






import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    ElectraForCausalLM,
							    ElectraForMaskedLM,
							    ElectraForMultipleChoice,
							    ElectraForPreTraining,
							    ElectraForQuestionAnswering,
							    ElectraForSequenceClassification,
							    ElectraForTokenClassification,
							    ElectraModel,
							)
							from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST




class 						UpperCAmelCase_  :


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=13						, _SCREAMING_SNAKE_CASE=7						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=99						, _SCREAMING_SNAKE_CASE=32						, _SCREAMING_SNAKE_CASE=5						, _SCREAMING_SNAKE_CASE=4						, _SCREAMING_SNAKE_CASE=37						, _SCREAMING_SNAKE_CASE="gelu"						, _SCREAMING_SNAKE_CASE=0.1						, _SCREAMING_SNAKE_CASE=0.1						, _SCREAMING_SNAKE_CASE=512						, _SCREAMING_SNAKE_CASE=16						, _SCREAMING_SNAKE_CASE=2						, _SCREAMING_SNAKE_CASE=0.02						, _SCREAMING_SNAKE_CASE=3						, _SCREAMING_SNAKE_CASE=4						, _SCREAMING_SNAKE_CASE=None						, )       -> Tuple:
								snake_case_ :							Dict         =				parent
								snake_case_ :							List[str]         =				batch_size
								snake_case_ :							str         =				seq_length
								snake_case_ :							List[str]         =				is_training
								snake_case_ :							Optional[int]         =				use_input_mask
								snake_case_ :							Tuple         =				use_token_type_ids
								snake_case_ :							Dict         =				use_labels
								snake_case_ :							str         =				vocab_size
								snake_case_ :							int         =				hidden_size
								snake_case_ :							Tuple         =				num_hidden_layers
								snake_case_ :							int         =				num_attention_heads
								snake_case_ :							Dict         =				intermediate_size
								snake_case_ :							Union[str, Any]         =				hidden_act
								snake_case_ :							Optional[Any]         =				hidden_dropout_prob
								snake_case_ :							List[Any]         =				attention_probs_dropout_prob
								snake_case_ :							Optional[Any]         =				max_position_embeddings
								snake_case_ :							Tuple         =				type_vocab_size
								snake_case_ :							Union[str, Any]         =				type_sequence_label_size
								snake_case_ :							Tuple         =				initializer_range
								snake_case_ :							Dict         =				num_labels
								snake_case_ :							int         =				num_choices
								snake_case_ :							List[str]         =				scope






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							Union[str, Any]         =				ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size )

								snake_case_ :							List[str]         =				None
								if self.use_input_mask:
										snake_case_ :							Optional[Any]         =				random_attention_mask([self.batch_size, self.seq_length] )

								snake_case_ :							str         =				None
								if self.use_token_type_ids:
										snake_case_ :							List[Any]         =				ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size )

								snake_case_ :							Union[str, Any]         =				None
								snake_case_ :							List[str]         =				None
								snake_case_ :							Optional[int]         =				None
								if self.use_labels:
										snake_case_ :							Optional[int]         =				ids_tensor([self.batch_size]						, self.type_sequence_label_size )
										snake_case_ :							Dict         =				ids_tensor([self.batch_size, self.seq_length]						, self.num_labels )
										snake_case_ :							Tuple         =				ids_tensor([self.batch_size]						, self.num_choices )
										snake_case_ :							str         =				ids_tensor([self.batch_size, self.seq_length]						, 1 )

								snake_case_ :							Any         =				self.get_config()

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    fake_token_labels,
								)






						def 					_lowerCAmelCase				(		self )       -> Any:
								return ElectraConfig(
								    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, is_decoder=_SCREAMING_SNAKE_CASE						, initializer_range=self.initializer_range						, )






