






'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str]    )  ->			str:

   """simple docstring"""
   __a				     =    SwinvaConfig()
   __a				     =    swinva_name.split('_'    )

   __a				     =    name_split[1]
   if "to" in name_split[3]:
      __a				     =    int(name_split[3][-3:]    )
   else:
      __a				     =    int(name_split[3]    )
   if "to" in name_split[2]:
      __a				     =    int(name_split[2][-2:]    )
   else:
      __a				     =    int(name_split[2][6:]    )

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

   if "to" in swinva_name:
      __a				     =    (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      __a				     =    21841
      __a				     =    'huggingface/label-files'
      __a				     =    'imagenet-22k-id2label.json'
      __a				     =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					repo_type='dataset'    ),					'r'    )    )
      __a				     =    {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}

   else:
      __a				     =    1000
      __a				     =    'huggingface/label-files'
      __a				     =    'imagenet-1k-id2label.json'
      __a				     =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					repo_type='dataset'    ),					'r'    )    )
      __a				     =    {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}

   __a				     =    img_size
   __a				     =    num_classes
   __a				     =    embed_dim
   __a				     =    depths
   __a				     =    num_heads
   __a				     =    window_size

   return config



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			Any:

   """simple docstring"""
   if "patch_embed.proj" in name:
      __a				     =    name.replace('patch_embed.proj',					'embeddings.patch_embeddings.projection'    )
   if "patch_embed.norm" in name:
      __a				     =    name.replace('patch_embed.norm',					'embeddings.norm'    )
   if "layers" in name:
      __a				     =    'encoder.' + name
   if "attn.proj" in name:
      __a				     =    name.replace('attn.proj',					'attention.output.dense'    )
   if "attn" in name:
      __a				     =    name.replace('attn',					'attention.self'    )
   if "norm1" in name:
      __a				     =    name.replace('norm1',					'layernorm_before'    )
   if "norm2" in name:
      __a				     =    name.replace('norm2',					'layernorm_after'    )
   if "mlp.fc1" in name:
      __a				     =    name.replace('mlp.fc1',					'intermediate.dense'    )
   if "mlp.fc2" in name:
      __a				     =    name.replace('mlp.fc2',					'output.dense'    )
   if "q_bias" in name:
      __a				     =    name.replace('q_bias',					'query.bias'    )
   if "k_bias" in name:
      __a				     =    name.replace('k_bias',					'key.bias'    )
   if "v_bias" in name:
      __a				     =    name.replace('v_bias',					'value.bias'    )
   if "cpb_mlp" in name:
      __a				     =    name.replace('cpb_mlp',					'continuous_position_bias_mlp'    )
   if name == "norm.weight":
      __a				     =    'layernorm.weight'
   if name == "norm.bias":
      __a				     =    'layernorm.bias'

   if "head" in name:
      __a				     =    name.replace('head',					'classifier'    )
   else:
      __a				     =    'swinv2.' + name

   return name



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	int    )  ->			List[str]:

   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      __a				     =    orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

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

   return orig_state_dict



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			int:

   """simple docstring"""
   __a				     =    timm.create_model(SCREAMING_SNAKE_CASE__,					pretrained=SCREAMING_SNAKE_CASE__    )
   timm_model.eval()

   __a				     =    get_swinva_config(SCREAMING_SNAKE_CASE__    )
   __a				     =    SwinvaForImageClassification(SCREAMING_SNAKE_CASE__    )
   model.eval()

   __a				     =    convert_state_dict(timm_model.state_dict(),					SCREAMING_SNAKE_CASE__    )
   model.load_state_dict(SCREAMING_SNAKE_CASE__    )

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

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

   __a				     =    timm_model(inputs['pixel_values']    )
   __a				     =    model(**SCREAMING_SNAKE_CASE__    ).logits

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

   print(f"""Saving model {swinva_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__    )

   model.push_to_hub(
       repo_path_or_name=Path(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ),					organization='nandwalritik',					commit_message='Add model',					)


if __name__ == "__main__":
 __UpperCamelCase      :       Dict      			=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swinv2_name""",
     default="""swinv2_tiny_patch4_window8_256""",
     type=str,
     help="""Name of the Swinv2 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."""
 )

 __UpperCamelCase      :       Union[str, Any]      			=      parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	bool = False    )  ->			bool:

   """simple docstring"""
   if n == 2:
      return True
   if not n % 2 or n < 2:
      return False
   if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
      return False
   if n > 3317044064679887385961981 and not allow_probable:
      raise ValueError(
          'Warning: upper bound of deterministic test is exceeded. '
          'Pass allow_probable=True to allow probabilistic test. '
          'A return value of True indicates a probable prime.'    )
   # array bounds provided by analysis
   __a				     =    [
       2047,
       1373653,
       25326001,
       3215031751,
       2152302898747,
       3474749660383,
       341550071728321,
       1,
       3825123056546413051,
       1,
       1,
       318665857834031151167461,
       3317044064679887385961981,
   ]

   __a				     =    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
   for idx, _p in enumerate(SCREAMING_SNAKE_CASE__,					1    ):
      if n < _p:
         # then we have our last prime to check
         __a				     =    primes[:idx]
         break
   __a     ,			__a				     =    n - 1, 0
   # break up n -1 into a power of 2 (s) and
   # remaining odd component
   # essentially, solve for d * 2 ** s == n - 1
   while d % 2 == 0:
      d //= 2
      s += 1
   for prime in plist:
      __a				     =    False
      for r in range(SCREAMING_SNAKE_CASE__    ):
         __a				     =    pow(SCREAMING_SNAKE_CASE__,					d * 2**r,					SCREAMING_SNAKE_CASE__    )
         # see article for analysis explanation for m
         if (r == 0 and m == 1) or ((m + 1) % n == 0):
            __a				     =    True
            # this loop will not determine compositeness
            break
      if pr:
         continue
      # if pr is False, then the above loop never evaluated to true,
      # and the n MUST be composite
      return False
   return True



def    __UpperCAmelCase	(				)  ->			None:

   """simple docstring"""
   assert not miller_rabin(561    )
   assert miller_rabin(563    )
   # 2047

   assert not miller_rabin(838201    )
   assert miller_rabin(838207    )
   # 1_373_653

   assert not miller_rabin(17316001    )
   assert miller_rabin(17316017    )
   # 25_326_001

   assert not miller_rabin(3078386641    )
   assert miller_rabin(3078386653    )
   # 3_215_031_751

   assert not miller_rabin(1713045574801    )
   assert miller_rabin(1713045574819    )
   # 2_152_302_898_747

   assert not miller_rabin(2779799728307    )
   assert miller_rabin(2779799728327    )
   # 3_474_749_660_383

   assert not miller_rabin(113850023909441    )
   assert miller_rabin(113850023909527    )
   # 341_550_071_728_321

   assert not miller_rabin(1275041018848804351    )
   assert miller_rabin(1275041018848804391    )
   # 3_825_123_056_546_413_051

   assert not miller_rabin(79666464458507787791867    )
   assert miller_rabin(79666464458507787791951    )
   # 318_665_857_834_031_151_167_461

   assert not miller_rabin(552840677446647897660333    )
   assert miller_rabin(552840677446647897660359    )
   # 3_317_044_064_679_887_385_961_981
   # upper limit for probabilistic test


if __name__ == "__main__":
 test_miller_rabin()