


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


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''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 	__lowerCamelCase			(_a						):
       _lowercase											=     """levit"""







       def __init__(			self: List[str],A_: Dict=224,A_: Dict=3,A_: Any=3,A_: Any=2,A_: Any=1,A_: Dict=16,A_: str=[128, 256, 384],A_: Any=[4, 8, 12],A_: List[str]=[4, 4, 4],A_: List[Any]=[16, 16, 16],A_: Union[str, Any]=0,A_: Optional[Any]=[2, 2, 2],A_: Union[str, Any]=[2, 2, 2],A_: str=0.0_2,**A_: Any,):






            '''simple docstring'''




            super().__init__(**A_					)
            __UpperCamelCase =			image_size
            __UpperCamelCase =			num_channels
            __UpperCamelCase =			kernel_size
            __UpperCamelCase =			stride
            __UpperCamelCase =			padding
            __UpperCamelCase =			hidden_sizes
            __UpperCamelCase =			num_attention_heads
            __UpperCamelCase =			depths
            __UpperCamelCase =			key_dim
            __UpperCamelCase =			drop_path_rate
            __UpperCamelCase =			patch_size
            __UpperCamelCase =			attention_ratio
            __UpperCamelCase =			mlp_ratio
            __UpperCamelCase =			initializer_range
            __UpperCamelCase =			[
                ['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 	__lowerCamelCase			(_a						):
       _lowercase											=     version.parse("""1.11"""						)







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return 1E-4










'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




