




import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__a							     =					logging.getLogger(__name__)







def lowerCamelCase__  (     _lowercase=2					,		_lowercase=3					,		_lowercase=16					,		_lowercase = 10					,		_lowercase = 2 ):

     '''simple docstring'''


     def get_dataset(_lowercase ):
          UpperCAmelCase_     :				Optional[int]  =     torch.randn(batch_size * n_batches					,		1 )
          return TensorDataset(_lowercase					,		a * x + b + 0.1 * torch.randn(batch_size * n_batches					,		1 ) )

     UpperCAmelCase_     :				Tuple  =     get_dataset(_lowercase )
     UpperCAmelCase_     :				List[Any]  =     get_dataset(_lowercase )
     UpperCAmelCase_     :				Optional[int]  =     DataLoader(_lowercase					,		shuffle=_lowercase					,		batch_size=_lowercase					,		num_workers=4 )
     UpperCAmelCase_     :				int  =     DataLoader(_lowercase					,		shuffle=_lowercase					,		batch_size=_lowercase					,		num_workers=4 )
     return (train_dataloader, valid_dataloader)







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     []
     for epoch in range(_lowercase ):
          # Train quickly
          model.train()
          for batch in dataloader:
               UpperCAmelCase_,       UpperCAmelCase_     :				int  =     batch
               UpperCAmelCase_     :				List[Any]  =     model(_lowercase )
               UpperCAmelCase_     :				Dict  =     torch.nn.functional.mse_loss(_lowercase					,		_lowercase )
               accelerator.backward(_lowercase )
               optimizer.step()
               optimizer.zero_grad()
          rands.append(random.random() )  # Introduce some randomness
          if scheduler is not None:
               scheduler.step()
     return rands

class     __a(	nn.Module  ):






    """simple docstring"""





    def __init__( self    )					->						Dict:
         super().__init__()
         UpperCAmelCase_     :				List[str]  =     nn.Parameter(torch.randn(1    )    )
         UpperCAmelCase_     :				int  =     nn.Parameter(torch.randn(1    )    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         return x * self.a + self.b


class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Optional[int]:
         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42    )
              UpperCAmelCase_     :				Tuple  =     DummyModel()
              UpperCAmelCase_     :				str  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_,       UpperCAmelCase_     :				str  =     dummy_dataloaders()
              UpperCAmelCase_     :				Any  =     ProjectConfiguration(total_limit=1  ,project_dir=_SCREAMING_SNAKE_CASE  ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE    )
              # Train baseline
              UpperCAmelCase_     :				str  =     Accelerator(project_config=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				int  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save initial
              accelerator.save_state()

              # Save second state
              accelerator.save_state()
              self.assertEqual(len(os.listdir(accelerator.project_dir    )    )  ,1    )





    def 				a__ ( self    )					->						Any:
         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42    )
              UpperCAmelCase_     :				Optional[int]  =     DummyModel()
              UpperCAmelCase_     :				Union[str, Any]  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     dummy_dataloaders()
              # Train baseline
              UpperCAmelCase_     :				Union[str, Any]  =     Accelerator()
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				int  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save initial
              UpperCAmelCase_     :				Union[str, Any]  =     os.path.join(_SCREAMING_SNAKE_CASE  ,'''initial'''    )
              accelerator.save_state(_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Optional[Any]  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				List[Any]  =     optimizer.state_dict()
              UpperCAmelCase_     :				Optional[Any]  =     train(3  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				int  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				Tuple  =     optimizer.state_dict()

              # Train partially
              set_seed(42    )
              UpperCAmelCase_     :				Tuple  =     DummyModel()
              UpperCAmelCase_     :				Dict  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     dummy_dataloaders()
              UpperCAmelCase_     :				List[str]  =     Accelerator()
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              accelerator.load_state(_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Union[str, Any]  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				Any  =     optimizer.state_dict()
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				str  =     train(2  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save everything
              UpperCAmelCase_     :				Union[str, Any]  =     os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoint'''    )
              accelerator.save_state(_SCREAMING_SNAKE_CASE    )

              # Load everything back in and make sure all states work
              accelerator.load_state(_SCREAMING_SNAKE_CASE    )
              test_rands += train(1  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Union[str, Any]  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				Any  =     optimizer.state_dict()
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42    )
              UpperCAmelCase_     :				str  =     DummyModel()
              UpperCAmelCase_     :				List[str]  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_,       UpperCAmelCase_     :				int  =     dummy_dataloaders()
              UpperCAmelCase_     :				Union[str, Any]  =     ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE    )

              # Train baseline
              UpperCAmelCase_     :				Optional[int]  =     Accelerator(project_dir=_SCREAMING_SNAKE_CASE  ,project_config=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				str  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save initial
              accelerator.save_state()
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				int  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				List[Any]  =     optimizer.state_dict()
              UpperCAmelCase_     :				int  =     train(3  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Optional[int]  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				List[str]  =     optimizer.state_dict()

              # Train partially
              set_seed(42    )
              UpperCAmelCase_     :				Optional[int]  =     DummyModel()
              UpperCAmelCase_     :				str  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     dummy_dataloaders()
              UpperCAmelCase_     :				List[str]  =     ProjectConfiguration(iteration=1  ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				str  =     Accelerator(project_dir=_SCREAMING_SNAKE_CASE  ,project_config=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_0'''    )    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Dict  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				List[Any]  =     optimizer.state_dict()
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				Dict  =     train(2  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save everything
              accelerator.save_state()

