowerCamelCase_						:   Tuple		= """_""".join(checkpoint_path.split("""/"""					)[-1].split("""."""					)[0].split("""_"""					)[:-1]					)

   lowerCamelCase_						:   Dict		= config.depths[-1] - config.num_metaad_blocks + 1
   lowerCamelCase_						:   Optional[int]		= convert_torch_checkpoint(A__						,						A__					)

   model.load_state_dict(A__					)
   model.eval()

   lowerCamelCase_						:   Tuple		= {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   # prepare image
   lowerCamelCase_						:   str		= prepare_img()
   lowerCamelCase_						:   Optional[Any]		= 256
   lowerCamelCase_						:   int		= 224
   lowerCamelCase_						:   Tuple		= EfficientFormerImageProcessor(
       size={"""shortest_edge""": image_size}						,						crop_size={"""height""": crop_size, """width""": crop_size}						,						resample=pillow_resamplings["""bicubic"""]						,						)
   lowerCamelCase_						:   Dict		= processor(images=A__						,						return_tensors="""pt"""					).pixel_values

   # original processing pipeline
   lowerCamelCase_						:   str		= Compose(
       [
           Resize(A__						,						interpolation=pillow_resamplings["""bicubic"""]					),
           CenterCrop(A__					),
           ToTensor(),
           Normalize(A__						,						A__					),
       ]					)
   lowerCamelCase_						:   Any		= image_transforms(A__					).unsqueeze(0					)

   assert torch.allclose(A__						,						A__					)

   lowerCamelCase_						:   str		= model(A__					)
   lowerCamelCase_						:   Any		= outputs.logits

   lowerCamelCase_						:   int		= (1, 1000)

   if "l1" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]					)
      assert torch.allclose(logits[0, :10]						,						A__						,						atol=1e-3					)
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      lowerCamelCase_						:   str		= torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]					)
      assert torch.allclose(logits[0, :10]						,						A__						,						atol=1e-3					)
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      lowerCamelCase_						:   str		= torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]					)
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''					)

   # Save Checkpoints
   Path(A__					).mkdir(exist_ok=A__					)
   model.save_pretrained(A__					)
   print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''					)
   processor.save_pretrained(A__					)
   print(f'''Processor successfuly saved at {pytorch_dump_path}'''					)

   if push_to_hub:
      print("""Pushing model to the hub..."""					)

      model.push_to_hub(
          repo_id=f'''Bearnardd/{pytorch_dump_path}'''						,						commit_message="""Add model"""						,						use_temp_dir=A__						,						)
      processor.push_to_hub(
          repo_id=f'''Bearnardd/{pytorch_dump_path}'''						,						commit_message="""Add image processor"""						,						use_temp_dir=A__						,						)


if __name__ == "__main__":
      snake_case__				:	int					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--pytorch_model_path',
          default=None,
          type=str,
          required=True,
          help='Path to EfficientFormer pytorch checkpoint.',
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The json file for EfficientFormer model config.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )

      parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
      parser.add_argument(
          '--no-push_to_hub',
          dest='push_to_hub',
          action='store_false',
          help='Do not push model and image processor to the hub',
      )
      parser.set_defaults(push_to_hub=True)

      snake_case__				:	int					       =					parser.parse_args()
      convert_efficientformer_checkpoint(
          checkpoint_path=args.pytorch_model_path,
          efficientformer_config_file=args.config_file,
          pytorch_dump_path=args.pytorch_dump_path,
          push_to_hub=args.push_to_hub,
      )









import requests
from bsa import BeautifulSoup


def    __lowerCamelCase   (      A__						: str = "https://www.worldometers.info/coronavirus"					)     ->    dict:
   lowerCamelCase_						:   List[str]		= BeautifulSoup(requests.get(A__					).text						,						"""html.parser"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""h1"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""div"""						,						{"""class""": """maincounter-number"""}					)
   keys += soup.findAll("""span"""						,						{"""class""": """panel-title"""}					)
   values += soup.findAll("""div"""						,						{"""class""": """number-table-main"""}					)
   return {key.text.strip(): value.text.strip() for key, value in zip(A__						,						A__					)}


if __name__ == "__main__":
      print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
      for key, value in world_covidaa_stats().items():
            print(F'{key}\n{value}\n')





