import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      [2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
  SCREAMING_SNAKE_CASE__										=      True if """large""" in model_name or """huge""" in model_name else False
  SCREAMING_SNAKE_CASE__										=      True if """large""" in model_name or """huge""" in model_name else False
  SCREAMING_SNAKE_CASE__										=      True if """large""" in model_name or """huge""" in model_name else False

  if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
    if "fl3" in model_name:
      SCREAMING_SNAKE_CASE__										=      [3, 3, 3, 3]
      SCREAMING_SNAKE_CASE__										=      [5, 5, 5, 5]
    elif "fl4" in model_name:
      SCREAMING_SNAKE_CASE__										=      [4, 4, 4, 4]
      SCREAMING_SNAKE_CASE__										=      [3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    SCREAMING_SNAKE_CASE__										=      [3, 3, 3, 3]
    if "lrf" in model_name:
      SCREAMING_SNAKE_CASE__										=      [3, 3, 3, 3]
    else:
      SCREAMING_SNAKE_CASE__										=      [2, 2, 2, 2]

  if "tiny" in model_name:
    SCREAMING_SNAKE_CASE__										=      96
  elif "small" in model_name:
    SCREAMING_SNAKE_CASE__										=      96
  elif "base" in model_name:
    SCREAMING_SNAKE_CASE__										=      1_28
  elif "large" in model_name:
    SCREAMING_SNAKE_CASE__										=      1_92
  elif "xlarge" in model_name:
    SCREAMING_SNAKE_CASE__										=      2_56
  elif "huge" in model_name:
    SCREAMING_SNAKE_CASE__										=      3_52

  # set label information
  SCREAMING_SNAKE_CASE__										=      """huggingface/label-files"""
  if "large" in model_name or "huge" in model_name:
    SCREAMING_SNAKE_CASE__										=      """imagenet-22k-id2label.json"""
  else:
    SCREAMING_SNAKE_CASE__										=      """imagenet-1k-id2label.json"""

  SCREAMING_SNAKE_CASE__										=      json.load(open(hf_hub_download(__lowerCamelCase ,     __lowerCamelCase ,     repo_type="""dataset"""	) ,     """r"""	)	)
  SCREAMING_SNAKE_CASE__										=      {int(__lowerCamelCase	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE__										=      {v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE__										=      FocalNetConfig(
      embed_dim=__lowerCamelCase ,     depths=__lowerCamelCase ,     focal_levels=__lowerCamelCase ,     focal_windows=__lowerCamelCase ,     use_conv_embed=__lowerCamelCase ,     idalabel=__lowerCamelCase ,     labelaid=__lowerCamelCase ,     use_post_layernorm=__lowerCamelCase ,     use_layerscale=__lowerCamelCase ,     )

  return config



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any]	)							->					Optional[Any]:







  """simple docstring"""





  if "patch_embed.proj" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""patch_embed.proj""" ,     """embeddings.patch_embeddings.projection"""	)
  if "patch_embed.norm" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""patch_embed.norm""" ,     """embeddings.norm"""	)
  if "layers" in name:
    SCREAMING_SNAKE_CASE__										=      """encoder.""" + name
  if "encoder.layers" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""encoder.layers""" ,     """encoder.stages"""	)
  if "downsample.proj" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""downsample.proj""" ,     """downsample.projection"""	)
  if "blocks" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""blocks""" ,     """layers"""	)
  if "modulation.f.weight" in name or "modulation.f.bias" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""modulation.f""" ,     """modulation.projection_in"""	)
  if "modulation.h.weight" in name or "modulation.h.bias" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""modulation.h""" ,     """modulation.projection_context"""	)
  if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""modulation.proj""" ,     """modulation.projection_out"""	)

  if name == "norm.weight":
    SCREAMING_SNAKE_CASE__										=      """layernorm.weight"""
  if name == "norm.bias":
    SCREAMING_SNAKE_CASE__										=      """layernorm.bias"""

  if "head" in name:
    SCREAMING_SNAKE_CASE__										=      name.replace("""head""" ,     """classifier"""	)
  else:
    SCREAMING_SNAKE_CASE__										=      """focalnet.""" + name

  return name



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Optional[int]=False	)							->					List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {
      """focalnet-tiny""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth""",
      """focalnet-tiny-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth""",
      """focalnet-small""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth""",
      """focalnet-small-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth""",
      """focalnet-base""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth""",
      """focalnet-base-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth""",
      """focalnet-large-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth""",
      """focalnet-large-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth""",
      """focalnet-xlarge-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth""",
      """focalnet-xlarge-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth""",
  }
  # fmt: on

