






'''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 			__UpperCAmelCase				(lowercase__=2	,lowercase__=True	,lowercase__=False				)		->				Optional[Any]:

						'''simple docstring'''


						a_				     =   Accelerator(step_scheduler_with_optimizer=lowercase__	,split_batches=lowercase__				)
						a_				     =   torch.nn.Linear(2	,4				)
						a_				     =   torch.optim.AdamW(model.parameters()	,lr=1.0				)
						a_				     =   torch.optim.lr_scheduler.OneCycleLR(lowercase__	,max_lr=0.01	,steps_per_epoch=2	,epochs=1				)
						a_     ,       a_     ,       a_				     =   accelerator.prepare(lowercase__	,lowercase__	,lowercase__				)

						# 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				(lowercase__=2	,lowercase__=True	,lowercase__=False				)		->				Tuple:

						'''simple docstring'''


						a_				     =   Accelerator(step_scheduler_with_optimizer=lowercase__	,split_batches=lowercase__				)
						a_				     =   torch.nn.Linear(2	,4				)
						a_				     =   torch.optim.AdamW(model.parameters()	,lr=1.0				)
						a_				     =   torch.optim.lr_scheduler.LambdaLR(lowercase__	,lr_lambda=lambda lowercase__				: 1 - n / 10				)
						a_     ,       a_     ,       a_				     =   accelerator.prepare(lowercase__	,lowercase__	,lowercase__				)

						# Optimizer has stepped
						a_				     =   False
						scheduler.step()
						a_				     =   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
						a_				     =   True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												a_				     =   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 			__UpperCAmelCase				(lowercase__ = 2				)		->				int:

						'''simple docstring'''


						from transformers import get_linear_schedule_with_warmup

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

												a_     ,       a_     ,       a_				     =   accelerator.prepare(lowercase__	,lowercase__	,lowercase__				)

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



		def _lowerCAmelCase     (    self:    Optional[Any])  ->Optional[int]:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Dict)  ->int:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Tuple)  ->str:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    str)  ->Union[str, Any]:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Union[str, Any])  ->List[Any]:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    List[Any])  ->int:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    str)  ->Any:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Optional[int])  ->Any:






								'''simple docstring'''


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




		@require_huggingface_suite
		def _lowerCAmelCase     (    self:    Dict)  ->Optional[int]:






								'''simple docstring'''


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











'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


a_      = logging.get_logger(__name__)

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





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		_UpperCAmelCase         ='''mobilebert'''



		def __init__(    self:    Union[str, Any]				,   a:    str=3_05_22				,   a:    List[str]=5_12				,   a:    List[str]=24				,   a:    Dict=4				,   a:    List[str]=5_12				,   a:    Optional[int]="relu"				,   a:    Dict=0.0				,   a:    Union[str, Any]=0.1				,   a:    int=5_12				,   a:    Optional[Any]=2				,   a:    List[Any]=0.02				,   a:    Optional[Any]=1e-12				,   a:    List[Any]=0				,   a:    Optional[int]=1_28				,   a:    Union[str, Any]=True				,   a:    Dict=True				,   a:    List[Any]=1_28				,   a:    Any=False				,   a:    Any=True				,   a:    Dict=4				,   a:    List[Any]="no_norm"				,   a:    Any=True				,   a:    Tuple=None				,   **a:    Any				,   )  ->Any:






								'''simple docstring'''


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

								a_				     =   vocab_size
								a_				     =   hidden_size
								a_				     =   num_hidden_layers
								a_				     =   num_attention_heads
								a_				     =   hidden_act
								a_				     =   intermediate_size
								a_				     =   hidden_dropout_prob
								a_				     =   attention_probs_dropout_prob
								a_				     =   max_position_embeddings
								a_				     =   type_vocab_size
								a_				     =   initializer_range
								a_				     =   layer_norm_eps
								a_				     =   embedding_size
								a_				     =   trigram_input
								a_				     =   use_bottleneck
								a_				     =   intra_bottleneck_size
								a_				     =   use_bottleneck_attention
								a_				     =   key_query_shared_bottleneck
								a_				     =   num_feedforward_networks
								a_				     =   normalization_type
								a_				     =   classifier_activation

								if self.use_bottleneck:
														a_				     =   intra_bottleneck_size
								else:
														a_				     =   hidden_size

								a_				     =   classifier_dropout





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		@property
		def _lowerCAmelCase     (    self:    Optional[int])  ->Mapping[str, Mapping[int, str]]:






								'''simple docstring'''


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



