






import os

import pytest
from attr import dataclass


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



@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    str
		_snake_case    :    List[Any]       						=  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
		_snake_case    :    Any       						=  {
		    '''task_name''': '''mnli''',
		    '''per_device_train_batch_size''': 1_6,
		    '''per_device_eval_batch_size''': 1_6,
		    '''do_train''': True,
		    '''do_eval''': True,
		    '''do_predict''': True,
		    '''output_dir''': '''/opt/ml/model''',
		    '''overwrite_output_dir''': True,
		    '''max_steps''': 5_0_0,
		    '''save_steps''': 5_5_0_0,
		}
		_snake_case    :    Tuple       						=  {**hyperparameters, '''max_steps''': 1_0_0_0}



		@property
		def 					__UpperCAmelCase			(							self   )	->							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 					__UpperCAmelCase			(							self   )	->							str:
							return f"{self.framework}-transfromers-test"



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							return f"./tests/sagemaker/scripts/{self.framework}"



		@property
		def 					__UpperCAmelCase			(							self   )	->							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      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						SageMakerTestEnvironment(framework=request.cls.framework  )










# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