  SCREAMING_SNAKE_CASE__										=      model_name_to_url[model_name]
  print("""Checkpoint URL: """ ,     __lowerCamelCase	)
  SCREAMING_SNAKE_CASE__										=      torch.hub.load_state_dict_from_url(__lowerCamelCase ,     map_location="""cpu"""	)["""model"""]

  # rename keys
  for key in state_dict.copy().keys():
    SCREAMING_SNAKE_CASE__										=      state_dict.pop(__lowerCamelCase	)
    SCREAMING_SNAKE_CASE__										=      val

  SCREAMING_SNAKE_CASE__										=      get_focalnet_config(__lowerCamelCase	)
  SCREAMING_SNAKE_CASE__										=      FocalNetForImageClassification(__lowerCamelCase	)
  model.eval()

  # load state dict
  model.load_state_dict(__lowerCamelCase	)

  # verify conversion
  SCREAMING_SNAKE_CASE__										=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

  SCREAMING_SNAKE_CASE__										=      BitImageProcessor(
      do_resize=__lowerCamelCase ,     size={"""shortest_edge""": 2_56} ,     resample=PILImageResampling.BILINEAR ,     do_center_crop=__lowerCamelCase ,     crop_size=2_24 ,     do_normalize=__lowerCamelCase ,     image_mean=__lowerCamelCase ,     image_std=__lowerCamelCase ,     )
  SCREAMING_SNAKE_CASE__										=      Image.open(requests.get(__lowerCamelCase ,     stream=__lowerCamelCase	).raw	)
  SCREAMING_SNAKE_CASE__										=      processor(images=__lowerCamelCase ,     return_tensors="""pt"""	)

  SCREAMING_SNAKE_CASE__										=      transforms.Compose(
      [
          transforms.Resize(2_56	),
          transforms.CenterCrop(2_24	),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406] ,     std=[0.229, 0.224, 0.225]	),
      ]	)

  SCREAMING_SNAKE_CASE__										=      image_transforms(__lowerCamelCase	).unsqueeze(0	)

  # verify pixel_values
  assert torch.allclose(inputs.pixel_values ,     __lowerCamelCase ,     atol=1E-4	)

  SCREAMING_SNAKE_CASE__										=      model(**__lowerCamelCase	)

  SCREAMING_SNAKE_CASE__										=      outputs.logits.argmax(-1	).item()
  print("""Predicted class:""" ,     model.config.idalabel[predicted_class_idx]	)

  print("""First values of logits:""" ,     outputs.logits[0, :3]	)

  if model_name == "focalnet-tiny":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([0.2166, -0.4368, 0.2191]	)
  elif model_name == "focalnet-tiny-lrf":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([1.1669, 0.0125, -0.1695]	)
  elif model_name == "focalnet-small":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([0.4917, -0.0430, 0.1341]	)
  elif model_name == "focalnet-small-lrf":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([-0.2588, -0.5342, -0.2331]	)
  elif model_name == "focalnet-base":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([-0.1655, -0.4090, -0.1730]	)
  elif model_name == "focalnet-base-lrf":
    SCREAMING_SNAKE_CASE__										=      torch.tensor([0.5306, -0.0483, -0.3928]	)
  assert torch.allclose(outputs.logits[0, :3] ,     __lowerCamelCase ,     atol=1E-4	)
  print("""Looks ok!"""	)

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(__lowerCamelCase	)
    processor.save_pretrained(__lowerCamelCase	)

  if push_to_hub:
    print(f"""Pushing model and processor of {model_name} to the hub..."""	)
    model.push_to_hub(f"""{model_name}"""	)
    processor.push_to_hub(f"""{model_name}"""	)


if __name__ == "__main__":
   __lowerCamelCase			: List[Any]							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''focalnet-tiny''',
       type=str,
       help='''Name of the FocalNet model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
       help='''Whether to push the model and processor to the hub.''',
   )

   __lowerCamelCase			: str							   =		parser.parse_args()
   convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

from __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			int(math.sqrt(__lowerCamelCase    )    )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(__lowerCamelCase    )

            # Set multiples of start be False
            for i in range(start * start					,       num + 1					,       __lowerCamelCase    ):
                if sieve[i] is True:
                    _SCREAMING_SNAKE_CASE									=			False

        start += 1

    for j in range(end + 1					,       num + 1    ):
        if sieve[j] is True:
            prime.append(__lowerCamelCase    )

    return prime


if __name__ == "__main__":
      print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



