


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


snake_case_  :	Union[str, Any]							  =							logging.get_logger(__name__)

snake_case_  :	Dict							  =							{
    # 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__							(						SCREAMING_SNAKE_CASE__     ):
						UpperCAmelCase									=  "graphormer"
						UpperCAmelCase									=  ["past_key_values"]




						def __init__(				self    :					Any ,			_a    :					List[Any] = 1 ,			_a    :					Dict = 512 * 9 ,			_a    :					List[Any] = 512 * 3 ,			_a    :					Dict = 512 ,			_a    :					Optional[int] = 512 ,			_a    :					List[Any] = 512 ,			_a    :					str = 128 ,			_a    :					Any = 5 ,			_a    :					Optional[Any] = 1024 ,			_a    :					Optional[int] = "multi_hop" ,			_a    :					Any = 512 ,			_a    :					str = False ,			_a    :					Optional[int] = 12 ,			_a    :					Optional[Any] = 768 ,			_a    :					Optional[Any] = 768 ,			_a    :					Tuple = 32 ,			_a    :					str = 0.1 ,			_a    :					List[Any] = 0.1 ,			_a    :					Optional[Any] = 0.0 ,			_a    :					str = False ,			_a    :					Tuple = False ,			_a    :					int = False ,			_a    :					Dict = "gelu" ,			_a    :					int = None ,			_a    :					str = False ,			_a    :					Tuple = 0 ,			_a    :					int = False ,			_a    :					List[Any] = 0.0 ,			_a    :					Union[str, Any] = 8 ,			_a    :					Any = None ,			_a    :					str = None ,			_a    :					Any = True ,			_a    :					Dict = True ,			_a    :					str=0 ,			_a    :					str=1 ,			_a    :					Union[str, Any]=2 ,			**_a    :					List[Any] ,			)							->   Optional[Any]:


										"""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=snake_case__ ,			bos_token_id=snake_case__ ,			eos_token_id=snake_case__ ,			**snake_case__ ,			)







"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






