
"""simple docstring"""




import warnings
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__      :	str          =    logging.get_logger(__name__)

A__      :	str          =    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'segformer'



     def __init__(      self ,			__UpperCamelCase=3 ,			__UpperCamelCase=4 ,			__UpperCamelCase=[2, 2, 2, 2] ,			__UpperCamelCase=[8, 4, 2, 1] ,			__UpperCamelCase=[32, 64, 1_60, 2_56] ,			__UpperCamelCase=[7, 3, 3, 3] ,			__UpperCamelCase=[4, 2, 2, 2] ,			__UpperCamelCase=[1, 2, 5, 8] ,			__UpperCamelCase=[4, 4, 4, 4] ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=1E-6 ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=2_55 ,			**__UpperCamelCase ,			)->					Optional[int]:
          super().__init__(**__UpperCamelCase  )

          if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
               warnings.warn(
                   "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                   " removed, as the behaviour will default to that of reshape_last_stage = True." ,			__UpperCamelCase ,			)

          UpperCAmelCase__							: Optional[int] 							=						num_channels
          UpperCAmelCase__							: Union[str, Any] 							=						num_encoder_blocks
          UpperCAmelCase__							: Tuple 							=						depths
          UpperCAmelCase__							: List[str] 							=						sr_ratios
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_sizes
          UpperCAmelCase__							: Any 							=						patch_sizes
          UpperCAmelCase__							: List[Any] 							=						strides
          UpperCAmelCase__							: Optional[int] 							=						mlp_ratios
          UpperCAmelCase__							: Tuple 							=						num_attention_heads
          UpperCAmelCase__							: Any 							=						hidden_act
          UpperCAmelCase__							: Optional[int] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Union[str, Any] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Any 							=						classifier_dropout_prob
          UpperCAmelCase__							: Dict 							=						initializer_range
          UpperCAmelCase__							: Tuple 							=						drop_path_rate
          UpperCAmelCase__							: Optional[int] 							=						layer_norm_eps
          UpperCAmelCase__							: List[Any] 							=						decoder_hidden_size
          UpperCAmelCase__							: int 							=						kwargs.get("reshape_last_stage" ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						semantic_loss_ignore_index







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



     @property
     def  lowerCAmelCase__       (      self  )->					Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]  )



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-4



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return 12





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


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



