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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = True   )		->						Optional[Any]:




       '''simple docstring'''






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

       with torch.no_grad():
              if hidden_sizes == 1_28:
                     if name[-1] == "S":
                            _A=	timm.create_model('levit_128s'       ,					pretrained=lowerCAmelCase_   )
                     else:
                            _A=	timm.create_model('levit_128'       ,					pretrained=lowerCAmelCase_   )
              if hidden_sizes == 1_92:
                     _A=	timm.create_model('levit_192'       ,					pretrained=lowerCAmelCase_   )
              if hidden_sizes == 2_56:
                     _A=	timm.create_model('levit_256'       ,					pretrained=lowerCAmelCase_   )
              if hidden_sizes == 3_84:
                     _A=	timm.create_model('levit_384'       ,					pretrained=lowerCAmelCase_   )

              from_model.eval()
              _A=	LevitForImageClassificationWithTeacher(lowerCAmelCase_   ).eval()
              _A=	OrderedDict()

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

              _A=	torch.randn((2, 3, 2_24, 2_24)   )
              _A=	from_model(lowerCAmelCase_   )
              _A=	our_model(lowerCAmelCase_   ).logits

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

       _A=	name
       print(lowerCAmelCase_   )

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

              print(F"Pushed {checkpoint_name}"   )

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = None       ,					lowerCAmelCase_ = True   )		->						Union[str, Any]:




       '''simple docstring'''






       _A=	'imagenet-1k-id2label.json'
       _A=	10_00
       _A=	(1, num_labels)

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

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

       _A=	partial(lowerCAmelCase_       ,					num_labels=lowerCAmelCase_       ,					idalabel=lowerCAmelCase_       ,					labelaid=lowerCAmelCase_   )

       _A=	{
           'levit-128S': 1_28,
           'levit-128': 1_28,
           'levit-192': 1_92,
           'levit-256': 2_56,
           'levit-384': 3_84,
       }

       _A=	{
           'levit-128S': ImageNetPreTrainedConfig(
               hidden_sizes=[1_28, 2_56, 3_84]       ,					num_attention_heads=[4, 6, 8]       ,					depths=[2, 3, 4]       ,					key_dim=[16, 16, 16]       ,					drop_path_rate=0       ,					),
           'levit-128': ImageNetPreTrainedConfig(
               hidden_sizes=[1_28, 2_56, 3_84]       ,					num_attention_heads=[4, 8, 12]       ,					depths=[4, 4, 4]       ,					key_dim=[16, 16, 16]       ,					drop_path_rate=0       ,					),
           'levit-192': ImageNetPreTrainedConfig(
               hidden_sizes=[1_92, 2_88, 3_84]       ,					num_attention_heads=[3, 5, 6]       ,					depths=[4, 4, 4]       ,					key_dim=[32, 32, 32]       ,					drop_path_rate=0       ,					),
           'levit-256': ImageNetPreTrainedConfig(
               hidden_sizes=[2_56, 3_84, 5_12]       ,					num_attention_heads=[4, 6, 8]       ,					depths=[4, 4, 4]       ,					key_dim=[32, 32, 32]       ,					drop_path_rate=0       ,					),
           'levit-384': ImageNetPreTrainedConfig(
               hidden_sizes=[3_84, 5_12, 7_68]       ,					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__":
      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)
UpperCAmelCase_								=							{
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}