




'''simple docstring'''





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


lowercase__			=	logging.get_logger(__name__)

lowercase__			=	{
    # 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 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''graphormer'''
      __lowercase 		=  ['''past_key_values''']






      def __init__(self ,  a__ = 1 ,  a__ = 5_1_2 * 9 ,  a__ = 5_1_2 * 3 ,  a__ = 5_1_2 ,  a__ = 5_1_2 ,  a__ = 5_1_2 ,  a__ = 1_2_8 ,  a__ = 5 ,  a__ = 1_0_2_4 ,  a__ = "multi_hop" ,  a__ = 5_1_2 ,  a__ = False ,  a__ = 1_2 ,  a__ = 7_6_8 ,  a__ = 7_6_8 ,  a__ = 3_2 ,  a__ = 0.1 ,  a__ = 0.1 ,  a__ = 0.0 ,  a__ = False ,  a__ = False ,  a__ = False ,  a__ = "gelu" ,  a__ = None ,  a__ = False ,  a__ = 0 ,  a__ = False ,  a__ = 0.0 ,  a__ = 8 ,  a__ = None ,  a__ = None ,  a__ = True ,  a__ = True ,  a__=0 ,  a__=1 ,  a__=2 ,  **a__ ,  )       ->   str:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= num_classes
         SCREAMING_SNAKE_CASE__			:				List[str]       						= num_atoms
         SCREAMING_SNAKE_CASE__			:				Dict       						= num_in_degree
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_out_degree
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= num_edges
         SCREAMING_SNAKE_CASE__			:				List[str]       						= num_spatial
         SCREAMING_SNAKE_CASE__			:				List[str]       						= num_edge_dis
         SCREAMING_SNAKE_CASE__			:				Dict       						= edge_type
         SCREAMING_SNAKE_CASE__			:				Tuple       						= multi_hop_max_dist
         SCREAMING_SNAKE_CASE__			:				Dict       						= spatial_pos_max
         SCREAMING_SNAKE_CASE__			:				Dict       						= max_nodes
         SCREAMING_SNAKE_CASE__			:				int       						= num_hidden_layers
         SCREAMING_SNAKE_CASE__			:				Any       						= embedding_dim
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= embedding_dim
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= ffn_embedding_dim
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= num_attention_heads
         SCREAMING_SNAKE_CASE__			:				str       						= dropout
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= attention_dropout
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= layerdrop
         SCREAMING_SNAKE_CASE__			:				int       						= encoder_normalize_before
         SCREAMING_SNAKE_CASE__			:				Any       						= pre_layernorm
         SCREAMING_SNAKE_CASE__			:				int       						= apply_graphormer_init
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= activation_fn
         SCREAMING_SNAKE_CASE__			:				int       						= embed_scale
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= freeze_embeddings
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_trans_layers_to_freeze
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= share_input_output_embed
         SCREAMING_SNAKE_CASE__			:				Any       						= traceable
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= q_noise
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= qn_block_size

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

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







'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowercase__			=	logging.get_logger(__name__)

lowercase__			=	{'''vocab_file''': '''vocab.txt'''}

lowercase__			=	{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

lowercase__			=	{
    '''tau/splinter-base''': 5_1_2,
    '''tau/splinter-base-qass''': 5_1_2,
    '''tau/splinter-large''': 5_1_2,
    '''tau/splinter-large-qass''': 5_1_2,
}

lowercase__			=	{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  VOCAB_FILES_NAMES
      __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
      __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
      __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase 		=  SplinterTokenizer






      def __init__(self ,  a__=None ,  a__=None ,  a__=True ,  a__="[UNK]" ,  a__="[SEP]" ,  a__="[PAD]" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__="[QUESTION]" ,  a__=True ,  a__=None ,  **a__ ,  )       ->   Any:
         super().__init__(
             a__ ,  tokenizer_file=a__ ,  do_lower_case=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  tokenize_chinese_chars=a__ ,  strip_accents=a__ ,  additional_special_tokens=(question_token,) ,  **a__ ,  )

         SCREAMING_SNAKE_CASE__			:				Tuple       						= json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
         if (
             pre_tok_state.get('''lowercase''' ,  a__					) != do_lower_case
             or pre_tok_state.get('''strip_accents''' ,  a__					) != strip_accents
         ):
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(a__ ,  pre_tok_state.pop('''type'''					)					)
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= strip_accents
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pre_tok_class(**a__					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case






      @property
      def    A__(self					)       ->   List[str]:
         return self.convert_tokens_to_ids(self.question_token					)






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         SCREAMING_SNAKE_CASE__			:				Dict       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Any       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep					) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]






      def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
         SCREAMING_SNAKE_CASE__			:				int       						= self._tokenizer.model.save(a__ ,  name=a__					)
         return tuple(a__					)

