



# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = False      )   ->	Union[str, Any]:
    _A      =       scheduler
    _A      =       optimizers if isinstance(__lowerCAmelCase			,						(list, tuple)      ) else [optimizers]
    _A      =       split_batches
    _A      =       step_with_optimizer
    _A      =       GradientState()

 def     snake_case_      (    self					:					List[str]			,						*__lowerCAmelCase					:					List[str]			,						**__lowerCAmelCase					:					List[Any]      )   ->	Optional[int]:
    if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*__lowerCAmelCase			,						**__lowerCAmelCase      )
       return

    # Otherwise, first make sure the optimizer was stepped.
    if not self.gradient_state.sync_gradients:
       if self.gradient_state.adjust_scheduler:
          self.scheduler._step_count += 1
       return

    for opt in self.optimizers:
       if opt.step_was_skipped:
          return
    if self.split_batches:
       # Split batches -> the training dataloader batch size is not changed so one step per training step
       self.scheduler.step(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       _A      =       AcceleratorState().num_processes
       for _ in range(__lowerCAmelCase      ):
          # Special case when using OneCycle and `drop_last` was not used
          if hasattr(self.scheduler			,						'''total_steps'''      ):
             if self.scheduler._step_count <= self.scheduler.total_steps:
                self.scheduler.step(*__lowerCAmelCase			,						**__lowerCAmelCase      )
          else:
             self.scheduler.step(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					int      )   ->	List[Any]:
    return self.scheduler.get_last_lr()

 def     snake_case_      (    self					:					str      )   ->	Optional[int]:
    return self.scheduler.state_dict()

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[int]      )   ->	Optional[Any]:
    self.scheduler.load_state_dict(__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	int:
    return self.scheduler.get_lr()

 def     snake_case_      (    self					:					Optional[Any]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[Any]      )   ->	Tuple:
    return self.scheduler.print_lr(*__lowerCAmelCase			,						**__lowerCAmelCase      )




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






