"""simple docstring"""







import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE									=						'us-east-1'  # defaults region



@dataclass
class  _lowerCamelCase			:
				_snake_case     =  42
				_snake_case     =  """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
				_snake_case     =  {
				    """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,
				}
				_snake_case     =  {**hyperparameters, """max_steps""": 1_000}







				@property
				def 							__UpperCAmelCase					( self    :       Any						):

					"""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    :       Optional[int]						):

					"""simple docstring"""






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







				@property
				def 							__UpperCAmelCase					( self    :       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      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							SageMakerTestEnvironment(framework=request.cls.framework			)




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

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



