
import os

import pytest
from attr import dataclass


__a  			=					'us-east-1'  # defaults region
@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:str
	a			:List[str]         =	'arn:aws:iam::558105141721:role/sagemaker_execution_role'
	a			:Optional[int]         =	{
	    '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': 500,
	    'save_steps': 5_500,
	}
	a			:Any         =	{**hyperparameters, 'max_steps': 1_000}
	@property
	def 						_lowercase  (       self  :			Any       )       ->     str:
					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 						_lowercase  (       self  :			Union[str, Any]       )       ->     str:
					return f'''{self.framework}-transfromers-test'''
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     str:
					return f'''./tests/sagemaker/scripts/{self.framework}'''




	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     str:
					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						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				lowercase_       =					SageMakerTestEnvironment(framework=request.cls.framework					)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




