import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





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

			return orig_key





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: str):
			for key in orig_state_dict.copy().keys():
						lowerCamelCase						:Any						  =     orig_state_dict.pop(a_)

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase						:str						  =     val

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

			return orig_state_dict





def  _lowerCamelCase					(   a_				: int						,			a_				: Union[str, Any]						,			a_				: str):
			lowerCamelCase						:Optional[Any]						  =     torch.load(a_						,			map_location='''cpu''')['''model_state_dict''']
			lowerCamelCase						:Dict						  =     YosoConfig.from_json_file(a_)
			lowerCamelCase						:List[str]						  =     YosoForMaskedLM(a_)

			lowerCamelCase						:Optional[int]						  =     convert_checkpoint_helper(config.max_position_embeddings						,			a_)

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

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


if __name__ == "__main__":
			A__     :   Tuple           =							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."""
			)
			A__     :   Dict           =							parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




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__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

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


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



