






"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any] )      ->				str:
  lowerCamelCase_         =						SwinConfig()
  lowerCamelCase_         =						swin_name.split('_' )

  lowerCamelCase_         =						name_split[1]
  lowerCamelCase_         =						int(name_split[4] )
  lowerCamelCase_         =						int(name_split[3][-1] )

  if model_size == "tiny":
    lowerCamelCase_         =						96
    lowerCamelCase_         =						(2, 2, 6, 2)
    lowerCamelCase_         =						(3, 6, 12, 24)
  elif model_size == "small":
    lowerCamelCase_         =						96
    lowerCamelCase_         =						(2, 2, 18, 2)
    lowerCamelCase_         =						(3, 6, 12, 24)
  elif model_size == "base":
    lowerCamelCase_         =						128
    lowerCamelCase_         =						(2, 2, 18, 2)
    lowerCamelCase_         =						(4, 8, 16, 32)
  else:
    lowerCamelCase_         =						192
    lowerCamelCase_         =						(2, 2, 18, 2)
    lowerCamelCase_         =						(6, 12, 24, 48)

  if "in22k" in swin_name:
    lowerCamelCase_         =						21841
  else:
    lowerCamelCase_         =						1000
    lowerCamelCase_         =						'huggingface/label-files'
    lowerCamelCase_         =						'imagenet-1k-id2label.json'
    lowerCamelCase_         =						json.load(open(hf_hub_download(_lowerCamelCase		,				_lowerCamelCase		,				repo_type='dataset' )		,				'r' ) )
    lowerCamelCase_         =						{int(_lowerCamelCase ): v for k, v in idalabel.items()}
    lowerCamelCase_         =						idalabel
    lowerCamelCase_         =						{v: k for k, v in idalabel.items()}

  lowerCamelCase_         =						img_size
  lowerCamelCase_         =						num_classes
  lowerCamelCase_         =						embed_dim
  lowerCamelCase_         =						depths
  lowerCamelCase_         =						num_heads
  lowerCamelCase_         =						window_size

  return config







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any] )      ->				List[Any]:
  if "patch_embed.proj" in name:
    lowerCamelCase_         =						name.replace('patch_embed.proj'		,				'embeddings.patch_embeddings.projection' )
  if "patch_embed.norm" in name:
    lowerCamelCase_         =						name.replace('patch_embed.norm'		,				'embeddings.norm' )
  if "layers" in name:
    lowerCamelCase_         =						'encoder.' + name
  if "attn.proj" in name:
    lowerCamelCase_         =						name.replace('attn.proj'		,				'attention.output.dense' )
  if "attn" in name:
    lowerCamelCase_         =						name.replace('attn'		,				'attention.self' )
  if "norm1" in name:
    lowerCamelCase_         =						name.replace('norm1'		,				'layernorm_before' )
  if "norm2" in name:
    lowerCamelCase_         =						name.replace('norm2'		,				'layernorm_after' )
  if "mlp.fc1" in name:
    lowerCamelCase_         =						name.replace('mlp.fc1'		,				'intermediate.dense' )
  if "mlp.fc2" in name:
    lowerCamelCase_         =						name.replace('mlp.fc2'		,				'output.dense' )

  if name == "norm.weight":
    lowerCamelCase_         =						'layernorm.weight'
  if name == "norm.bias":
    lowerCamelCase_         =						'layernorm.bias'

  if "head" in name:
    lowerCamelCase_         =						name.replace('head'		,				'classifier' )
  else:
    lowerCamelCase_         =						'swin.' + name

  return name







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		str )      ->				Tuple:
  for key in orig_state_dict.copy().keys():
    lowerCamelCase_         =						orig_state_dict.pop(_lowerCamelCase )

    if "mask" in key:
      continue
    elif "qkv" in key:
      lowerCamelCase_         =						key.split('.' )
      lowerCamelCase_         =						int(key_split[1] )
      lowerCamelCase_         =						int(key_split[3] )
      lowerCamelCase_         =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        lowerCamelCase_         =						val[:dim, :]
        lowerCamelCase_         =						val[
            dim : dim * 2, :
        ]
        lowerCamelCase_         =						val[-dim:, :]
      else:
        lowerCamelCase_         =						val[
            :dim
        ]
        lowerCamelCase_         =						val[
            dim : dim * 2
        ]
        lowerCamelCase_         =						val[
            -dim:
        ]
    else:
      lowerCamelCase_         =						val

  return orig_state_dict







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		int )      ->				Optional[Any]:
  lowerCamelCase_         =						timm.create_model(_lowerCamelCase		,				pretrained=_lowerCamelCase )
  timm_model.eval()

  lowerCamelCase_         =						get_swin_config(_lowerCamelCase )
  lowerCamelCase_         =						SwinForImageClassification(_lowerCamelCase )
  model.eval()

  lowerCamelCase_         =						convert_state_dict(timm_model.state_dict()		,				_lowerCamelCase )
  model.load_state_dict(_lowerCamelCase )

  lowerCamelCase_         =						'http://images.cocodataset.org/val2017/000000039769.jpg'

  lowerCamelCase_         =						AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'		,				'-' ) ) )
  lowerCamelCase_         =						Image.open(requests.get(_lowerCamelCase		,				stream=_lowerCamelCase ).raw )
  lowerCamelCase_         =						image_processor(images=_lowerCamelCase		,				return_tensors='pt' )

  lowerCamelCase_         =						timm_model(inputs['pixel_values'] )
  lowerCamelCase_         =						model(**_lowerCamelCase ).logits

  assert torch.allclose(_lowerCamelCase		,				_lowerCamelCase		,				atol=1e-3 )

  print(F'''Saving model {swin_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 __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				int					    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--swin_name''',
      default='''swin_tiny_patch4_window7_224''',
      type=str,
      help='''Name of the Swin timm 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.'''
  )

  _SCREAMING_SNAKE_CASE					:				int					    =						parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)












"""simple docstring"""





from __future__ import annotations

import math







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5		,				int(math.sqrt(_lowerCamelCase ) + 1 )		,				6 ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						str(_lowerCamelCase )
  lowerCamelCase_         =						[n]
  for i in range(1		,				len(_lowerCamelCase ) ):
    list_nums.append(int(str_num[i:] ) )
    list_nums.append(int(str_num[:-i] ) )
  return list_nums







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				bool:
  if len(str(_lowerCamelCase ) ) > 3:
    if not is_prime(int(str(_lowerCamelCase )[-3:] ) ) or not is_prime(int(str(_lowerCamelCase )[:3] ) ):
      return False
  return True







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 11 )      ->				list[int]:
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						13
  while len(_lowerCamelCase ) != count:
    if validate(_lowerCamelCase ):
      lowerCamelCase_         =						list_truncated_nums(_lowerCamelCase )
      if all(is_prime(_lowerCamelCase ) for i in list_nums ):
        list_truncated_primes.append(_lowerCamelCase )
    num += 2
  return list_truncated_primes







def      lowerCamelCase__       (							)      ->				int:
  return sum(compute_truncated_primes(11 ) )


if __name__ == "__main__":
  print(F'''{sum(compute_truncated_primes(11)) = }''')




