"""simple docstring"""






import os

import pytest
from attr import dataclass


lowerCAmelCase__  :  Any						     =	'us-east-1'  # defaults region
@dataclass
class 				snake_case       :




					"""simple docstring"""

					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": 5_00,
					    "save_steps": 55_00,
					}
					snake_case__				    = {**hyperparameters, "max_steps": 10_00}







					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									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 		__lowerCAmelCase						(				self   :	List[str]			):
									return f'''{self.framework}-transfromers-test'''







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return f'''./tests/sagemaker/scripts/{self.framework}'''







					@property
					def 		__lowerCAmelCase						(				self   :	List[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_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							SageMakerTestEnvironment(framework=request.cls.framework       )










from typing import TYPE_CHECKING

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


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         '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

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

