
'''simple docstring'''
import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


A__ :		Any					=importlib.metadata.version('numpy')
A__ :		Optional[int]					=".".join([str(x) for x in sys.version_info[:3]])







class 			__A					(			__lowerCamelCase      ):
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   require_version_core("""numpy<1000.4.5"""							)
   require_version_core("""numpy<1000.4"""							)
   require_version_core("""numpy<1000"""							)

   # le
   require_version_core("""numpy<=1000.4.5"""							)
   require_version_core(f"numpy<={numpy_ver}"							)

   # eq
   require_version_core(f"numpy=={numpy_ver}"							)

   # ne
   require_version_core("""numpy!=1000.4.5"""							)

   # ge
   require_version_core("""numpy>=1.0"""							)
   require_version_core("""numpy>=1.0.0"""							)
   require_version_core(f"numpy>={numpy_ver}"							)

   # gt
   require_version_core("""numpy>1.0.0"""							)

   # mix
   require_version_core("""numpy>1.0.0,<1000"""							)

   # requirement w/o version
   require_version_core("""numpy"""							)

   # unmet requirements due to version conflict
   for req in ["numpy==1.0.0", "numpy>=1000.0.0", f"numpy<{numpy_ver}"]:
     try:
       require_version_core(lowerCamelCase							)
     except ImportError as e:
       self.assertIn(f"{req} is required"					, str(lowerCamelCase							)							)
       self.assertIn("""but found"""					, str(lowerCamelCase							)							)

        # unmet requirements due to missing module
   for req in ["numpipypie>1", "numpipypie2"]:
     try:
       require_version_core(lowerCamelCase							)
     except importlib.metadata.PackageNotFoundError as e:
       self.assertIn(f"The \'{req}\' distribution was not found and is required by this application"					, str(lowerCamelCase							)							)
       self.assertIn("""Try: pip install transformers -U"""					, str(lowerCamelCase							)							)

        # bogus requirements formats:
        # 1. whole thing
   for req in ["numpy??1.0.0", "numpy1.0.0"]:
     try:
       require_version_core(lowerCamelCase							)
     except ValueError as e:
       self.assertIn("""requirement needs to be in the pip package format"""					, str(lowerCamelCase							)							)
        # 2. only operators
   for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
     try:
       require_version_core(lowerCamelCase							)
     except ValueError as e:
       self.assertIn("""need one of """					, str(lowerCamelCase							)							)






 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   require_version("""python>=3.6.0"""							)

   # not matching requirements
   for req in ["python>9.9.9", "python<3.0.0"]:
     try:
       require_version_core(lowerCamelCase							)
     except ImportError as e:
       self.assertIn(f"{req} is required"					, str(lowerCamelCase							)							)
       self.assertIn(f"but found python=={python_ver}"					, str(lowerCamelCase							)							)










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


__UpperCAmelCase			:  List[str]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""graphormer"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       List[Any]				,							A					:       int = 1				,							A					:       int = 512 * 9				,							A					:       int = 512 * 3				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 128				,							A					:       int = 5				,							A					:       int = 1_024				,							A					:       str = "multi_hop"				,							A					:       int = 512				,							A					:       bool = False				,							A					:       int = 12				,							A					:       int = 768				,							A					:       int = 768				,							A					:       int = 32				,							A					:       float = 0.1				,							A					:       float = 0.1				,							A					:       float = 0.0				,							A					:       bool = False				,							A					:       bool = False				,							A					:       bool = False				,							A					:       str = "gelu"				,							A					:       float = None				,							A					:       bool = False				,							A					:       int = 0				,							A					:       bool = False				,							A					:       float = 0.0				,							A					:       int = 8				,							A					:       int = None				,							A					:       int = None				,							A					:       bool = True				,							A					:       bool = True				,							A					:       Any=0				,							A					:       Optional[int]=1				,							A					:       Any=2				,							**A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		num_classes
         __snake_case:		int					    =		num_atoms
         __snake_case:		List[str]					    =		num_in_degree
         __snake_case:		str					    =		num_out_degree
         __snake_case:		Tuple					    =		num_edges
         __snake_case:		Optional[int]					    =		num_spatial
         __snake_case:		int					    =		num_edge_dis
         __snake_case:		Tuple					    =		edge_type
         __snake_case:		str					    =		multi_hop_max_dist
         __snake_case:		List[str]					    =		spatial_pos_max
         __snake_case:		Optional[Any]					    =		max_nodes
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Optional[int]					    =		ffn_embedding_dim
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Any					    =		dropout
         __snake_case:		Dict					    =		attention_dropout
         __snake_case:		Any					    =		layerdrop
         __snake_case:		Dict					    =		encoder_normalize_before
         __snake_case:		Any					    =		pre_layernorm
         __snake_case:		Optional[int]					    =		apply_graphormer_init
         __snake_case:		List[str]					    =		activation_fn
         __snake_case:		Optional[int]					    =		embed_scale
         __snake_case:		Dict					    =		freeze_embeddings
         __snake_case:		Any					    =		num_trans_layers_to_freeze
         __snake_case:		int					    =		share_input_output_embed
         __snake_case:		Dict					    =		traceable
         __snake_case:		str					    =		q_noise
         __snake_case:		Union[str, Any]					    =		qn_block_size

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

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






