code: 
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A  :					Optional[Any]												=	logging.get_logger(__name__)
  
  __A  :					int												=	{
      # 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				(     _SCREAMING_SNAKE_CASE		):
  		SCREAMING_SNAKE_CASE              =					"graphormer"
  		SCREAMING_SNAKE_CASE              =					["past_key_values"]
  
  
  		def __init__(self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Any = 1  ,  __SCREAMING_SNAKE_CASE				:	int = 5_1_2 * 9  ,  __SCREAMING_SNAKE_CASE				:	List[Any] = 5_1_2 * 3  ,  __SCREAMING_SNAKE_CASE				:	str = 5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Dict = 5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Any = 5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any] = 1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any] = 5  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any] = 1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Dict = "multi_hop"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = 5_1_2  ,  __SCREAMING_SNAKE_CASE				:	int = False  ,  __SCREAMING_SNAKE_CASE				:	str = 1_2  ,  __SCREAMING_SNAKE_CASE				:	str = 7_6_8  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = 7_6_8  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any] = 3_2  ,  __SCREAMING_SNAKE_CASE				:	List[str] = 0.1  ,  __SCREAMING_SNAKE_CASE				:	str = 0.1  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = 0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any] = False  ,  __SCREAMING_SNAKE_CASE				:	Any = False  ,  __SCREAMING_SNAKE_CASE				:	str = False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any] = "gelu"  ,  __SCREAMING_SNAKE_CASE				:	str = None  ,  __SCREAMING_SNAKE_CASE				:	Tuple = False  ,  __SCREAMING_SNAKE_CASE				:	List[str] = 0  ,  __SCREAMING_SNAKE_CASE				:	int = False  ,  __SCREAMING_SNAKE_CASE				:	Dict = 0.0  ,  __SCREAMING_SNAKE_CASE				:	List[str] = 8  ,  __SCREAMING_SNAKE_CASE				:	List[str] = None  ,  __SCREAMING_SNAKE_CASE				:	Dict = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = True  ,  __SCREAMING_SNAKE_CASE				:	int = True  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=0  ,  __SCREAMING_SNAKE_CASE				:	int=1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=2  ,  **__SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  ):
  							A    =      num_classes
  							A    =      num_atoms
  							A    =      num_in_degree
  							A    =      num_out_degree
  							A    =      num_edges
  							A    =      num_spatial
  							A    =      num_edge_dis
  							A    =      edge_type
  							A    =      multi_hop_max_dist
  							A    =      spatial_pos_max
  							A    =      max_nodes
  							A    =      num_hidden_layers
  							A    =      embedding_dim
  							A    =      embedding_dim
  							A    =      ffn_embedding_dim
  							A    =      num_attention_heads
  							A    =      dropout
  							A    =      attention_dropout
  							A    =      layerdrop
  							A    =      encoder_normalize_before
  							A    =      pre_layernorm
  							A    =      apply_graphormer_init
  							A    =      activation_fn
  							A    =      embed_scale
  							A    =      freeze_embeddings
  							A    =      num_trans_layers_to_freeze
  							A    =      share_input_output_embed
  							A    =      traceable
  							A    =      q_noise
  							A    =      qn_block_size
  
  							# These parameters are here for future extensions
  							# atm, the model only supports self attention
  							A    =      kdim
  							A    =      vdim
  							A    =      self_attention
  							A    =      bias
  
  							super().__init__(
  							    pad_token_id=__A  ,  bos_token_id=__A  ,  eos_token_id=__A  ,  **__A  ,  )
  
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  __A  :					List[Any]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None):
  							A    =      language
  							A    =      train_language
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self.language if self.train_language is None else self.train_language
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      F"""train-{i}"""
  												A    =      line[0]
  												A    =      line[1]
  												A    =      "contradiction" if line[2] == "contradictory" else line[2]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  "XNLI-1.0/xnli.test.tsv"))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      line[0]
  												if language != self.language:
  																	continue
  												A    =      F"""test-{i}"""
  												A    =      line[6]
  												A    =      line[7]
  												A    =      line[1]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return ["contradiction", "entailment", "neutral"]
  
  __A  :					Optional[int]												=	{
      'xnli': XnliProcessor,
  }
  
  __A  :					Union[str, Any]												=	{
      'xnli': 'classification',
  }
  
  __A  :					Tuple												=	{
      'xnli': 3,
  }
  
  
  
  
  
  