



'''simple docstring'''
import os

import pytest
from attr import dataclass


snake_case_ :	Union[str, Any]         =  'us-east-1'  # defaults region






@dataclass
class 							lowercase__	:
	lowercase__  				=			42
	lowercase__  				=			"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
	lowercase__  				=			{
	    """task_name""": """mnli""",
	    """per_device_train_batch_size""": 16,
	    """per_device_eval_batch_size""": 16,
	    """do_train""": True,
	    """do_eval""": True,
	    """do_predict""": True,
	    """output_dir""": """/opt/ml/model""",
	    """overwrite_output_dir""": True,
	    """max_steps""": 5_00,
	    """save_steps""": 55_00,
	}
	lowercase__  				=			{**hyperparameters, """max_steps""": 10_00}





	@property
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							if self.framework == "pytorch":
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
													    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
													]
							else:
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
													    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
													]





	@property
	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							return F'{self.framework}-transfromers-test'





	@property
	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							return F'./tests/sagemaker/scripts/{self.framework}'





	@property
	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							if self.framework == "pytorch":
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
							else:
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"






@pytest.fixture(scope='class'							)
def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Any 				=   SageMakerTestEnvironment(framework=request.cls.framework							)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

				UpperCAmelCase						:   str			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




