





'''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 							UpperCAmelCase_  (						__lowerCamelCase : Tuple			):
   lowercase_      :str         =			SwinConfig()
   lowercase_      :int         =			swin_name.split("_"			)

   lowercase_      :List[Any]         =			name_split[1]
   lowercase_      :Dict         =			int(name_split[4]			)
   lowercase_      :int         =			int(name_split[3][-1]			)

   if model_size == "tiny":
      lowercase_      :Dict         =			96
      lowercase_      :List[Any]         =			(2, 2, 6, 2)
      lowercase_      :Dict         =			(3, 6, 12, 24)
   elif model_size == "small":
      lowercase_      :Union[str, Any]         =			96
      lowercase_      :Optional[Any]         =			(2, 2, 18, 2)
      lowercase_      :List[str]         =			(3, 6, 12, 24)
   elif model_size == "base":
      lowercase_      :Any         =			1_28
      lowercase_      :List[str]         =			(2, 2, 18, 2)
      lowercase_      :Dict         =			(4, 8, 16, 32)
   else:
      lowercase_      :Optional[Any]         =			1_92
      lowercase_      :Any         =			(2, 2, 18, 2)
      lowercase_      :List[Any]         =			(6, 12, 24, 48)

   if "in22k" in swin_name:
      lowercase_      :Any         =			2_18_41
   else:
      lowercase_      :Optional[Any]         =			10_00
      lowercase_      :Optional[Any]         =			"huggingface/label-files"
      lowercase_      :Optional[Any]         =			"imagenet-1k-id2label.json"
      lowercase_      :List[str]         =			json.load(open(hf_hub_download(lowerCamelCase__     ,lowerCamelCase__     ,repo_type="dataset"			)     ,"r"			)			)
      lowercase_      :Tuple         =			{int(lowerCamelCase__			): v for k, v in idalabel.items()}
      lowercase_      :Tuple         =			idalabel
      lowercase_      :List[str]         =			{v: k for k, v in idalabel.items()}

   lowercase_      :Dict         =			img_size
   lowercase_      :Optional[int]         =			num_classes
   lowercase_      :int         =			embed_dim
   lowercase_      :str         =			depths
   lowercase_      :Dict         =			num_heads
   lowercase_      :Optional[int]         =			window_size

   return config
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):




   if "patch_embed.proj" in name:
      lowercase_      :List[str]         =			name.replace("patch_embed.proj"     ,"embeddings.patch_embeddings.projection"			)
   if "patch_embed.norm" in name:
      lowercase_      :List[Any]         =			name.replace("patch_embed.norm"     ,"embeddings.norm"			)
   if "layers" in name:
      lowercase_      :List[Any]         =			"encoder." + name
   if "attn.proj" in name:
      lowercase_      :Optional[int]         =			name.replace("attn.proj"     ,"attention.output.dense"			)
   if "attn" in name:
      lowercase_      :str         =			name.replace("attn"     ,"attention.self"			)
   if "norm1" in name:
      lowercase_      :List[Any]         =			name.replace("norm1"     ,"layernorm_before"			)
   if "norm2" in name:
      lowercase_      :Optional[Any]         =			name.replace("norm2"     ,"layernorm_after"			)
   if "mlp.fc1" in name:
      lowercase_      :List[str]         =			name.replace("mlp.fc1"     ,"intermediate.dense"			)
   if "mlp.fc2" in name:
      lowercase_      :List[Any]         =			name.replace("mlp.fc2"     ,"output.dense"			)

   if name == "norm.weight":
      lowercase_      :Optional[int]         =			"layernorm.weight"
   if name == "norm.bias":
      lowercase_      :Optional[int]         =			"layernorm.bias"

   if "head" in name:
      lowercase_      :str         =			name.replace("head"     ,"classifier"			)
   else:
      lowercase_      :Optional[Any]         =			"swin." + name

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[Any]			):




   for key in orig_state_dict.copy().keys():
      lowercase_      :Optional[Any]         =			orig_state_dict.pop(lowerCamelCase__			)

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

         if "weight" in key:
            lowercase_      :Optional[int]         =			val[:dim, :]
            lowercase_      :Union[str, Any]         =			val[
                dim : dim * 2, :
            ]
            lowercase_      :Union[str, Any]         =			val[-dim:, :]
         else:
            lowercase_      :Optional[int]         =			val[
                :dim
            ]
            lowercase_      :Union[str, Any]         =			val[
                dim : dim * 2
            ]
            lowercase_      :Optional[int]         =			val[
                -dim:
            ]
      else:
         lowercase_      :int         =			val

   return orig_state_dict
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple     ,__lowerCamelCase : str			):
   lowercase_      :Union[str, Any]         =			timm.create_model(lowerCamelCase__     ,pretrained=lowerCamelCase__			)
   timm_model.eval()

   lowercase_      :Any         =			get_swin_config(lowerCamelCase__			)
   lowercase_      :List[str]         =			SwinForImageClassification(lowerCamelCase__			)
   model.eval()

   lowercase_      :Dict         =			convert_state_dict(timm_model.state_dict()     ,lowerCamelCase__			)
   model.load_state_dict(lowerCamelCase__			)

   lowercase_      :List[Any]         =			"http://images.cocodataset.org/val2017/000000039769.jpg"

   lowercase_      :Optional[int]         =			AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"     ,"-"			)			)			)
   lowercase_      :Tuple         =			Image.open(requests.get(lowerCamelCase__     ,stream=lowerCamelCase__			).raw			)
   lowercase_      :Optional[int]         =			image_processor(images=lowerCamelCase__     ,return_tensors="pt"			)

   lowercase_      :Optional[Any]         =			timm_model(inputs["pixel_values"]			)
   lowercase_      :Any         =			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__":
 lowerCAmelCase :					Dict 							=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.'''
 )

 lowerCAmelCase :					Tuple 							=parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''


import sys

lowerCAmelCase :					List[Any] 							=(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   lowercase_      :List[str]         =			1
   for digit in s:
      product *= int(__lowerCamelCase			)
   return product
def 							UpperCAmelCase_  (						__lowerCamelCase : str = N			):
   lowercase_      :Any         =			-sys.maxsize - 1
   lowercase_      :int         =			n[:13]
   lowercase_      :Optional[int]         =			13
   while cur_index < len(__lowerCamelCase			) - 13:
      if int(n[cur_index]			) >= int(substr[0]			):
         lowercase_      :str         =			substr[1:] + n[cur_index]
         cur_index += 1
      else:
         lowercase_      :List[str]         =			max(__lowerCamelCase     ,str_eval(__lowerCamelCase			)			)
         lowercase_      :Any         =			n[cur_index : cur_index + 13]
         cur_index += 13
   return largest_product


if __name__ == "__main__":
 print(F'''{solution() = }''')


