




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__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{
    '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					,			__lowercase=50_265					,			__lowercase=768					,			__lowercase=12					,			__lowercase=12					,			__lowercase=3_072					,			__lowercase="gelu"					,			__lowercase=0.1					,			__lowercase=0.1					,			__lowercase=512					,			__lowercase=0					,			__lowercase=0.02					,			__lowercase=1E-7					,			__lowercase=False					,			__lowercase=-1					,			__lowercase=0					,			__lowercase=True					,			__lowercase=None					,			__lowercase=0					,			__lowercase="gelu"					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(**__lowercase   )

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

						# Backwards compatibility
						if type(__lowercase   ) == str:
							lowerCamelCase_				:						Optional[Any]				  =				[x.strip() for x in pos_att_type.lower().split('|'   )]

						lowerCamelCase_				:						List[str]				  =				pos_att_type
						lowerCamelCase_				:						int				  =				vocab_size
						lowerCamelCase_				:						Optional[Any]				  =				layer_norm_eps

						lowerCamelCase_				:						str				  =				kwargs.get('pooler_hidden_size'					,			__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				pooler_dropout
						lowerCamelCase_				:						Optional[int]				  =				pooler_hidden_act


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




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







						'''simple docstring'''




						return 12





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = False					,			__lowercase = None					,			__lowercase = 3					,			__lowercase = 40					,			__lowercase = 40					,			__lowercase = None					,			):







						'''simple docstring'''




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



import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A=None      ):
  UpperCAmelCase          =	None
  # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
  if str(weight.dtype      ) == "torch.bfloat16":
    # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
    UpperCAmelCase          =	weight.view(torch.intaa      )
    UpperCAmelCase          =	"bfloat16"
  UpperCAmelCase          =	weight.cpu().numpy()
  UpperCAmelCase          =	os.path.join(__A ,							F"{weight_name}.dat"      )
  if index is not None:
    if dtype is None:
      UpperCAmelCase          =	str(array.dtype      )
    UpperCAmelCase          =	{"dtype": dtype, "shape": list(array.shape      )}
  if array.ndim == 0:
    UpperCAmelCase          =	array[None]
  UpperCAmelCase          =	np.memmap(__A ,							dtype=array.dtype ,							mode="w+" ,							shape=array.shape      )
  UpperCAmelCase          =	array[:]
  file_array.flush()
  return index
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	tuple(weight_info["shape"]      )
  if shape == ():
    # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
    UpperCAmelCase          =	(1,)

  UpperCAmelCase          =	weight_info["dtype"]
  if dtype == "bfloat16":
    # NumPy does not support bfloat16 so this was saved as a int16
    UpperCAmelCase          =	"int16"

  UpperCAmelCase          =	np.memmap(__A ,							dtype=__A ,							shape=__A ,							mode="r"      )

  if len(weight_info["shape"]      ) == 0:
    UpperCAmelCase          =	weight[0]
  UpperCAmelCase          =	torch.tensor(__A      )
  if weight_info["dtype"] == "bfloat16":
    UpperCAmelCase          =	weight.view(torch.bfloataa      )

  return weight
def   _lowerCAmelCase(						__A ,							__A      ):
  if index is None or len(__A      ) == 0:
    # Nothing to save
    return

  UpperCAmelCase          =	os.path.join(__A ,							"index.json"      )
  if os.path.isfile(__A      ):
    with open(__A ,							"r" ,							encoding="utf-8"      ) as f:
      UpperCAmelCase          =	json.load(__A      )
  else:
    UpperCAmelCase          =	{}
  current_index.update(__A      )

  with open(__A ,							"w" ,							encoding="utf-8"      ) as f:
    json.dump(__A ,							__A ,							indent=2      )
def   _lowerCAmelCase(						__A ,							__A      ):
  os.makedirs(__A ,							exist_ok=__A      )
  UpperCAmelCase          =	{}
  for name, parameter in state_dict.items():
    UpperCAmelCase          =	offload_weight(__A ,							__A ,							__A ,							index=__A      )

  # Update index
  save_offload_index(__A ,							__A      )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Tuple      ,    lowerCAmelCase__		:			Mapping      ,    lowerCAmelCase__		:			str     )     ->   Tuple:
   UpperCAmelCase          =	dataset
   UpperCAmelCase          =	prefix


 def __getitem__(    self		:			Dict      ,    lowerCAmelCase__		:			Any     )     ->   Optional[int]:
   return self.dataset[f"{self.prefix}{key}"]


 def __iter__(    self		:			Optional[int]     )     ->   Any:
   return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )


 def __len__(    self		:			Union[str, Any]     )     ->   int:
   return len(self.dataset     )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Dict[str, torch.Tensor] = None      ,    lowerCAmelCase__		:			Optional[Union[str, os.PathLike]] = None      ,    lowerCAmelCase__		:			Mapping = None      ,    lowerCAmelCase__		:			Dict=None      ,    )     ->   Dict:
   if state_dict is None and save_folder is None:
     raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."     )

   UpperCAmelCase          =	{} if state_dict is None else state_dict
   UpperCAmelCase          =	save_folder
   if index is None and save_folder is not None:
     with open(os.path.join(lowerCAmelCase__      ,    "index.json"     )     ) as f:
       UpperCAmelCase          =	json.load(lowerCAmelCase__     )
   UpperCAmelCase          =	{} if index is None else index
   UpperCAmelCase          =	list(self.state_dict.keys()     )
   self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
   UpperCAmelCase          =	device


 def __getitem__(    self		:			str      ,    lowerCAmelCase__		:			str     )     ->   Dict:
   # State dict gets priority
   if key in self.state_dict:
     return self.state_dict[key]
   UpperCAmelCase          =	self.index[key]
   if weight_info.get("safetensors_file"     ) is not None:
     if not is_safetensors_available():
       raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."     )

     from safetensors import safe_open

     UpperCAmelCase          =	"cpu" if self.device is None else self.device
     with safe_open(weight_info["safetensors_file"]      ,    framework="pt"      ,    device=lowerCAmelCase__     ) as f:
       UpperCAmelCase          =	f.get_tensor(weight_info.get("weight_name"      ,    lowerCAmelCase__     )     )

     if "dtype" in weight_info:
       return tensor.to(getattr(lowerCAmelCase__      ,    weight_info["dtype"]     )     )
     else:
       return tensor

   UpperCAmelCase          =	os.path.join(self.save_folder      ,    f"{key}.dat"     )
   return load_offloaded_weight(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __iter__(    self		:			str     )     ->   Dict:
   return iter(self.all_keys     )


 def __len__(    self		:			Optional[Any]     )     ->   List[Any]:
   return len(self.all_keys     )

def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	{}
  for module_name in submodule_names:
    # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
    # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
    result.update(
        {
            key: param
            for key, param in state_dict.items()
            if key == module_name or key.startswith(module_name + "."      )
        }      )
  return result



