





import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


UpperCAmelCase							   =      set()
UpperCAmelCase							   =      get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_ = None       )	->	Optional[int]:

     def decorator(lowerCAmelCase_       ):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase_       ):
               a  :    Tuple				=						deprecated_class_or_function.__init__
               a  :    List[Any]				=						deprecated_class_or_function.__name__
          else:
               a  :    Optional[Any]				=						deprecated_class_or_function
               a  :    Optional[int]				=						deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               a  :    int				=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''       )[-2]

          a  :    int				=						(
              f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
              if help_message
              else ''''''
          )

          @wraps(lowerCAmelCase_       )
          def wrapper(*lowerCAmelCase_						,      **lowerCAmelCase_       ):
               a  :    Optional[Any]				=						hash(lowerCAmelCase_       )
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase_						,      category=lowerCAmelCase_						,      stacklevel=2       )
                    _emitted_deprecation_warnings.add(lowerCAmelCase_       )
               return deprecated_function(*lowerCAmelCase_						,      **lowerCAmelCase_       )

          a  :    str				=						'''deprecated'''

          if inspect.isclass(lowerCAmelCase_       ):
               a  :    str				=						wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator






class 							_SCREAMING_SNAKE_CASE(   enum.EnumMeta	):
   def __getattribute__(    cls							,     lowerCAmelCase_    )	->Tuple:
        a  :    str				=						super().__getattribute__(lowerCAmelCase_    )
        if isinstance(lowerCAmelCase_							,     enum.Enum    ) and obj._on_access:
             obj._on_access()
        return obj
   def __getitem__(    cls							,     lowerCAmelCase_    )	->List[Any]:
        a  :    Tuple				=						super().__getitem__(lowerCAmelCase_    )
        if member._on_access:
             member._on_access()
        return member


   def __call__(    cls							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     *, lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_=1    )	->Tuple:
        a  :    Tuple				=						super().__call__(lowerCAmelCase_							,     lowerCAmelCase_							,     module=lowerCAmelCase_							,     qualname=lowerCAmelCase_							,     type=lowerCAmelCase_							,     start=lowerCAmelCase_    )
        if isinstance(lowerCAmelCase_							,     enum.Enum    ) and obj._on_access:
             obj._on_access()
        return obj






class 							_SCREAMING_SNAKE_CASE(   enum.Enum       ,      metaclass=snake_case__	):
   def __new__(    cls							,     lowerCAmelCase_    )	->Dict:
        a  :    Union[str, Any]				=						object.__new__(cls    )
        a  :    Union[str, Any]				=						value
        a  :    int				=						member.deprecate
        return member
   @property
   def _lowercase(    self    )	->List[str]:
        return ""


   def _lowercase(    self    )	->Any:
        a  :    Any				=						F""" {self.help_message}""" if self.help_message else ''''''
        warnings.warn(
            F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
            + help_message							,     lowerCAmelCase_							,     stacklevel=3							,     )
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # 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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

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

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