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_1'''    )    )
              test_rands += train(1  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              ((UpperCAmelCase_),       (UpperCAmelCase_))     :				Optional[int]  =     model.a.item(), model.b.item()
              UpperCAmelCase_     :				str  =     optimizer.state_dict()
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				str  =     torch.tensor([1, 2, 3]    )
         UpperCAmelCase_     :				Any  =     torch.tensor([2, 3, 4]    )
         UpperCAmelCase_     :				int  =     DummyModel()
         UpperCAmelCase_     :				Tuple  =     torch.optim.Adam(net.parameters()    )
         UpperCAmelCase_     :				Optional[int]  =     Accelerator()
         with self.assertRaises(_SCREAMING_SNAKE_CASE    ) as ve:
              accelerator.register_for_checkpointing(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     str(ve.exception    )
         self.assertTrue('''Item at index 0''' in message    )
         self.assertTrue('''Item at index 1''' in message    )
         self.assertFalse('''Item at index 2''' in message    )
         self.assertFalse('''Item at index 3''' in message    )





    def 				a__ ( self    )					->						List[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42    )
              UpperCAmelCase_     :				Any  =     DummyModel()
              UpperCAmelCase_     :				Any  =     torch.optim.Adam(params=model.parameters()  ,lr=1e-3    )
              UpperCAmelCase_     :				Union[str, Any]  =     torch.optim.lr_scheduler.StepLR(_SCREAMING_SNAKE_CASE  ,step_size=1  ,gamma=0.99    )
              UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     dummy_dataloaders()
              UpperCAmelCase_     :				str  =     ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE    )
              # Train baseline
              UpperCAmelCase_     :				List[str]  =     Accelerator(project_dir=_SCREAMING_SNAKE_CASE  ,project_config=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     accelerator.prepare(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              # Save initial
              accelerator.save_state()
              UpperCAmelCase_     :				int  =     scheduler.state_dict()
              train(3  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              self.assertNotEqual(_SCREAMING_SNAKE_CASE  ,scheduler.state_dict()    )

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_0'''    )    )
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,scheduler.state_dict()    )





    def 				a__ ( self    )					->						str:
         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42    )
              UpperCAmelCase_     :				List[str]  =     DummyModel()
              UpperCAmelCase_     :				Dict  =     ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE  ,total_limit=2    )
              # Train baseline
              UpperCAmelCase_     :				Tuple  =     Accelerator(project_dir=_SCREAMING_SNAKE_CASE  ,project_config=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Union[str, Any]  =     accelerator.prepare(_SCREAMING_SNAKE_CASE    )
              # Save 3 states:
              for _ in range(11    ):
                   accelerator.save_state()
              self.assertTrue(not os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_0'''    )    )    )
              self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_9'''    )    )    )
              self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,'''checkpoints'''  ,'''checkpoint_10'''    )    )    )





    @require_cuda
    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[Any]  =     ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
         execute_subprocess_async(_SCREAMING_SNAKE_CASE  ,env=os.environ.copy()    )


if __name__ == "__main__":
     __a							     =					'/tmp/accelerate/state_checkpointing'
     __a							     =					DummyModel()
     __a							     =					torch.optim.Adam(params=model.parameters(), lr=1E-3)
     __a							     =					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
     __a     ,__a							     =					dummy_dataloaders()
     __a							     =					ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     __a							     =					Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
     if accelerator.process_index == 0:
          if os.path.exists(savedir):
               shutil.rmtree(savedir)
          os.makedirs(savedir)
     __a     ,__a     ,__a     ,__a     ,__a							     =					accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     __a     ,__a							     =					accelerator.prepare(model, optimizer)
     train(3, model, train_dataloader, optimizer, accelerator, scheduler)
     # Check that the intial optimizer is loaded on the GPU
     for group in optimizer.param_groups:
          __a							     =					group['params'][0].device
          break
     assert param_device.type == accelerator.device.type
     __a							     =					model.cpu()
     accelerator.wait_for_everyone()
     accelerator.save_state()
     accelerator.wait_for_everyone()

     # Check CPU state
     accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
     for group in optimizer.param_groups:
          __a							     =					group['params'][0].device
          break
     assert (
         param_device.type == torch.device('cpu').type
     ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

     # Check device state
     model.to(accelerator.device)
     accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
     for group in optimizer.param_groups:
          __a							     =					group['params'][0].device
          break
     assert (
         param_device.type == accelerator.device.type
     ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

     # Check error
     with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
          accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
     accelerator.wait_for_everyone()
     if accelerator.process_index == 0:
          shutil.rmtree(savedir)
     accelerator.wait_for_everyone()




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


A_						:    Any				=      logging.get_logger(__name__)

A_						:    Optional[int]				=      {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''xmod'''





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple=30_522     ,       _SCREAMING_SNAKE_CASE				:       Tuple=768     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Any="gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=512     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple=("en_XX",)     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]     ,       )						->	List[str]:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							str							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Tuple							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							int							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_norm
					SCREAMING_SNAKE_CASE						:							int							=     adapter_reduction_factor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     adapter_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     adapter_reuse_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     ln_before_adapter
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     default_language




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







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





					"""simple docstring"""


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





