code: 
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     int							      =				['''image_processor''', '''tokenizer''']
  						UpperCamelCase			:     List[str]							      =				'''EfficientNetImageProcessor'''
  						UpperCamelCase			:     Union[str, Any]							      =				('''BertTokenizer''', '''BertTokenizerFast''')
  
  
  
  
  
  						def __init__( self       :Any		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Dict    )     ->Any:
  										super().__init__(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  
  
  
  
  						def __call__( self       :List[Any]		,						lowerCAmelCase__       :Tuple=None		,						lowerCAmelCase__       :int=None		,						lowerCAmelCase__       :Dict="max_length"		,						lowerCAmelCase__       :Dict=64		,						lowerCAmelCase__       :Union[str, Any]=None		,						**lowerCAmelCase__       :Optional[int]    )     ->List[Any]:
  										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:
  														lowercase						    =			self.tokenizer(
  														    lowerCAmelCase__		,						padding=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						return_tensors=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  										if images is not None:
  														lowercase						    =			self.image_processor(lowerCAmelCase__		,						return_tensors=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  										if text is not None and images is not None:
  														lowercase						    =			image_features.pixel_values
  														return encoding
  										elif text is not None:
  														return encoding
  										else:
  														return BatchEncoding(data=dict(**lowerCAmelCase__    )		,						tensor_type=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						*lowerCAmelCase__       :Dict		,						**lowerCAmelCase__       :Tuple    )     ->Union[str, Any]:
  										return self.tokenizer.batch_decode(*lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						*lowerCAmelCase__       :int		,						**lowerCAmelCase__       :List[Any]    )     ->List[Any]:
  										return self.tokenizer.decode(*lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						@property
  						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Optional[int]:
  										lowercase						    =			self.tokenizer.model_input_names
  										lowercase						    =			self.image_processor.model_input_names
  										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
  
style_context: 
  import sys
  from collections.abc import Mapping
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  
  
  
  class      UpperCamelCase_			(			TensorFormatter[Mapping, np.ndarray, Mapping]		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :int		,						lowerCAmelCase__       :Any=None		,						**lowerCAmelCase__       :Union[str, Any]    )     ->int:
  										super().__init__(features=lowerCAmelCase__    )
  										lowercase						    =			np_array_kwargs
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :str    )     ->Optional[Any]:
  										if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ):
  														if column and all(
  														    isinstance(lowerCAmelCase__		,						np.ndarray    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
  																		return np.stack(lowerCAmelCase__    )
  														else:
  																		# don't use np.array(column, dtype=object)
  																		# since it fails in certain cases
  																		# see https://stackoverflow.com/q/51005699
  																		lowercase						    =			np.empty(len(lowerCAmelCase__    )		,						dtype=lowerCAmelCase__    )
  																		lowercase						    =			column
  																		return out
  										return column
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :List[str]    )     ->Union[str, Any]:
  										if isinstance(lowerCAmelCase__		,						(str, bytes, type(lowerCAmelCase__    ))    ):
  														return value
  										elif isinstance(lowerCAmelCase__		,						(np.character, np.ndarray)    ) and np.issubdtype(value.dtype		,						np.character    ):
  														return value
  										elif isinstance(lowerCAmelCase__		,						np.number    ):
  														return value
  
  										lowercase						    =			{}
  
  										if isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.integer    ):
  														lowercase						    =			{"dtype": np.intaa}
  										elif isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.floating    ):
  														lowercase						    =			{"dtype": np.floataa}
  										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
  														import PIL.Image
  
  														if isinstance(lowerCAmelCase__		,						PIL.Image.Image    ):
  																		return np.asarray(lowerCAmelCase__		,						**self.np_array_kwargs    )
  
  										return np.asarray(lowerCAmelCase__		,						**{**default_dtype, **self.np_array_kwargs}    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :Dict    )     ->int:
  										# support for torch, tf, jax etc.
  										if config.TORCH_AVAILABLE and "torch" in sys.modules:
  														import torch
  
  														if isinstance(lowerCAmelCase__		,						torch.Tensor    ):
  																		return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
  										if hasattr(lowerCAmelCase__		,						"__array__"    ) and not isinstance(lowerCAmelCase__		,						(np.ndarray, np.character, np.number)    ):
  														lowercase						    =			data_struct.__array__()
  										# support for nested types like struct of list of struct
  										if isinstance(lowerCAmelCase__		,						np.ndarray    ):
  														if data_struct.dtype == object:
  																		return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
  										if isinstance(lowerCAmelCase__		,						(list, tuple)    ):
  														return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
  										return self._tensorize(lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :dict    )     ->str:
  										return map_nested(self._recursive_tensorize		,						lowerCAmelCase__		,						map_list=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
  										lowercase						    =			self.numpy_arrow_extractor().extract_row(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_row(lowerCAmelCase__    )
  										return self.recursive_tensorize(lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :pa.Table    )     ->np.ndarray:
  										lowercase						    =			self.numpy_arrow_extractor().extract_column(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_column(lowerCAmelCase__		,						pa_table.column_names[0]    )
  										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
  										lowercase						    =			self._consolidate(lowerCAmelCase__    )
  										return column
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
  										lowercase						    =			self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_batch(lowerCAmelCase__    )
  										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
  										for column_name in batch:
  														lowercase						    =			self._consolidate(batch[column_name]    )
  										return batch
  