'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a   :	List[str] 							=   logging.get_logger()

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__ = True						)					->       Optional[Any]:





       """simple docstring"""


       print(f"""Converting {name}..."""						)

       with torch.no_grad():
              if hidden_sizes == 128:
                     if name[-1] == "S":
                            __UpperCAmelCase  :				int       				=		timm.create_model("levit_128s"							,    pretrained=lowerCamelCase__						)
                     else:
                            __UpperCAmelCase  :				List[str]       				=		timm.create_model("levit_128"							,    pretrained=lowerCamelCase__						)
              if hidden_sizes == 192:
                     __UpperCAmelCase  :				Optional[int]       				=		timm.create_model("levit_192"							,    pretrained=lowerCamelCase__						)
              if hidden_sizes == 256:
                     __UpperCAmelCase  :				Tuple       				=		timm.create_model("levit_256"							,    pretrained=lowerCamelCase__						)
              if hidden_sizes == 384:
                     __UpperCAmelCase  :				Optional[Any]       				=		timm.create_model("levit_384"							,    pretrained=lowerCamelCase__						)

              from_model.eval()
              __UpperCAmelCase  :				Dict       				=		LevitForImageClassificationWithTeacher(lowerCamelCase__						).eval()
              __UpperCAmelCase  :				List[str]       				=		OrderedDict()

              __UpperCAmelCase  :				Any       				=		from_model.state_dict()
              __UpperCAmelCase  :				List[str]       				=		list(from_model.state_dict().keys()						)
              __UpperCAmelCase  :				List[str]       				=		list(our_model.state_dict().keys()						)
              print(len(lowerCamelCase__						)							,    len(lowerCamelCase__						)						)
              for i in range(len(lowerCamelCase__						)						):
                     __UpperCAmelCase  :				int       				=		weights[og_keys[i]]
              our_model.load_state_dict(lowerCamelCase__						)

              __UpperCAmelCase  :				List[str]       				=		torch.randn((2, 3, 224, 224)						)
              __UpperCAmelCase  :				List[Any]       				=		from_model(lowerCamelCase__						)
              __UpperCAmelCase  :				int       				=		our_model(lowerCamelCase__						).logits

       assert torch.allclose(lowerCamelCase__							,    lowerCamelCase__						), "The model logits don't match the original one."

       __UpperCAmelCase  :				Union[str, Any]       				=		name
       print(lowerCamelCase__						)

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name						)
              __UpperCAmelCase  :				Any       				=		LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name						)

              print(f"""Pushed {checkpoint_name}"""						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = None							,    lowerCamelCase__ = True						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		"imagenet-1k-id2label.json"
       __UpperCAmelCase  :				Optional[Any]       				=		1000
       __UpperCAmelCase  :				int       				=		(1, num_labels)

       __UpperCAmelCase  :				Tuple       				=		"huggingface/label-files"
       __UpperCAmelCase  :				List[Any]       				=		num_labels
       __UpperCAmelCase  :				Union[str, Any]       				=		json.load(open(hf_hub_download(lowerCamelCase__							,    lowerCamelCase__							,    repo_type="dataset"						)							,    "r"						)						)
       __UpperCAmelCase  :				Optional[int]       				=		{int(lowerCamelCase__						): v for k, v in idalabel.items()}

       __UpperCAmelCase  :				List[str]       				=		idalabel
       __UpperCAmelCase  :				List[Any]       				=		{v: k for k, v in idalabel.items()}

       __UpperCAmelCase  :				Optional[Any]       				=		partial(lowerCamelCase__							,    num_labels=lowerCamelCase__							,    idalabel=lowerCamelCase__							,    labelaid=lowerCamelCase__						)

       __UpperCAmelCase  :				Optional[int]       				=		{
           "levit-128S": 128,
           "levit-128": 128,
           "levit-192": 192,
           "levit-256": 256,
           "levit-384": 384,
       }

       __UpperCAmelCase  :				str       				=		{
           "levit-128S": ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]							,    num_attention_heads=[4, 6, 8]							,    depths=[2, 3, 4]							,    key_dim=[16, 16, 16]							,    drop_path_rate=0							,    ),
           "levit-128": ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]							,    num_attention_heads=[4, 8, 12]							,    depths=[4, 4, 4]							,    key_dim=[16, 16, 16]							,    drop_path_rate=0							,    ),
           "levit-192": ImageNetPreTrainedConfig(
               hidden_sizes=[192, 288, 384]							,    num_attention_heads=[3, 5, 6]							,    depths=[4, 4, 4]							,    key_dim=[32, 32, 32]							,    drop_path_rate=0							,    ),
           "levit-256": ImageNetPreTrainedConfig(
               hidden_sizes=[256, 384, 512]							,    num_attention_heads=[4, 6, 8]							,    depths=[4, 4, 4]							,    key_dim=[32, 32, 32]							,    drop_path_rate=0							,    ),
           "levit-384": ImageNetPreTrainedConfig(
               hidden_sizes=[384, 512, 768]							,    num_attention_heads=[6, 9, 12]							,    depths=[4, 4, 4]							,    key_dim=[32, 32, 32]							,    drop_path_rate=0.1							,    ),
       }

       if model_name:
              convert_weight_and_push(
                  names_to_hidden_sizes[model_name]							,    lowerCamelCase__							,    names_to_config[model_name]							,    lowerCamelCase__							,    lowerCamelCase__						)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(names_to_hidden_sizes[model_name]							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       return config, expected_shape


if __name__ == "__main__":
   _a   :	Tuple 							=   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default=None,
       type=str,
       help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="levit-dump-folder/",
       type=Path,
       required=False,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
   parser.add_argument(
       "--no-push_to_hub",
       dest="push_to_hub",
       action="store_false",
       help="Do not push model and image processor to the hub",
   )

   _a   :	Optional[Any] 							=   parser.parse_args()
   _a   :	Path 							=   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''




def _lowercase	(      lowerCamelCase__ = 100						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		(n * (n + 1) // 2) ** 2
       __UpperCAmelCase  :				Any       				=		n * (n + 1) * (2 * n + 1) // 6
       return sum_cubes - sum_squares


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






