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()
lowerCAmelCase__	   =						logging.get_logger()



def      __lowercase				(      _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":
        __lowercase					=		timm.create_model("levit_128s"			, pretrained=__lowerCAmelCase			)
      else:
        __lowercase					=		timm.create_model("levit_128"			, pretrained=__lowerCAmelCase			)
    if hidden_sizes == 192:
      __lowercase					=		timm.create_model("levit_192"			, pretrained=__lowerCAmelCase			)
    if hidden_sizes == 256:
      __lowercase					=		timm.create_model("levit_256"			, pretrained=__lowerCAmelCase			)
    if hidden_sizes == 384:
      __lowercase					=		timm.create_model("levit_384"			, pretrained=__lowerCAmelCase			)

    from_model.eval()
    __lowercase					=		LevitForImageClassificationWithTeacher(__lowerCAmelCase			).eval()
    __lowercase					=		OrderedDict()

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

    __lowercase					=		torch.randn((2, 3, 224, 224)			)
    __lowercase					=		from_model(__lowerCAmelCase			)
    __lowercase					=		our_model(__lowerCAmelCase			).logits

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

  __lowercase					=		name
  print(__lowerCAmelCase			)

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

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



def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase = None			, _UpperCAmelCase = True			)      ->     List[Any]:






  '''simple docstring'''

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

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

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

  __lowercase					=		partial(__lowerCAmelCase			, num_labels=__lowerCAmelCase			, idalabel=__lowerCAmelCase			, labelaid=__lowerCAmelCase			)

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

  __lowercase					=		{
      "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__":
     lowerCAmelCase__	   =						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',
     )

     lowerCAmelCase__	   =						parser.parse_args()
     lowerCAmelCase__	   =						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 . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