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								(
								    (
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				
								) :							List[Any]         =				self.prepare_config_and_inputs()

								snake_case_ :							int         =				True
								snake_case_ :							int         =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
								snake_case_ :							Dict         =				ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2 )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Any:
								snake_case_ :							str         =				ElectraModel(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Any         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE )
								snake_case_ :							int         =				model(_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE )
								snake_case_ :							str         =				model(_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							int         =				True
								snake_case_ :							Union[str, Any]         =				ElectraModel(_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Union[str, Any]         =				model(
								    _SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, encoder_hidden_states=_SCREAMING_SNAKE_CASE						, encoder_attention_mask=_SCREAMING_SNAKE_CASE						, )
								snake_case_ :							Union[str, Any]         =				model(
								    _SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, encoder_hidden_states=_SCREAMING_SNAKE_CASE						, )
								snake_case_ :							str         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Tuple:
								snake_case_ :							List[str]         =				ElectraForMaskedLM(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Optional[Any]         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Optional[Any]:
								snake_case_ :							Union[str, Any]         =				ElectraForCausalLM(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Optional[Any]         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Optional[Any]:
								snake_case_ :							str         =				self.num_labels
								snake_case_ :							List[Any]         =				ElectraForTokenClassification(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Optional[int]         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Dict:
								snake_case_ :							Tuple         =				self.num_labels
								snake_case_ :							int         =				ElectraForPreTraining(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Dict         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							Dict         =				self.num_labels
								snake_case_ :							List[str]         =				ElectraForSequenceClassification(_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							int         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							List[str]         =				ElectraForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							Optional[int]         =				model(
								    _SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, start_positions=_SCREAMING_SNAKE_CASE						, end_positions=_SCREAMING_SNAKE_CASE						, )
								self.parent.assertEqual(result.start_logits.shape						, (self.batch_size, self.seq_length) )
								self.parent.assertEqual(result.end_logits.shape						, (self.batch_size, self.seq_length) )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> Optional[Any]:
								snake_case_ :							Any         =				self.num_choices
								snake_case_ :							List[str]         =				ElectraForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
								model.to(_SCREAMING_SNAKE_CASE )
								model.eval()
								snake_case_ :							List[str]         =				input_ids.unsqueeze(1 ).expand(-1						, self.num_choices						, -1 ).contiguous()
								snake_case_ :							int         =				token_type_ids.unsqueeze(1 ).expand(-1						, self.num_choices						, -1 ).contiguous()
								snake_case_ :							Optional[Any]         =				input_mask.unsqueeze(1 ).expand(-1						, self.num_choices						, -1 ).contiguous()
								snake_case_ :							str         =				model(
								    _SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE						, token_type_ids=_SCREAMING_SNAKE_CASE						, labels=_SCREAMING_SNAKE_CASE						, )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_choices) )






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Optional[int]         =				self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				(
								    snake_case_
								)       ,				
								) :							Any         =				config_and_inputs
								snake_case_ :							str         =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_torch
class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ,     unittest.TestCase    ):


						'''simple docstring'''




						A   :      Dict						 =    (
						    (
						        ElectraModel,
						        ElectraForPreTraining,
						        ElectraForMaskedLM,
						        ElectraForCausalLM,
						        ElectraForMultipleChoice,
						        ElectraForTokenClassification,
						        ElectraForSequenceClassification,
						        ElectraForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						A   :      Tuple						 =    (
						    {
						        'feature-extraction': ElectraModel,
						        'fill-mask': ElectraForMaskedLM,
						        'question-answering': ElectraForQuestionAnswering,
						        'text-classification': ElectraForSequenceClassification,
						        'text-generation': ElectraForCausalLM,
						        'token-classification': ElectraForTokenClassification,
						        'zero-shot': ElectraForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						A   :      Any						 =    True






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=False )       -> Any:
								snake_case_ :							int         =				super()._prepare_for_class(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, return_labels=_SCREAMING_SNAKE_CASE )

								if return_labels:
										if model_class in get_values(_SCREAMING_SNAKE_CASE ):
												snake_case_ :							Any         =				torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length)						, dtype=torch.long						, device=_SCREAMING_SNAKE_CASE )
								return inputs_dict






