

"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :str				=       ['image_processor', 'tokenizer']
     _SCREAMING_SNAKE_CASE  :Optional[int]				=       'AutoImageProcessor'
     _SCREAMING_SNAKE_CASE  :Optional[int]				=       'AutoTokenizer'

     def __init__(  self		,			_a		,			_a     )     ->					Union[str, Any]:


           """simple docstring"""


           super().__init__(lowercase_		,			lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.image_processor

     def __call__(  self		,			_a=None		,			_a=None		,			_a=None		,			**_a     )     ->					List[str]:


           """simple docstring"""


           if text is None and images is None:
                 raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

           if text is not None:
                 SCREAMING_SNAKE_CASE__      :					Tuple											=		self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_     )

           if images is not None:
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_     )

           if text is not None and images is not None:
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image_features.pixel_values
                 return encoding
           elif text is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**lowercase_     )		,			tensor_type=lowercase_     )

     def     _a	(  self		,			*_a		,			**_a     )     ->					Any:


           """simple docstring"""


           return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_     )

     def     _a	(  self		,			*_a		,			**_a     )     ->					str:


           """simple docstring"""


           return self.tokenizer.decode(*lowercase_		,			**lowercase_     )





     @property
     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           return ["input_ids", "attention_mask", "pixel_values"]








import os

import pytest
from attr import dataclass


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




@dataclass
class A  :
      UpperCamelCase__							:  str
      UpperCamelCase__							:  Dict      ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
      UpperCamelCase__							:  int      ={
          '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': 5500,
      }
      UpperCamelCase__							:  Optional[Any]      ={**hyperparameters, 'max_steps': 1000}






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """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        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




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






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




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






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """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 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=SageMakerTestEnvironment(framework=request.cls.framework     )


