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()
UpperCamelCase__											=					logging.get_logger()




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A = True							)     ->				str:







      '''simple docstring'''


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

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

            from_model.eval()
            UpperCAmelCase__								=  LevitForImageClassificationWithTeacher(__A							).eval()
            UpperCAmelCase__								=  OrderedDict()

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

            UpperCAmelCase__								=  torch.randn((2, 3, 224, 224)							)
            UpperCAmelCase__								=  from_model(__A							)
            UpperCAmelCase__								=  our_model(__A							).logits

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

      UpperCAmelCase__								=  name
      print(__A							)

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

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




def    lowerCAmelCase_ (     __A,  __A = None,  __A = True							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  (1, num_labels)

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

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

      UpperCAmelCase__								=  partial(__A,  num_labels=__A,  idalabel=__A,  labelaid=__A							)

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

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


if __name__ == "__main__":
      UpperCamelCase__											=					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',
      )

      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					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)




import math




def    lowerCAmelCase_ (     __A							)     ->				bool:







      '''simple docstring'''


      return math.sqrt(__A							) * math.sqrt(__A							) == num




def    lowerCAmelCase_ (     __A							)     ->				bool:







      '''simple docstring'''


      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  n
      while left <= right:
            UpperCAmelCase__								=  (left + right) // 2
            if mid**2 == n:
                  return True
            elif mid**2 > n:
                  UpperCAmelCase__								=  mid - 1
            else:
                  UpperCAmelCase__								=  mid + 1
      return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



