




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 				_A  (		lowerCAmelCase_	:   Optional[int]     ):
     """simple docstring"""

     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__            =					2_1841
     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 				_A  (		lowerCAmelCase_	:   int     ):
     """simple docstring"""

     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 				_A  (		lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]     ):
     """simple docstring"""

     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 				_A  (		lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   List[Any]     ):
     """simple docstring"""

     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__":
     UpperCamelCase								=	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.'
     )

     UpperCamelCase								=	parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


"""simple docstring"""


import math






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [True] * n
			lowerCamelCase_					=  False
			lowerCamelCase_					=  False
			lowerCamelCase_					=  True

			for i in range(3	,int(n**0.5 + 1						)	,2						):
						lowerCamelCase_					=  i * 2
						while index < n:
									lowerCamelCase_					=  False
									lowerCamelCase_					=  index + i

			lowerCamelCase_					=  [2]

			for i in range(3	,lowerCAmelCase__	,2						):
						if is_prime[i]:
									primes.append(lowerCAmelCase__						)

			return primes






def  lowercase						(	lowerCAmelCase__ = 999_966_663_333						):
			lowerCamelCase_					=  math.floor(math.sqrt(lowerCAmelCase__						)						) + 100
			lowerCamelCase_					=  prime_sieve(lowerCAmelCase__						)

			lowerCamelCase_					=  0
			lowerCamelCase_					=  0
			lowerCamelCase_					=  primes[prime_index]

			while (last_prime**2) <= limit:
						lowerCamelCase_					=  primes[prime_index + 1]

						lowerCamelCase_					=  last_prime**2
						lowerCamelCase_					=  next_prime**2

						# Get numbers divisible by lps(current)
						lowerCamelCase_					=  lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						lowerCamelCase_					=  upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						lowerCamelCase_					=  0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						lowerCamelCase_					=  next_prime
						prime_index += 1

			return matches_sum


if __name__ == "__main__":
						print(solution())



