


'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   if "model" in orig_key:
      _UpperCAmelCase       :       List[Any]							   =orig_key.replace('model.'	,   ''							)
   if "norm1" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('norm1'	,   'attention.output.LayerNorm'							)
   if "norm2" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('norm2'	,   'output.LayerNorm'							)
   if "norm" in orig_key:
      _UpperCAmelCase       :       List[Any]							   =orig_key.replace('norm'	,   'LayerNorm'							)
   if "transformer" in orig_key:
      _UpperCAmelCase       :       List[str]							   =orig_key.split('.'							)[0].split('_'							)[-1]
      _UpperCAmelCase       :       int							   =orig_key.replace(f"transformer_{layer_num}"	,   f"encoder.layer.{layer_num}"							)
   if "mha.attn" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('mha.attn'	,   'attention.self'							)
   if "mha" in orig_key:
      _UpperCAmelCase       :       List[Any]							   =orig_key.replace('mha'	,   'attention'							)
   if "W_q" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('W_q'	,   'self.query'							)
   if "W_k" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('W_k'	,   'self.key'							)
   if "W_v" in orig_key:
      _UpperCAmelCase       :       str							   =orig_key.replace('W_v'	,   'self.value'							)
   if "ff1" in orig_key:
      _UpperCAmelCase       :       Dict							   =orig_key.replace('ff1'	,   'intermediate.dense'							)
   if "ff2" in orig_key:
      _UpperCAmelCase       :       int							   =orig_key.replace('ff2'	,   'output.dense'							)
   if "ff" in orig_key:
      _UpperCAmelCase       :       Tuple							   =orig_key.replace('ff'	,   'output.dense'							)
   if "mlm_class" in orig_key:
      _UpperCAmelCase       :       List[str]							   =orig_key.replace('mlm.mlm_class'	,   'cls.predictions.decoder'							)
   if "mlm" in orig_key:
      _UpperCAmelCase       :       str							   =orig_key.replace('mlm'	,   'cls.predictions.transform'							)
   if "cls" not in orig_key:
      _UpperCAmelCase       :       int							   ='yoso.' + orig_key

   return orig_key





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]							):







   '''simple docstring'''




   for key in orig_state_dict.copy().keys():
      _UpperCAmelCase       :       int							   =orig_state_dict.pop(__lowerCamelCase							)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         _UpperCAmelCase       :       List[Any]							   =val

   _UpperCAmelCase       :       Optional[int]							   =orig_state_dict['cls.predictions.decoder.bias']
   _UpperCAmelCase       :       List[str]							   =torch.arange(__lowerCamelCase							).expand((1, -1)							) + 2

   return orig_state_dict





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =torch.load(__lowerCamelCase	,   map_location='cpu'							)['model_state_dict']
   _UpperCAmelCase       :       str							   =YosoConfig.from_json_file(__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =YosoForMaskedLM(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =convert_checkpoint_helper(config.max_position_embeddings	,   __lowerCamelCase							)

   print(model.load_state_dict(__lowerCamelCase							)							)
   model.eval()
   model.save_pretrained(__lowerCamelCase							)

   print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)


if __name__ == "__main__":
 lowercase							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The json file for YOSO model config.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 lowercase							=parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase							=logging.get_logger(__name__)

lowercase							={
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="levit"






       def __init__(      self  ,							snake_case=2_2_4  ,							snake_case=3  ,							snake_case=3  ,							snake_case=2  ,							snake_case=1  ,							snake_case=1_6  ,							snake_case=[1_2_8, 2_5_6, 3_8_4]  ,							snake_case=[4, 8, 1_2]  ,							snake_case=[4, 4, 4]  ,							snake_case=[1_6, 1_6, 1_6]  ,							snake_case=0  ,							snake_case=[2, 2, 2]  ,							snake_case=[2, 2, 2]  ,							snake_case=0.02  ,							**snake_case  ,							)						->  Any:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase       :       List[str]							   =image_size
          _UpperCAmelCase       :       str							   =num_channels
          _UpperCAmelCase       :       int							   =kernel_size
          _UpperCAmelCase       :       Any							   =stride
          _UpperCAmelCase       :       Tuple							   =padding
          _UpperCAmelCase       :       Optional[int]							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =num_attention_heads
          _UpperCAmelCase       :       List[Any]							   =depths
          _UpperCAmelCase       :       Any							   =key_dim
          _UpperCAmelCase       :       List[Any]							   =drop_path_rate
          _UpperCAmelCase       :       int							   =patch_size
          _UpperCAmelCase       :       Tuple							   =attention_ratio
          _UpperCAmelCase       :       Any							   =mlp_ratio
          _UpperCAmelCase       :       Optional[Any]							   =initializer_range
          _UpperCAmelCase       :       str							   =[
              ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=version.parse("1.11"    )






       @property
       def       lowerCAmelCase		(      self)						->  Mapping[str, Mapping[int, str]]:




          '''simple docstring'''
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ])






       @property
       def       lowerCAmelCase		(      self)						->  float:




          '''simple docstring'''
          return 1E-4

