
'''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


a_											=						logging.get_logger(__name__)

a_											=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""data2vec-vision"""





 def __init__(    self ,       lowercase_=7_68 ,       lowercase_=12 ,       lowercase_=12 ,       lowercase_=30_72 ,       lowercase_="gelu" ,       lowercase_=0.0 ,       lowercase_=0.0 ,       lowercase_=0.02 ,       lowercase_=1E-12 ,       lowercase_=2_24 ,       lowercase_=16 ,       lowercase_=3 ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=True ,       lowercase_=[3, 5, 7, 11] ,       lowercase_=[1, 2, 3, 6] ,       lowercase_=True ,       lowercase_=0.4 ,       lowercase_=2_56 ,       lowercase_=1 ,       lowercase_=False ,       lowercase_=2_55 ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)

     snake_case_ :    int     			=						hidden_size
     snake_case_ :    Dict     			=						num_hidden_layers
     snake_case_ :    List[Any]     			=						num_attention_heads
     snake_case_ :    int     			=						intermediate_size
     snake_case_ :    str     			=						hidden_act
     snake_case_ :    Tuple     			=						hidden_dropout_prob
     snake_case_ :    Any     			=						attention_probs_dropout_prob
     snake_case_ :    List[str]     			=						initializer_range
     snake_case_ :    Any     			=						layer_norm_eps

     snake_case_ :    Optional[int]     			=						image_size
     snake_case_ :    str     			=						patch_size
     snake_case_ :    Dict     			=						num_channels
     snake_case_ :    Any     			=						use_mask_token
     snake_case_ :    Dict     			=						use_absolute_position_embeddings
     snake_case_ :    Union[str, Any]     			=						use_relative_position_bias
     snake_case_ :    Any     			=						use_shared_relative_position_bias
     snake_case_ :    Tuple     			=						layer_scale_init_value
     snake_case_ :    Optional[Any]     			=						drop_path_rate
     snake_case_ :    str     			=						use_mean_pooling
     # decode head attributes (semantic segmentation)
     snake_case_ :    Dict     			=						out_indices
     snake_case_ :    Dict     			=						pool_scales
     # auxiliary head attributes (semantic segmentation)
     snake_case_ :    Any     			=						use_auxiliary_head
     snake_case_ :    Union[str, Any]     			=						auxiliary_loss_weight
     snake_case_ :    Any     			=						auxiliary_channels
     snake_case_ :    int     			=						auxiliary_num_convs
     snake_case_ :    List[str]     			=						auxiliary_concat_input
     snake_case_ :    Any     			=						semantic_loss_ignore_index






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						version.parse("""1.11"""					)





 @property
 def        snake_case__		(    self):
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ])





 @property
 def        snake_case__		(    self):
     return 1E-4






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

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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


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




