




'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_			=						{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCamelCase_			=						{
    'gpt-neox-20b': 20_48,
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						['''input_ids''', '''attention_mask''']



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Dict=None  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     List[Any]="<|endoftext|>"  ,				__lowerCamelCase :     List[Any]="<|endoftext|>"  ,				__lowerCamelCase :     List[Any]="<|endoftext|>"  ,				__lowerCamelCase :     Tuple=False  ,				**__lowerCamelCase :     Dict  ,				):

      """simple docstring"""
      super().__init__(
          __lowerCamelCase  ,				__lowerCamelCase  ,				tokenizer_file=__lowerCamelCase  ,				unk_token=__lowerCamelCase  ,				bos_token=__lowerCamelCase  ,				eos_token=__lowerCamelCase  ,				add_prefix_space=__lowerCamelCase  ,				**__lowerCamelCase  ,				)

      _SCREAMING_SNAKE_CASE	=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
      if pre_tok_state.get("add_prefix_space"  ,				__lowerCamelCase   ) != add_prefix_space:
         _SCREAMING_SNAKE_CASE	=				getattr(__lowerCamelCase  ,				pre_tok_state.pop("type"   )   )
         _SCREAMING_SNAKE_CASE	=				add_prefix_space
         _SCREAMING_SNAKE_CASE	=				pre_tok_class(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				add_prefix_space



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[str] = None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self._tokenizer.model.save(__lowerCamelCase  ,				name=__lowerCamelCase   )
      return tuple(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     "Conversation"   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__lowerCamelCase  ,				add_special_tokens=__lowerCamelCase   ) + [self.eos_token_id]   )

      if len(__lowerCamelCase   ) > self.model_max_length:
         _SCREAMING_SNAKE_CASE	=				input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''



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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    '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_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



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

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

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

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

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{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 :     List[str]   ):

      """simple docstring"""
      return 1_2



   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 = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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

