




"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


A				=							logging.get_logger(__name__)

A				=							{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class   a__					(						_UpperCamelCase ):
			lowercase_		=						'bert'


			def __init__(   self   :     Dict   ,	UpperCamelCase_   :     Dict=30522   ,	UpperCamelCase_   :     str=768   ,	UpperCamelCase_   :     Tuple=12   ,	UpperCamelCase_   :     Any=12   ,	UpperCamelCase_   :     Dict=3072   ,	UpperCamelCase_   :     Union[str, Any]="gelu"   ,	UpperCamelCase_   :     Any=0.1   ,	UpperCamelCase_   :     Optional[Any]=0.1   ,	UpperCamelCase_   :     Tuple=512   ,	UpperCamelCase_   :     Any=2   ,	UpperCamelCase_   :     Optional[int]=0.02   ,	UpperCamelCase_   :     Any=1e-12   ,	UpperCamelCase_   :     Tuple=0   ,	UpperCamelCase_   :     List[Any]="absolute"   ,	UpperCamelCase_   :     str=True   ,	UpperCamelCase_   :     List[str]=None   ,	**UpperCamelCase_   :     Tuple   ,	):


						"""simple docstring"""




						super().__init__(pad_token_id=UpperCamelCase_   ,	**UpperCamelCase_)

						__UpperCAmelCase						:			Dict												=       vocab_size
						__UpperCAmelCase						:			str												=       hidden_size
						__UpperCAmelCase						:			Dict												=       num_hidden_layers
						__UpperCAmelCase						:			Optional[int]												=       num_attention_heads
						__UpperCAmelCase						:			str												=       hidden_act
						__UpperCAmelCase						:			str												=       intermediate_size
						__UpperCAmelCase						:			Any												=       hidden_dropout_prob
						__UpperCAmelCase						:			Tuple												=       attention_probs_dropout_prob
						__UpperCAmelCase						:			Optional[Any]												=       max_position_embeddings
						__UpperCAmelCase						:			Any												=       type_vocab_size
						__UpperCAmelCase						:			int												=       initializer_range
						__UpperCAmelCase						:			Optional[int]												=       layer_norm_eps
						__UpperCAmelCase						:			Dict												=       position_embedding_type
						__UpperCAmelCase						:			int												=       use_cache
						__UpperCAmelCase						:			int												=       classifier_dropout






class   a__					(						_UpperCamelCase ):


			@property
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						if self.task == "multiple-choice":
									__UpperCAmelCase						:			Tuple												=       {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									__UpperCAmelCase						:			Any												=       {0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ])








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 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	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,
		)






