



'''simple docstring'''





import os

import pytest
from attr import dataclass


__a          =		"us-east-1"  # defaults region







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    lowercase					=		42
    lowercase					=		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
    lowercase					=		{
        "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,
    }
    lowercase					=		{**hyperparameters, "max_steps": 10_00}




    @property
    def lowerCamelCase       (    self					:     List[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 lowerCamelCase       (    self					:     Union[str, Any]						):
          return f"{self.framework}-transfromers-test"




    @property
    def lowerCamelCase       (    self					:     List[str]						):
          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 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      snake_case__  :    Dict    						=      SageMakerTestEnvironment(framework=request.cls.framework		)


import warnings

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







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

								super().__init__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''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:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class