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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   		=			{
    """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/config.json""",
    """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/config.json""",
    """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json""",
    """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json""",
    """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json""",
    """microsoft/deberta-xlarge-mnli""": """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""deberta"""







   def __init__(    self	:	Tuple      ,      lowerCamelCase_	:	Dict=5_0265      ,      lowerCamelCase_	:	Optional[int]=768      ,      lowerCamelCase_	:	Optional[int]=12      ,      lowerCamelCase_	:	str=12      ,      lowerCamelCase_	:	Optional[Any]=3072      ,      lowerCamelCase_	:	List[Any]="gelu"      ,      lowerCamelCase_	:	Optional[Any]=0.1      ,      lowerCamelCase_	:	Any=0.1      ,      lowerCamelCase_	:	Dict=512      ,      lowerCamelCase_	:	Any=0      ,      lowerCamelCase_	:	int=0.0_2      ,      lowerCamelCase_	:	Dict=1E-7      ,      lowerCamelCase_	:	List[str]=False      ,      lowerCamelCase_	:	List[Any]=-1      ,      lowerCamelCase_	:	Any=0      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	Optional[Any]=None      ,      lowerCamelCase_	:	Optional[Any]=0      ,      lowerCamelCase_	:	Tuple="gelu"      ,      **lowerCamelCase_	:	Union[str, Any]      ,      ):





    """simple docstring"""


    super().__init__(**lowerCamelCase_							)

    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			type_vocab_size
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			relative_attention
    UpperCamelCase      =			max_relative_positions
    UpperCamelCase      =			pad_token_id
    UpperCamelCase      =			position_biased_input

    # Backwards compatibility
    if type(lowerCamelCase_							) == str:
     UpperCamelCase      =			[x.strip() for x in pos_att_type.lower().split("""|"""							)]

    UpperCamelCase      =			pos_att_type
    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			layer_norm_eps

    UpperCamelCase      =			kwargs.get("""pooler_hidden_size"""      ,      lowerCamelCase_							)
    UpperCamelCase      =			pooler_dropout
    UpperCamelCase      =			pooler_hidden_act





class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):







   @property
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    if self.task == "multiple-choice":
     UpperCamelCase      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
     UpperCamelCase      =			{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 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    return 12







   def 	lowerCamelCase_  (    self	:	List[str]      ,      lowerCamelCase_	:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,      lowerCamelCase_	:	int = -1      ,      lowerCamelCase_	:	int = -1      ,      lowerCamelCase_	:	int = -1      ,      lowerCamelCase_	:	bool = False      ,      lowerCamelCase_	:	Optional["TensorType"] = None      ,      lowerCamelCase_	:	int = 3      ,      lowerCamelCase_	:	int = 40      ,      lowerCamelCase_	:	int = 40      ,      lowerCamelCase_	:	"PreTrainedTokenizerBase" = None      ,      ):





    """simple docstring"""


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

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""					)
 for name, module in model.named_children():
  if isinstance(UpperCamelCase_       ,				nn.Linear					) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape					):
    return
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			te.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				nn.LayerNorm					) and to_transformer_engine and _convert_ln:
   UpperCamelCase      =			te.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.Linear					) and not to_transformer_engine and _convert_linear:
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			nn.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.LayerNorm					) and not to_transformer_engine and _convert_ln:
   UpperCamelCase      =			nn.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  else:
   convert_model(
       UpperCamelCase_       ,				to_transformer_engine=UpperCamelCase_       ,				_convert_linear=_convert_linear       ,				_convert_ln=_convert_ln       ,				)





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""					)
 for m in model.modules():
  if isinstance(UpperCamelCase_       ,				(te.LayerNorm, te.Linear)					):
   return True
 return False
