import importlib.metadata
import sys

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


UpperCAmelCase							   =      importlib.metadata.version("""numpy""")
UpperCAmelCase							   =      """.""".join([str(x) for x in sys.version_info[:3]])






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def _lowercase(    self    )	->Optional[int]:
        # lt + different version strings
        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    )	->Tuple:
        # matching requirement
        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							   =      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_							,     )