
"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union


def   __magic_name__     (				__snake_case    :	Union[str, Any]  )			->    Optional[int]:
       if isinstance(lowerCamelCase_					,			lowerCamelCase_  ):
              # Data already in bytes, returns as it as
              return hash_data
       elif isinstance(lowerCamelCase_					,			lowerCamelCase_  ):
              # We keep the data as it as for it ot be later encoded to UTF-8
              # However replace `\\` with `/` for Windows compatibility
              lowercase   :    Optional[Any]			=							hash_data.replace("\\"					,			"/"  )
       elif isinstance(lowerCamelCase_					,			lowerCamelCase_  ):
              lowercase   :    Tuple			=							str(lowerCamelCase_  )
       else:
              # If data is not of the required type, raise error
              raise InvalidKeyError(lowerCamelCase_  )

       return hash_data.encode("utf-8"  )

class 		a__						(				A__						):
   def __init__(      self     ,   _a    ):
          lowercase   :    Tuple			=							"\nFAILURE TO GENERATE DATASET: Invalid key type detected"
          lowercase   :    Tuple			=							f"""\nFound Key {hash_data} of type {type(_a    )}"""
          lowercase   :    int			=							"\nKeys should be either str, int or bytes type"
          super().__init__(f"""{self.prefix}{self.err_msg}{self.suffix}"""    )


class 		a__						(				A__						):
   def __init__(      self     ,   _a     ,   _a     ,   _a=""    ):
          lowercase   :    Optional[Any]			=							key
          lowercase   :    Union[str, Any]			=							duplicate_key_indices
          lowercase   :    Union[str, Any]			=							fix_msg
          lowercase   :    Optional[int]			=							"Found multiple examples generated with the same key"
          if len(_a    ) <= 20:
                 lowercase   :    int			=							f"""\nThe examples at index {", ".join(_a    )} have the key {key}"""
          else:
                 lowercase   :    List[Any]			=							f"""\nThe examples at index {", ".join(duplicate_key_indices[:20]    )}... ({len(_a    ) - 20} more) have the key {key}"""
          lowercase   :    Dict			=							"\n" + fix_msg if fix_msg else ""
          super().__init__(f"""{self.prefix}{self.err_msg}{self.suffix}"""    )


class 		a__						:
   def __init__(      self     ,   _a    ):
          lowercase   :    List[Any]			=							hashlib.mda(_as_bytes(_a    )    )

   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Dict			=							self._split_mda.copy()
          lowercase   :    int			=							_as_bytes(_a    )
          mda.update(_a    )
          # Convert to integer with hexadecimal conversion
          return int(mda.hexdigest()     ,   16    )




"""simple docstring"""




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


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[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 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _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     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

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

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


