



import argparse

import torch

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

       return orig_key
def        lowercase_							(A : Any		,					A : str      ):
       for key in orig_state_dict.copy().keys():
              snake_case__							:	str   	=	orig_state_dict.pop(A      )

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     snake_case__							:	Dict   	=	val

       snake_case__							:	Union[str, Any]   	=	orig_state_dict['cls.predictions.decoder.bias']
       snake_case__							:	str   	=	torch.arange(A      ).expand((1, -1)      ) + 2

       return orig_state_dict
def        lowercase_							(A : str		,					A : List[str]		,					A : Union[str, Any]      ):
       snake_case__							:	Any   	=	torch.load(A		,					map_location='cpu'      )['model_state_dict']
       snake_case__							:	str   	=	YosoConfig.from_json_file(A      )
       snake_case__							:	Any   	=	YosoForMaskedLM(A      )

       snake_case__							:	Optional[Any]   	=	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_   :Union[str, Any]        =      parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