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							List[str]         =				ElectraModelTester(self )
								snake_case_ :							Union[str, Any]         =				ConfigTester(self						, config_class=_SCREAMING_SNAKE_CASE						, hidden_size=37 )






						def 					_lowerCAmelCase				(		self )       -> int:
								self.config_tester.run_common_tests()






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Dict         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_model(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							Optional[int]         =				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_electra_model_as_decoder(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							str         =				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										snake_case_ :							Union[str, Any]         =				type
										self.model_tester.create_and_check_electra_model(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							Optional[Any]         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_masked_lm(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							Optional[int]         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_token_classification(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							str         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_pretraining(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							int         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_sequence_classification(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							Optional[int]         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_question_answering(*_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								snake_case_ :							List[str]         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_multiple_choice(*_SCREAMING_SNAKE_CASE )






						@slow
						def 					_lowerCAmelCase				(		self )       -> str:
								for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_ :							Any         =				ElectraModel.from_pretrained(_SCREAMING_SNAKE_CASE )
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							Optional[int]         =				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_electra_for_causal_lm(*_SCREAMING_SNAKE_CASE )




@require_torch
class 						UpperCAmelCase_  (		unittest.TestCase    ):


						'''simple docstring'''




						@slow
						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							Optional[Any]         =				ElectraModel.from_pretrained("google/electra-small-discriminator" )
								snake_case_ :							Optional[Any]         =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
								snake_case_ :							int         =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
								snake_case_ :							Any         =				model(_SCREAMING_SNAKE_CASE						, attention_mask=_SCREAMING_SNAKE_CASE )[0]
								snake_case_ :							str         =				torch.Size((1, 11, 256) )
								self.assertEqual(output.shape						, _SCREAMING_SNAKE_CASE )
								snake_case_ :							Dict         =				torch.tensor(
								    [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]] )

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						, _SCREAMING_SNAKE_CASE						, atol=1e-4 ) )








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


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							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 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    'graphormer'
						A   :      List[str]						 =    ['past_key_values']






						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1						, _SCREAMING_SNAKE_CASE = 512 * 9						, _SCREAMING_SNAKE_CASE = 512 * 3						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 128						, _SCREAMING_SNAKE_CASE = 5						, _SCREAMING_SNAKE_CASE = 1024						, _SCREAMING_SNAKE_CASE = "multi_hop"						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 12						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 32						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = "gelu"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = 8						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE=2						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							str         =				num_classes
								snake_case_ :							Union[str, Any]         =				num_atoms
								snake_case_ :							Dict         =				num_in_degree
								snake_case_ :							Union[str, Any]         =				num_out_degree
								snake_case_ :							List[Any]         =				num_edges
								snake_case_ :							Union[str, Any]         =				num_spatial
								snake_case_ :							int         =				num_edge_dis
								snake_case_ :							List[Any]         =				edge_type
								snake_case_ :							Any         =				multi_hop_max_dist
								snake_case_ :							Tuple         =				spatial_pos_max
								snake_case_ :							Union[str, Any]         =				max_nodes
								snake_case_ :							str         =				num_hidden_layers
								snake_case_ :							str         =				embedding_dim
								snake_case_ :							List[str]         =				embedding_dim
								snake_case_ :							Dict         =				ffn_embedding_dim
								snake_case_ :							Any         =				num_attention_heads
								snake_case_ :							List[str]         =				dropout
								snake_case_ :							List[str]         =				attention_dropout
								snake_case_ :							Optional[Any]         =				layerdrop
								snake_case_ :							Optional[int]         =				encoder_normalize_before
								snake_case_ :							Union[str, Any]         =				pre_layernorm
								snake_case_ :							int         =				apply_graphormer_init
								snake_case_ :							List[Any]         =				activation_fn
								snake_case_ :							str         =				embed_scale
								snake_case_ :							int         =				freeze_embeddings
								snake_case_ :							Optional[Any]         =				num_trans_layers_to_freeze
								snake_case_ :							int         =				share_input_output_embed
								snake_case_ :							int         =				traceable
								snake_case_ :							Tuple         =				q_noise
								snake_case_ :							Tuple         =				qn_block_size

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

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