


'''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 __snake_case					(      UpperCAmelCase_						:						Dict		):
 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(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(SCREAMING_SNAKE_CASE_		): 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 __snake_case					(      UpperCAmelCase_						:						List[str]		):

 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 __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Optional[int]		):

 for key in orig_state_dict.copy().keys():
  lowerCamelCase_      =  orig_state_dict.pop(SCREAMING_SNAKE_CASE_		)

  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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  timm.create_model(SCREAMING_SNAKE_CASE_					,				pretrained=SCREAMING_SNAKE_CASE_		)
 timm_model.eval()

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

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

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

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

 lowerCamelCase_      =  timm_model(inputs["pixel_values"]		)
 lowerCamelCase_      =  model(**SCREAMING_SNAKE_CASE_		).logits

 assert torch.allclose(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				atol=1E-3		)

 print(F'''Saving model {swin_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_		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= 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."""
     )

     a_  :				List[Any]						= parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


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



