"""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()
UpperCAmelCase__  		=						logging.get_logger()






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase = True		):







  """simple docstring"""
  print(f'''Converting {name}...'''		)

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

    from_model.eval()
    _UpperCAmelCase						 =      LevitForImageClassificationWithTeacher(lowercase		).eval()
    _UpperCAmelCase						 =      OrderedDict()

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

    _UpperCAmelCase						 =      torch.randn((2, 3, 2_24, 2_24)		)
    _UpperCAmelCase						 =      from_model(lowercase		)
    _UpperCAmelCase						 =      our_model(lowercase		).logits

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

  _UpperCAmelCase						 =      name
  print(lowercase		)

  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     __UpperCAmelCase					(			lowercase   ,lowercase = None   ,lowercase = True		):







  """simple docstring"""
  _UpperCAmelCase						 =      """imagenet-1k-id2label.json"""
  _UpperCAmelCase						 =      10_00
  _UpperCAmelCase						 =      (1, num_labels)

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

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

  _UpperCAmelCase						 =      partial(lowercase   ,num_labels=lowercase   ,idalabel=lowercase   ,labelaid=lowercase		)

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

  _UpperCAmelCase						 =      {
      """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]   ,lowercase   ,names_to_config[model_name]   ,lowercase   ,lowercase		)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]   ,lowercase   ,lowercase   ,lowercase   ,lowercase		)
  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)

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):
    _snake_case     :  Union[str, Any]     							=							'nat'

    _snake_case     :  List[str]     							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  int=4   ,				__lowerCAmelCase	:  Union[str, Any]=3   ,				__lowerCAmelCase	:  Dict=64   ,				__lowerCAmelCase	:  int=[3, 4, 6, 5]   ,				__lowerCAmelCase	:  List[str]=[2, 4, 8, 16]   ,				__lowerCAmelCase	:  Tuple=7   ,				__lowerCAmelCase	:  List[str]=3.0   ,				__lowerCAmelCase	:  int=True   ,				__lowerCAmelCase	:  int=0.0   ,				__lowerCAmelCase	:  Tuple=0.0   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  List[str]="gelu"   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Union[str, Any]=1e-5   ,				__lowerCAmelCase	:  List[Any]=0.0   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  Optional[Any]   ,				):
      super().__init__(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      patch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      embed_dim
      _UpperCAmelCase						 =      depths
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      _UpperCAmelCase						 =      num_heads
      _UpperCAmelCase						 =      kernel_size
      _UpperCAmelCase						 =      mlp_ratio
      _UpperCAmelCase						 =      qkv_bias
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob
      _UpperCAmelCase						 =      drop_path_rate
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      layer_norm_eps
      _UpperCAmelCase						 =      initializer_range
      # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _UpperCAmelCase						 =      int(embed_dim * 2 ** (len(__lowerCAmelCase					) - 1)					)
      _UpperCAmelCase						 =      layer_scale_init_value
      _UpperCAmelCase						 =      ["""stem"""] + [f'''stage{idx}''' for idx in range(1   ,				len(__lowerCAmelCase					) + 1					)]
      _UpperCAmelCase ,				_UpperCAmelCase						 =      get_aligned_output_features_output_indices(
          out_features=__lowerCAmelCase   ,				out_indices=__lowerCAmelCase   ,				stage_names=self.stage_names					)
