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


A  :			Optional[int]     				=				logging.get_logger(__name__)

A  :			Union[str, Any]     				=				{
    # 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    ):
			snake_case_		     =					'''graphormer'''
			snake_case_		     =					['''past_key_values''']




			def __init__(  self	,  _snake_case = 1	,  _snake_case = 512 * 9	,  _snake_case = 512 * 3	,  _snake_case = 512	,  _snake_case = 512	,  _snake_case = 512	,  _snake_case = 128	,  _snake_case = 5	,  _snake_case = 1_024	,  _snake_case = "multi_hop"	,  _snake_case = 512	,  _snake_case = False	,  _snake_case = 12	,  _snake_case = 768	,  _snake_case = 768	,  _snake_case = 32	,  _snake_case = 0.1	,  _snake_case = 0.1	,  _snake_case = 0.0	,  _snake_case = False	,  _snake_case = False	,  _snake_case = False	,  _snake_case = "gelu"	,  _snake_case = None	,  _snake_case = False	,  _snake_case = 0	,  _snake_case = False	,  _snake_case = 0.0	,  _snake_case = 8	,  _snake_case = None	,  _snake_case = None	,  _snake_case = True	,  _snake_case = True	,  _snake_case=0	,  _snake_case=1	,  _snake_case=2	,  **_snake_case	,  )					->   Optional[int]:


							'''simple docstring'''




							__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=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__	,  **lowerCamelCase__	,  )

import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


A  :			Optional[int]     				=				2 << 2_0  # 2mb
A  :			Optional[int]     				=				get_logger(__name__)







class     __A(				a    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					self._options.get_all_options().get('''runner'''       )
								return runner in [None, "DirectRunner", "PortableRunner"]



def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Union[str, Any]:
				__a    							=					FileSystems
				if fs.exists(a__						):
								if force_upload:
												logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.create(a__						) as remote_file:
								with open(a__		,					'''rb'''						) as local_file:
												__a    							=					local_file.read(a__						)
												while chunk:
																remote_file.write(a__						)
																__a    							=					local_file.read(a__						)






def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Dict:
				__a    							=					FileSystems
				if os.path.exists(a__						):
								if force_download:
												logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.open(a__						) as remote_file:
								with open(a__		,					'''wb'''						) as local_file:
												__a    							=					remote_file.read(a__						)
												while chunk:
																local_file.write(a__						)
																__a    							=					remote_file.read(a__						)