


'''simple docstring'''


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       snake_case_       (  SCREAMING_SNAKE_CASE__=2							,				SCREAMING_SNAKE_CASE__=True							,				SCREAMING_SNAKE_CASE__=False					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__							,				split_batches=SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				torch.nn.Linear(2							,				4					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				torch.optim.AdamW(model.parameters()							,				lr=1.0					)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE__							,				max_lr=0.0_1							,				steps_per_epoch=2							,				epochs=1					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Tuple							=				accelerator.prepare(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				# 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       snake_case_       (  SCREAMING_SNAKE_CASE__=2							,				SCREAMING_SNAKE_CASE__=True							,				SCREAMING_SNAKE_CASE__=False					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__							,				split_batches=SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	str							=				torch.nn.Linear(2							,				4					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.optim.AdamW(model.parameters()							,				lr=1.0					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE__							,				lr_lambda=lambda SCREAMING_SNAKE_CASE__					: 1 - n / 10					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				accelerator.prepare(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				# Optimizer has stepped
				_SCREAMING_SNAKE_CASE			:	List[str]							=				False
				scheduler.step()
				_SCREAMING_SNAKE_CASE			:	Tuple							=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				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
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				1 - 2 / 10
				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       snake_case_       (  SCREAMING_SNAKE_CASE__ = 2					):







				"""simple docstring"""





				from transformers import get_linear_schedule_with_warmup

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[1, 2, 4]
				for num_steps in steps:
								_SCREAMING_SNAKE_CASE			:	List[str]							=				GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE__							,				adjust_scheduler=num_steps > 1					)
								_SCREAMING_SNAKE_CASE			:	Any							=				Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.nn.Linear(2							,				4					)
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.optim.AdamW(model.parameters()							,				lr=1_0.0					)
								_SCREAMING_SNAKE_CASE			:	str							=				get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE__							,				num_warmup_steps=0							,				num_training_steps=20					)

								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	int							=				accelerator.prepare(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

								for i in range(10 * num_steps					):
												with accelerator.accumulate(SCREAMING_SNAKE_CASE__					):
																optimizer.step()
																scheduler.step()

												if i == (10 * 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    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											debug_launcher(partial(__snake_case   , num_processes=1  )   , num_processes=1  )
											debug_launcher(partial(__snake_case   , num_processes=1   , split_batches=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											debug_launcher(partial(__snake_case   , num_processes=1  )   , num_processes=1  )
											debug_launcher(partial(__snake_case   , num_processes=1   , split_batches=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											debug_launcher(partial(__snake_case   , num_processes=1   , step_scheduler_with_optimizer=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											debug_launcher(partial(__snake_case   , num_processes=1   , step_scheduler_with_optimizer=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											AcceleratorState._reset_state(__snake_case  )
											debug_launcher(__snake_case  )
											debug_launcher(partial(__snake_case   , num_processes=1   , split_batches=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											AcceleratorState._reset_state(__snake_case  )
											debug_launcher(__snake_case  )
											debug_launcher(partial(__snake_case   , num_processes=1   , split_batches=__snake_case  )   , num_processes=1  )






							def UpperCAmelCase_     (   self  ):
											AcceleratorState._reset_state(__snake_case  )
											debug_launcher(partial(__snake_case   , step_scheduler_with_optimizer=__snake_case  )  )






							def UpperCAmelCase_     (   self  ):
											AcceleratorState._reset_state(__snake_case  )
											debug_launcher(partial(__snake_case   , step_scheduler_with_optimizer=__snake_case  )  )






							@require_huggingface_suite
							def UpperCAmelCase_     (   self  ):
											AcceleratorState._reset_state(__snake_case  )
											debug_launcher(partial(__snake_case   , num_processes=1  )  )
											debug_launcher(__snake_case  )








'''simple docstring'''


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


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[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    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






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

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

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




