from typing import Union

import fire
import torch
from tqdm import tqdm
def    A			(					_lowercase	,						_lowercase = "cpu"	,						_lowercase = None		):
      SCREAMING_SNAKE_CASE							:					str           =							torch.load(_lowercase	,						map_location=_lowercase		)
      for k, v in tqdm(state_dict.items()		):
            if not isinstance(_lowercase	,						torch.Tensor		):
                  raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''		)
            SCREAMING_SNAKE_CASE							:					Any           =							v.half()
      if save_path is None:  # overwrite src_path
            SCREAMING_SNAKE_CASE							:					str           =							src_path
      torch.save(_lowercase	,						_lowercase		)


if __name__ == "__main__":
     fire.Fire(convert)






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


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[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       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

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

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

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{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  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





