"""simple docstring"""







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE	:		int   =   logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE	:		str   =   {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

_SCREAMING_SNAKE_CASE	:		Dict   =   {
    '''microsoft/deberta-base''': 5_1_2,
    '''microsoft/deberta-large''': 5_1_2,
    '''microsoft/deberta-xlarge''': 5_1_2,
    '''microsoft/deberta-base-mnli''': 5_1_2,
    '''microsoft/deberta-large-mnli''': 5_1_2,
    '''microsoft/deberta-xlarge-mnli''': 5_1_2,
}

_SCREAMING_SNAKE_CASE	:		str   =   {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    VOCAB_FILES_NAMES
   __UpperCAmelCase	:							Optional[int]    =    PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase	:							Optional[int]    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase	:							Any    =    ["input_ids", "attention_mask", "token_type_ids"]
   __UpperCAmelCase	:							List[Any]    =    DebertaTokenizer







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE="replace" ,				_SCREAMING_SNAKE_CASE="[CLS]" ,				_SCREAMING_SNAKE_CASE="[SEP]" ,				_SCREAMING_SNAKE_CASE="[SEP]" ,				_SCREAMING_SNAKE_CASE="[CLS]" ,				_SCREAMING_SNAKE_CASE="[UNK]" ,				_SCREAMING_SNAKE_CASE="[PAD]" ,				_SCREAMING_SNAKE_CASE="[MASK]" ,				_SCREAMING_SNAKE_CASE=False ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				tokenizer_file=_SCREAMING_SNAKE_CASE ,				errors=_SCREAMING_SNAKE_CASE ,				bos_token=_SCREAMING_SNAKE_CASE ,				eos_token=_SCREAMING_SNAKE_CASE ,				unk_token=_SCREAMING_SNAKE_CASE ,				sep_token=_SCREAMING_SNAKE_CASE ,				cls_token=_SCREAMING_SNAKE_CASE ,				pad_token=_SCREAMING_SNAKE_CASE ,				mask_token=_SCREAMING_SNAKE_CASE ,				add_prefix_space=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					kwargs.pop('add_bos_token' ,				_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					add_prefix_space







   @property
   def    _A				(	self	):


      '''simple docstring'''

      if self._mask_token is None:
         if self.verbose:
            logger.error('Using mask_token, but it is not set yet.'	)
         return None
      return str(self._mask_token	)







   @mask_token.setter
   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					AddedToken(_SCREAMING_SNAKE_CASE ,				lstrip=_SCREAMING_SNAKE_CASE ,				rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	) else value
      __lowerCamelCase    		=					value







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''simple docstring'''

      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __lowerCamelCase    		=					[self.cls_token_id]
      __lowerCamelCase    		=					[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''simple docstring'''

      __lowerCamelCase    		=					[self.sep_token_id]
      __lowerCamelCase    		=					[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]







   def    _A				(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					kwargs.get('is_split_into_words' ,				_SCREAMING_SNAKE_CASE	)
      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					kwargs.get('is_split_into_words' ,				_SCREAMING_SNAKE_CASE	)

      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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

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









"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	**lowercase_    ,	)


