
"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case					  =						{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

snake_case					  =						{
    'microsoft/mpnet-base': 5_1_2,
}

snake_case					  =						{
    'microsoft/mpnet-base': {'do_lower_case': True},
}




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: str		    =							VOCAB_FILES_NAMES
				UpperCAmelCase_							: Optional[Any]		    =							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase_							: Tuple		    =							PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase_							: Union[str, Any]		    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase_							: str		    =							MPNetTokenizer
				UpperCAmelCase_							: Optional[Any]		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__=None						,      lowercase__=None						,      lowercase__=True						,      lowercase__="<s>"						,      lowercase__="</s>"						,      lowercase__="</s>"						,      lowercase__="<s>"						,      lowercase__="[UNK]"						,      lowercase__="<pad>"						,      lowercase__="<mask>"						,      lowercase__=True						,      lowercase__=None						,      **lowercase__						,      )       ->       int:
									"""simple docstring"""







									super().__init__(
									    lowercase__						,      tokenizer_file=lowercase__						,      do_lower_case=lowercase__						,      bos_token=lowercase__						,      eos_token=lowercase__						,      sep_token=lowercase__						,      cls_token=lowercase__						,      unk_token=lowercase__						,      pad_token=lowercase__						,      mask_token=lowercase__						,      tokenize_chinese_chars=lowercase__						,      strip_accents=lowercase__						,      **lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
									if (
									    pre_tok_state.get('lowercase'						,      lowercase__     ) != do_lower_case
									    or pre_tok_state.get('strip_accents'						,      lowercase__     ) != strip_accents
									):
														SCREAMING_SNAKE_CASE           =      getattr(lowercase__						,      pre_tok_state.pop('type'     )     )
														SCREAMING_SNAKE_CASE           =      do_lower_case
														SCREAMING_SNAKE_CASE           =      strip_accents
														SCREAMING_SNAKE_CASE           =      pre_tok_class(**lowercase__     )

									SCREAMING_SNAKE_CASE           =      do_lower_case


				@property
				def 				A   (							self     )       ->       str:
									"""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						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else value
									SCREAMING_SNAKE_CASE           =      value


				def 				A   (							self						,      lowercase__						,      lowercase__=None     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [self.sep_token_id]
									SCREAMING_SNAKE_CASE           =      [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 + sep + token_ids_a + sep     ) * [0]




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self._tokenizer.model.save(lowercase__						,      name=lowercase__     )
									return tuple(lowercase__     )


"""simple docstring"""




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


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # 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 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									SCREAMING_SNAKE_CASE           =      kdim
									SCREAMING_SNAKE_CASE           =      vdim
									SCREAMING_SNAKE_CASE           =      self_attention
									SCREAMING_SNAKE_CASE           =      bias

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