





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()
SCREAMING_SNAKE_CASE__							:      Any         =    logging.get_logger()






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase = True ):
   print(F"""Converting {name}...""" )

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

      from_model.eval()
      a__						:    Optional[Any]   		=	LevitForImageClassificationWithTeacher(lowerCamelCase ).eval()
      a__						:    Any   		=	OrderedDict()

      a__						:    Tuple   		=	from_model.state_dict()
      a__						:    List[str]   		=	list(from_model.state_dict().keys() )
      a__						:    int   		=	list(our_model.state_dict().keys() )
      print(len(lowerCamelCase )				,			len(lowerCamelCase ) )
      for i in range(len(lowerCamelCase ) ):
         a__						:    List[str]   		=	weights[og_keys[i]]
      our_model.load_state_dict(lowerCamelCase )

      a__						:    Any   		=	torch.randn((2, 3, 224, 224) )
      a__						:    Optional[Any]   		=	from_model(lowerCamelCase )
      a__						:    Any   		=	our_model(lowerCamelCase ).logits

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

   a__						:    List[Any]   		=	name
   print(lowerCamelCase )

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

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






def    _A	(   lowerCamelCase				,			lowerCamelCase = None				,			lowerCamelCase = True ):
   a__						:    List[Any]   		=	"imagenet-1k-id2label.json"
   a__						:    Tuple   		=	1000
   a__						:    Optional[Any]   		=	(1, num_labels)

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

   a__						:    List[str]   		=	idalabel
   a__						:    Union[str, Any]   		=	{v: k for k, v in idalabel.items()}

   a__						:    int   		=	partial(lowerCamelCase				,			num_labels=lowerCamelCase				,			idalabel=lowerCamelCase				,			labelaid=lowerCamelCase )

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

   a__						:    Tuple   		=	{
       "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__":
  SCREAMING_SNAKE_CASE__							:      str         =    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""",
  )

  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  SCREAMING_SNAKE_CASE__							:      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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__							:      Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

