


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


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

__UpperCamelCase					:	Tuple          =				{
    # 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 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    A_	:  int								=			'graphormer'
    A_	:  Tuple								=			['past_key_values']





    def __init__(						self					: str							, __snake_case					: int = 1							, __snake_case					: int = 512 * 9							, __snake_case					: int = 512 * 3							, __snake_case					: int = 512							, __snake_case					: int = 512							, __snake_case					: int = 512							, __snake_case					: int = 128							, __snake_case					: int = 5							, __snake_case					: int = 1_024							, __snake_case					: str = "multi_hop"							, __snake_case					: int = 512							, __snake_case					: bool = False							, __snake_case					: int = 12							, __snake_case					: int = 768							, __snake_case					: int = 768							, __snake_case					: int = 32							, __snake_case					: float = 0.1							, __snake_case					: float = 0.1							, __snake_case					: float = 0.0							, __snake_case					: bool = False							, __snake_case					: bool = False							, __snake_case					: bool = False							, __snake_case					: str = "gelu"							, __snake_case					: float = None							, __snake_case					: bool = False							, __snake_case					: int = 0							, __snake_case					: bool = False							, __snake_case					: float = 0.0							, __snake_case					: int = 8							, __snake_case					: int = None							, __snake_case					: int = None							, __snake_case					: bool = True							, __snake_case					: bool = True							, __snake_case					: Tuple=0							, __snake_case					: Any=1							, __snake_case					: Tuple=2							, **__snake_case					: Optional[Any]							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   int							= num_classes
       UpperCAmelCase_				:   List[str]							= num_atoms
       UpperCAmelCase_				:   Dict							= num_in_degree
       UpperCAmelCase_				:   Dict							= num_out_degree
       UpperCAmelCase_				:   Any							= num_edges
       UpperCAmelCase_				:   Optional[int]							= num_spatial
       UpperCAmelCase_				:   Union[str, Any]							= num_edge_dis
       UpperCAmelCase_				:   Any							= edge_type
       UpperCAmelCase_				:   Tuple							= multi_hop_max_dist
       UpperCAmelCase_				:   Optional[Any]							= spatial_pos_max
       UpperCAmelCase_				:   Union[str, Any]							= max_nodes
       UpperCAmelCase_				:   List[str]							= num_hidden_layers
       UpperCAmelCase_				:   str							= embedding_dim
       UpperCAmelCase_				:   List[str]							= embedding_dim
       UpperCAmelCase_				:   List[str]							= ffn_embedding_dim
       UpperCAmelCase_				:   Optional[int]							= num_attention_heads
       UpperCAmelCase_				:   str							= dropout
       UpperCAmelCase_				:   Optional[Any]							= attention_dropout
       UpperCAmelCase_				:   Any							= layerdrop
       UpperCAmelCase_				:   Union[str, Any]							= encoder_normalize_before
       UpperCAmelCase_				:   Tuple							= pre_layernorm
       UpperCAmelCase_				:   Optional[Any]							= apply_graphormer_init
       UpperCAmelCase_				:   str							= activation_fn
       UpperCAmelCase_				:   Tuple							= embed_scale
       UpperCAmelCase_				:   Dict							= freeze_embeddings
       UpperCAmelCase_				:   int							= num_trans_layers_to_freeze
       UpperCAmelCase_				:   Union[str, Any]							= share_input_output_embed
       UpperCAmelCase_				:   str							= traceable
       UpperCAmelCase_				:   List[str]							= q_noise
       UpperCAmelCase_				:   List[Any]							= qn_block_size

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

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



import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers.activations_tf import get_tf_activation





@require_tf
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]						)
       UpperCAmelCase_				:   List[Any]							= get_tf_activation('''gelu'''						)
       UpperCAmelCase_				:   Any							= get_tf_activation('''gelu_10'''						)

       UpperCAmelCase_				:   int							= gelu(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= geluaa(__snake_case						)

       UpperCAmelCase_				:   Union[str, Any]							= tf.where(y_gelu_aa < 10.0							, 1.0							, 0.0						)

       self.assertEqual(tf.math.reduce_max(__snake_case						).numpy().item()							, 10.0						)
       self.assertTrue(np.allclose(y_gelu * clipped_mask							, y_gelu_aa * clipped_mask						)						)





    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       get_tf_activation('''gelu'''						)
       get_tf_activation('''gelu_10'''						)
       get_tf_activation('''gelu_fast'''						)
       get_tf_activation('''gelu_new'''						)
       get_tf_activation('''glu'''						)
       get_tf_activation('''mish'''						)
       get_tf_activation('''quick_gelu'''						)
       get_tf_activation('''relu'''						)
       get_tf_activation('''sigmoid'''						)
       get_tf_activation('''silu'''						)
       get_tf_activation('''swish'''						)
       get_tf_activation('''tanh'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation('''bogus'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation(__snake_case						)