
import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case_      :					str				      =     TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=lowercase		)
class 		__lowerCamelCase     (   abc.ABC		):
							# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
							lowerCamelCase__:				str
							lowerCamelCase__:				ClassVar[Features]
							lowerCamelCase__:				ClassVar[Features]



							def     A__ (     self	,				__snake_case							)						->     T:






								"""simple docstring"""



								return copy.deepcopy(self							)



							@property
							def     A__ (     self							)						->     Features:






								"""simple docstring"""



								return Features(**self.input_schema	,				**self.label_schema							)



							@property
							@abc.abstractmethod
							def     A__ (     self							)						->     Dict[str, str]:






								"""simple docstring"""



								raise NotImplementedError




							@classmethod
							def     A__ (     cls	,				__snake_case							)						->     T:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {f.name for f in dataclasses.fields(cls							)}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}							)


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





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     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:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     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     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



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



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



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




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
