'''simple docstring'''






import os

import pytest
from attr import dataclass


_UpperCAmelCase    :     Optional[Any]         =       '''us-east-1'''  # defaults region







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   "arn:aws:iam::558105141721:role/sagemaker_execution_role"
   __lowerCAmelCase         =   {
       "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,
   }
   __lowerCAmelCase         =   {**hyperparameters, "max_steps": 1_0_0_0}
   @property
   def 	__UpperCAmelCase   (      self      :			List[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 	__UpperCAmelCase   (      self      :			List[str]     )  ->			str:
        return f'{self.framework}-transfromers-test'
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			str:
        return f'./tests/sagemaker/scripts/{self.framework}'

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  SageMakerTestEnvironment(framework=request.cls.framework				)




'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     List[Any]         =       {
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2TokenizerFast''']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       ['''LayoutLMv2FeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2ImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       [
       '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LayoutLMv2ForQuestionAnswering''',
       '''LayoutLMv2ForSequenceClassification''',
       '''LayoutLMv2ForTokenClassification''',
       '''LayoutLMv2Layer''',
       '''LayoutLMv2Model''',
       '''LayoutLMv2PreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

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



