





from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase					, **UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case      : str														=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case      : Dict														=		{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]							)











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        lowerCAmelCase_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						str:
       UpperCamelCase_      				=     Accelerator(step_scheduler_with_optimizer=UpperCamelCase_    ,						split_batches=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
       UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=1.0     )
       UpperCamelCase_      				=     torch.optim.lr_scheduler.OneCycleLR(UpperCamelCase_    ,						max_lr=0.01    ,						steps_per_epoch=2    ,						epochs=1     )
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

       # 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        lowerCAmelCase_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						List[Any]:
       UpperCamelCase_      				=     Accelerator(step_scheduler_with_optimizer=UpperCamelCase_    ,						split_batches=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
       UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=1.0     )
       UpperCamelCase_      				=     torch.optim.lr_scheduler.LambdaLR(UpperCamelCase_    ,						lr_lambda=lambda UpperCamelCase_     : 1 - n / 10     )
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

       # Optimizer has stepped
       UpperCamelCase_      				=     False
       scheduler.step()
       UpperCamelCase_      				=     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
       UpperCamelCase_      				=     True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              UpperCamelCase_      				=     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        lowerCAmelCase_       (							UpperCamelCase_ = 2     )   ->						int:
       from transformers import get_linear_schedule_with_warmup

       UpperCamelCase_      				=     [1, 2, 4]
       for num_steps in steps:
              UpperCamelCase_      				=     GradientAccumulationPlugin(num_steps=UpperCamelCase_    ,						adjust_scheduler=num_steps > 1     )
              UpperCamelCase_      				=     Accelerator(gradient_accumulation_plugin=UpperCamelCase_     )
              UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
              UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=10.0     )
              UpperCamelCase_      				=     get_linear_schedule_with_warmup(optimizer=UpperCamelCase_    ,						num_warmup_steps=0    ,						num_training_steps=20     )

              UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

              for i in range(10 * num_steps     ):
                     with accelerator.accumulate(UpperCamelCase_     ):
                            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 							_UpperCamelCase    (		unittest.TestCase   ):

       def lowercase       (						self:       Dict						)				->       Any:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)    ,      num_processes=1						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)    ,      num_processes=1						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Optional[int]						)				->       List[str]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Tuple						)				->       Optional[int]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       int						)				->       Tuple:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)						)

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)						)



       @require_huggingface_suite
       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)

