


"""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 				_snake_case      (   __snake_case							:      int						, __snake_case							:      str						, __snake_case							:      LevitConfig						, __snake_case							:      Path						, __snake_case							:      bool = True   ):




   """simple docstring"""







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

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

      from_model.eval()
      _lowerCamelCase	:					List[Any]   =		LevitForImageClassificationWithTeacher(__snake_case   ).eval()
      _lowerCamelCase	:					Union[str, Any]   =		OrderedDict()

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

      _lowerCamelCase	:					Dict   =		torch.randn((2, 3, 224, 224)   )
      _lowerCamelCase	:					Optional[int]   =		from_model(__snake_case   )
      _lowerCamelCase	:					Optional[int]   =		our_model(__snake_case   ).logits

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

   _lowerCamelCase	:					str   =		name
   print(__snake_case   )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name   )
      _lowerCamelCase	:					Optional[int]   =		LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name   )

      print(F'Pushed {checkpoint_name}'   )

def 				_snake_case      (   __snake_case							:      Path						, __snake_case							:      str = None						, __snake_case							:      bool = True   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		"""imagenet-1k-id2label.json"""
   _lowerCamelCase	:					Optional[Any]   =		1000
   _lowerCamelCase	:					Any   =		(1, num_labels)

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

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

   _lowerCamelCase	:					int   =		partial(__snake_case						, num_labels=__snake_case						, idalabel=__snake_case						, labelaid=__snake_case   )

   _lowerCamelCase	:					Optional[Any]   =		{
       """levit-128S""": 128,
       """levit-128""": 128,
       """levit-192""": 192,
       """levit-256""": 256,
       """levit-384""": 384,
   }

   _lowerCamelCase	:					Optional[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]						, __snake_case						, names_to_config[model_name]						, __snake_case						, __snake_case   )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]						, __snake_case						, __snake_case						, __snake_case						, __snake_case   )
   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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		num_hidden_layers
      _lowerCamelCase	:					int   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					str   =		intermediate_size
      _lowerCamelCase	:					Union[str, Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		type_vocab_size
      _lowerCamelCase	:					List[Any]   =		initializer_range
      _lowerCamelCase	:					Dict   =		layer_norm_eps
      _lowerCamelCase	:					List[Any]   =		position_embedding_type
      _lowerCamelCase	:					Any   =		quant_mode
      _lowerCamelCase	:					List[str]   =		force_dequant







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

