

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


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

__snake_case							:     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_    ):
						"""simple docstring"""






						_A  :							Optional[int]   			=	'''graphormer'''
						_A  :							Optional[int]   			=	['''past_key_values''']





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

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

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





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Dict				    =				torch.nn.Linear(2       ,					4)
			a_		:		int				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.OneCycleLR(a__       ,					max_lr=0.01       ,					steps_per_epoch=2       ,					epochs=1)
			a_    ,			a_    ,			a_		:		List[Any]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		int				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Tuple				    =				torch.nn.Linear(2       ,					4)
			a_		:		str				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.LambdaLR(a__       ,					lr_lambda=lambda a__: 1 - n / 1_0)
			a_    ,			a_    ,			a_		:		List[str]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			a_		:		Optional[Any]				    =				False
			scheduler.step()
			a_		:		Union[str, Any]				    =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

			# Optimizer has not stepped
			a_		:		Any				    =				True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						a_		:		Any				    =				1 - 2 / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''


def 			_UpperCAmelCase							(		a__ = 2):

			'''simple docstring'''



			from transformers import get_linear_schedule_with_warmup

			a_		:		int				    =				[1, 2, 4]
			for num_steps in steps:
						a_		:		List[str]				    =				GradientAccumulationPlugin(num_steps=a__       ,					adjust_scheduler=num_steps > 1)
						a_		:		List[str]				    =				Accelerator(gradient_accumulation_plugin=a__)
						a_		:		int				    =				torch.nn.Linear(2       ,					4)
						a_		:		Any				    =				torch.optim.AdamW(model.parameters()       ,					lr=10.0)
						a_		:		List[Any]				    =				get_linear_schedule_with_warmup(optimizer=a__       ,					num_warmup_steps=0       ,					num_training_steps=2_0)

						a_    ,			a_    ,			a_		:		int				    =				accelerator.prepare(a__       ,					a__       ,					a__)

						for i in range(1_0 * num_steps):
									with accelerator.accumulate(a__):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
						assert (
						    scheduler.get_last_lr()[0] == 0
						), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
						GradientState._reset_state()



@require_cpu
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				str:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						@require_huggingface_suite
						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1  )  )
									debug_launcher(_lowercase  )


