


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[Any]		=					logging.get_logger()

def       lowercase_		(		_A			:    int    ,					_A			:    str    ,					_A			:    LevitConfig    ,					_A			:    Path    ,					_A			:    bool = True						):

      """simple docstring"""




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

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

            from_model.eval()
            lowerCamelCase__		:				Union[str, Any]    =  LevitForImageClassificationWithTeacher(_A						).eval()
            lowerCamelCase__		:				Optional[Any]    =  OrderedDict()

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

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

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

      lowerCamelCase__		:				Dict    =  name
      print(_A						)

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

            print(F"Pushed {checkpoint_name}"						)

def       lowercase_		(		_A			:    Path    ,					_A			:    str = None    ,					_A			:    bool = True						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  "imagenet-1k-id2label.json"
      lowerCamelCase__		:				List[str]    =  1000
      lowerCamelCase__		:				Tuple    =  (1, num_labels)

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

      lowerCamelCase__		:				Optional[Any]    =  idalabel
      lowerCamelCase__		:				int    =  {v: k for k, v in idalabel.items()}

      lowerCamelCase__		:				Tuple    =  partial(_A    ,					num_labels=_A    ,					idalabel=_A    ,					labelaid=_A						)

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

      lowerCamelCase__		:				Any    =  {
          "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__":
    A      :    Optional[Any]		=					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      :    int		=					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)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A      :    str		=					logging.get_logger(__name__)

A      :    int		=					{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"mgp-str"





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			List[Any]=[32, 128]					,      __lowerCamelCase							:			Optional[int]=4					,      __lowerCamelCase							:			str=3					,      __lowerCamelCase							:			Optional[Any]=27					,      __lowerCamelCase							:			List[str]=38					,      __lowerCamelCase							:			Dict=50257					,      __lowerCamelCase							:			List[Any]=30522					,      __lowerCamelCase							:			Optional[Any]=768					,      __lowerCamelCase							:			str=12					,      __lowerCamelCase							:			Optional[int]=12					,      __lowerCamelCase							:			List[str]=4.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			Dict=1E-5					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			Optional[int]=0.0					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			Union[str, Any]=False					,      __lowerCamelCase							:			str=0.0_2					,      **__lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase       )

             lowerCamelCase__		:				int    =  image_size
             lowerCamelCase__		:				Union[str, Any]    =  patch_size
             lowerCamelCase__		:				Dict    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  max_token_length
             lowerCamelCase__		:				Optional[int]    =  num_character_labels
             lowerCamelCase__		:				Union[str, Any]    =  num_bpe_labels
             lowerCamelCase__		:				Optional[int]    =  num_wordpiece_labels
             lowerCamelCase__		:				List[str]    =  hidden_size
             lowerCamelCase__		:				Dict    =  num_hidden_layers
             lowerCamelCase__		:				Any    =  num_attention_heads
             lowerCamelCase__		:				Any    =  mlp_ratio
             lowerCamelCase__		:				List[Any]    =  distilled
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  drop_rate
             lowerCamelCase__		:				List[Any]    =  qkv_bias
             lowerCamelCase__		:				int    =  attn_drop_rate
             lowerCamelCase__		:				List[Any]    =  drop_path_rate
             lowerCamelCase__		:				List[str]    =  output_aa_attentions
             lowerCamelCase__		:				Dict    =  initializer_range


