


'''simple docstring'''


import os

import pytest
from attr import dataclass


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



@dataclass
class 	__lowerCAmelCase		:


  '''simple docstring'''







  a_  					=							42
  a_  					=							"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
  a_  					=							{
      """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_  					=							{**hyperparameters, """max_steps""": 1_000}






  @property
  def        _a							(							self :       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        _a							(							self :       Any ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return f'./tests/sagemaker/scripts/{self.framework}'






  @property
  def        _a							(							self :       List[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					(      lowerCamelCase  :      Union[str, Any]):
      A_						:  Dict          =				SageMakerTestEnvironment(framework=request.cls.framework)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
