



'''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


snake_case_ :	List[Any]         =  logging.get_logger(__name__)

snake_case_ :	Optional[int]         =  {
    '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__	(							lowercase   ):
	lowercase__  				=			"""deberta-v2"""





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				Optional[Any]=128100 ,lowerCamelCase__					:				Optional[int]=1536 ,lowerCamelCase__					:				Any=24 ,lowerCamelCase__					:				Dict=24 ,lowerCamelCase__					:				Optional[int]=6144 ,lowerCamelCase__					:				str="gelu" ,lowerCamelCase__					:				List[str]=0.1 ,lowerCamelCase__					:				Optional[Any]=0.1 ,lowerCamelCase__					:				Optional[int]=512 ,lowerCamelCase__					:				Dict=0 ,lowerCamelCase__					:				Dict=0.0_2 ,lowerCamelCase__					:				List[Any]=1E-7 ,lowerCamelCase__					:				List[Any]=False ,lowerCamelCase__					:				List[str]=-1 ,lowerCamelCase__					:				Any=0 ,lowerCamelCase__					:				Dict=True ,lowerCamelCase__					:				Tuple=None ,lowerCamelCase__					:				Union[str, Any]=0 ,lowerCamelCase__					:				Any="gelu" ,**lowerCamelCase__					:				str ,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__ )

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

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

							_UpperCamelCase					:		Any 				=   pos_att_type
							_UpperCamelCase					:		int 				=   vocab_size
							_UpperCamelCase					:		List[str] 				=   layer_norm_eps

							_UpperCamelCase					:		Tuple 				=   kwargs.get('pooler_hidden_size' ,lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   pooler_dropout
							_UpperCamelCase					:		str 				=   pooler_hidden_act






class 							lowercase__	(							lowercase   ):





	@property
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







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







							'''simple docstring'''
							return 12





	def 	UpperCamelCase_  ( self					:				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					:		List[Any] 				=   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









from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer







class 				__lowerCAmelCase       :







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        raise NotImplementedError()







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					str										=tokenizer
        a__	:					List[str]										=skip_prompt
        a__	:					List[Any]										=decode_kwargs

        # variables used in the streaming process
        a__	:					Dict										=[]
        a__	:					int										=0
        a__	:					str										=True







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        if len(value.shape							) > 1 and value.shape[0] > 1:
          raise ValueError("TextStreamer only supports batch size 1"							)
        elif len(value.shape							) > 1:
          a__	:					Any										=value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
          a__	:					Dict										=False
          return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()							)
        a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)

        # After the symbol for a new line, we flush the cache.
        if text.endswith("\n"							):
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        # If the last token is a CJK character, we print the characters.
        elif len(lowerCAmelCase__							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
          a__	:					List[str]										=text[self.print_len :]
          self.print_len += len(lowerCAmelCase__							)
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
          a__	:					str										=text[self.print_len : text.rfind(" "							) + 1]
          self.print_len += len(lowerCAmelCase__							)

        self.on_finalized_text(lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        if len(self.token_cache							) > 0:
          a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        else:
          a__	:					Union[str, Any]										=""

        a__	:					Any										=True
        self.on_finalized_text(lowerCAmelCase__		,      stream_end=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						Optional[Any]:



        '''simple docstring'''




        print(lowerCAmelCase__		,      flush=lowerCAmelCase__		,      end="" if not stream_end else None							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        if (
            (cp >= 0X4E_00 and cp <= 0X9F_FF)
            or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
            or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
            or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
            or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
            or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
            or (cp >= 0XF9_00 and cp <= 0XFA_FF)
            or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
        ):  #
          return True

        return False







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					str										=Queue()
        a__	:					Optional[Any]										=None
        a__	:					Any										=timeout







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						List[str]:



        '''simple docstring'''




        self.text_queue.put(lowerCAmelCase__		,      timeout=self.timeout							)
        if stream_end:
          self.text_queue.put(self.stop_signal		,      timeout=self.timeout							)







      def __iter__(    self							)   ->						Dict:



        '''simple docstring'''




        return self







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					int										=self.text_queue.get(timeout=self.timeout							)
        if value == self.stop_signal:
          raise StopIteration()
        else:
          return value






