





'''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   	=    logging.get_logger()

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase = True   )    ->       int:

   '''simple docstring'''







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

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

      from_model.eval()
      __SCREAMING_SNAKE_CASE   				=							LevitForImageClassificationWithTeacher(__UpperCAmelCase   ).eval()
      __SCREAMING_SNAKE_CASE   				=							OrderedDict()

      __SCREAMING_SNAKE_CASE   				=							from_model.state_dict()
      __SCREAMING_SNAKE_CASE   				=							list(from_model.state_dict().keys()   )
      __SCREAMING_SNAKE_CASE   				=							list(our_model.state_dict().keys()   )
      print(len(__UpperCAmelCase   )				,						len(__UpperCAmelCase   )   )
      for i in range(len(__UpperCAmelCase   )   ):
         __SCREAMING_SNAKE_CASE   				=							weights[og_keys[i]]
      our_model.load_state_dict(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							torch.randn((2, 3, 224, 224)   )
      __SCREAMING_SNAKE_CASE   				=							from_model(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							our_model(__UpperCAmelCase   ).logits

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

   __SCREAMING_SNAKE_CASE   				=							name
   print(__UpperCAmelCase   )

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase = None				,						__UpperCAmelCase = True   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""imagenet-1k-id2label.json"""
   __SCREAMING_SNAKE_CASE   				=							1000
   __SCREAMING_SNAKE_CASE   				=							(1, num_labels)

   __SCREAMING_SNAKE_CASE   				=							"""huggingface/label-files"""
   __SCREAMING_SNAKE_CASE   				=							num_labels
   __SCREAMING_SNAKE_CASE   				=							json.load(open(hf_hub_download(__UpperCAmelCase				,						__UpperCAmelCase				,						repo_type="""dataset"""   )				,						"""r"""   )   )
   __SCREAMING_SNAKE_CASE   				=							{int(__UpperCAmelCase   ): v for k, v in idalabel.items()}

   __SCREAMING_SNAKE_CASE   				=							idalabel
   __SCREAMING_SNAKE_CASE   				=							{v: k for k, v in idalabel.items()}

   __SCREAMING_SNAKE_CASE   				=							partial(__UpperCAmelCase				,						num_labels=__UpperCAmelCase				,						idalabel=__UpperCAmelCase				,						labelaid=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							{
       """levit-128S""": 128,
       """levit-128""": 128,
       """levit-192""": 192,
       """levit-256""": 256,
       """levit-384""": 384,
   }

   __SCREAMING_SNAKE_CASE   				=							{
       """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]				,						__UpperCAmelCase				,						names_to_config[model_name]				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   return config, expected_shape


if __name__ == "__main__":
    a   	=    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   	=    parser.parse_args()
    a   	=    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'''




from __future__ import annotations





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float		)		->  float:
   if days_between_payments <= 0:
      raise ValueError("""days_between_payments must be > 0"""		)
   if daily_interest_rate < 0:
      raise ValueError("""daily_interest_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)
   return principal * daily_interest_rate * days_between_payments





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_compounding_periods <= 0:
      raise ValueError("""number_of_compounding_periods must be > 0"""		)
   if nominal_annual_interest_rate_percentage < 0:
      raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return principal * (
       (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
       - 1
   )





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_years <= 0:
      raise ValueError("""number_of_years must be > 0"""		)
   if nominal_annual_percentage_rate < 0:
      raise ValueError("""nominal_annual_percentage_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return compound_interest(
       __lowerCAmelCase      ,    nominal_annual_percentage_rate / 3_65      ,    number_of_years * 3_65		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

