
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  None
	_A			=  None
	_A			=  None
	_A			=  None




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Optional[Any]  ,     a	:List[str]=1  ,     a	:Dict=0  ,     a	:Dict=2  ,     a	:List[str]=5_1_2  ,     a	:Optional[int]="cls"  ,     a	:Union[str, Any]=False  ,     a	:str=True  ,     **a	:int  ,     )	->				List[str]:
								super().__init__(pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     **a		)
								__UpperCamelCase			:     Optional[Any]         =      project_dim
								__UpperCamelCase			:     List[str]         =      pooler_fn
								__UpperCamelCase			:     Any         =      learn_encoder
								__UpperCamelCase			:     Dict         =      use_attention_mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  [R'pooler', R'logit_scale']
	_A			=  [R'position_ids', R'predictions.decoder.bias']
	_A			=  'roberta'
	_A			=  RobertaSeriesConfig







	def __init__(      self	:List[Any]  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__(a		)
								__UpperCamelCase			:     Optional[int]         =      XLMRobertaModel(a		)
								__UpperCamelCase			:     Dict         =      nn.Linear(config.hidden_size  ,     config.project_dim		)
								__UpperCamelCase			:     Any         =      getattr(a  ,     "has_pre_transformation"  ,     a		)
								if self.has_pre_transformation:
															__UpperCamelCase			:     str         =      nn.Linear(config.hidden_size  ,     config.project_dim		)
															__UpperCamelCase			:     List[Any]         =      nn.LayerNorm(config.hidden_size  ,     eps=config.layer_norm_eps		)
								self.post_init()







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[bool] = None  ,     a	:Optional[bool] = None  ,     a	:Optional[bool] = None  ,     )	->				Dict:
								__UpperCamelCase			:     Tuple         =      return_dict if return_dict is not None else self.config.use_return_dict

								__UpperCamelCase			:     Any         =      self.base_model(
								    input_ids=a  ,     attention_mask=a  ,     token_type_ids=a  ,     position_ids=a  ,     head_mask=a  ,     inputs_embeds=a  ,     encoder_hidden_states=a  ,     encoder_attention_mask=a  ,     output_attentions=a  ,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states  ,     return_dict=a  ,     )

								if self.has_pre_transformation:
															__UpperCamelCase			:     List[Any]         =      outputs["hidden_states"][-2]
															__UpperCamelCase			:     Tuple         =      self.pre_LN(a		)
															__UpperCamelCase			:     Optional[int]         =      self.transformation_pre(a		)

															return TransformationModelOutput(
															    projection_state=a  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )
								else:
															__UpperCamelCase			:     Tuple         =      self.transformation(outputs.last_hidden_state		)
															return TransformationModelOutput(
															    projection_state=a  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)