"""simple docstring"""







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 	UpperCamelCase    (					lowercase_		):
				UpperCAmelCase       :   Union[str, Any]					=	["""image_processor""", """tokenizer"""]
				UpperCAmelCase       :   Any					=	"""ViltImageProcessor"""
				UpperCAmelCase       :   int					=	("""BertTokenizer""", """BertTokenizerFast""")



				def __init__(self       :   List[str]			,						_A       :   Dict=None			,						_A       :   Optional[int]=None			,						**_A       :   List[str])					->			Union[str, Any]:
					__snake_case    :							Any   	=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
						    ' instead.'			,						lowerCamelCase_			,						)
						__snake_case    :							Union[str, Any]   	=				kwargs.pop('feature_extractor')

					__snake_case    :							List[str]   	=				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__(lowerCamelCase_			,						lowerCamelCase_)
					__snake_case    :							Dict   	=				self.image_processor



				def __call__(self       :   List[str]			,						_A       :   Dict			,						_A       :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						_A       :   bool = True			,						_A       :   Union[bool, str, PaddingStrategy] = False			,						_A       :   Union[bool, str, TruncationStrategy] = None			,						_A       :   Optional[int] = None			,						_A       :   int = 0			,						_A       :   Optional[int] = None			,						_A       :   Optional[bool] = None			,						_A       :   Optional[bool] = None			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = True			,						_A       :   Optional[Union[str, TensorType]] = None			,						**_A       :   Union[str, Any]			,						)					->			Any:
					__snake_case    :							List[str]   	=				self.tokenizer(
					    text=lowerCamelCase_			,						add_special_tokens=lowerCamelCase_			,						padding=lowerCamelCase_			,						truncation=lowerCamelCase_			,						max_length=lowerCamelCase_			,						stride=lowerCamelCase_			,						pad_to_multiple_of=lowerCamelCase_			,						return_token_type_ids=lowerCamelCase_			,						return_attention_mask=lowerCamelCase_			,						return_overflowing_tokens=lowerCamelCase_			,						return_special_tokens_mask=lowerCamelCase_			,						return_offsets_mapping=lowerCamelCase_			,						return_length=lowerCamelCase_			,						verbose=lowerCamelCase_			,						return_tensors=lowerCamelCase_			,						**lowerCamelCase_			,						)
					# add pixel_values + pixel_mask
					__snake_case    :							Dict   	=				self.image_processor(lowerCamelCase_			,						return_tensors=lowerCamelCase_)
					encoding.update(lowerCamelCase_)

					return encoding



				def       _lowercase  (self       :   Optional[Any]			,						*_A       :   Any			,						**_A       :   Optional[int])					->			List[str]:
					return self.tokenizer.batch_decode(*lowerCamelCase_			,						**lowerCamelCase_)



				def       _lowercase  (self       :   str			,						*_A       :   int			,						**_A       :   Any)					->			List[str]:
					return self.tokenizer.decode(*lowerCamelCase_			,						**lowerCamelCase_)



				@property
				def       _lowercase  (self       :   List[str])					->			List[str]:
					__snake_case    :							List[Any]   	=				self.tokenizer.model_input_names
					__snake_case    :							List[str]   	=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



				@property
				def       _lowercase  (self       :   Any)					->			Any:
					warnings.warn(
					    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,						lowerCamelCase_			,						)
					return self.image_processor_class






				@property
				def       _lowercase  (self       :   List[str])					->			Dict:
					warnings.warn(
					    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,						lowerCamelCase_			,						)
					return self.image_processor







"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser

class 	UpperCamelCase    (					lowercase		):



				@staticmethod
				@abstractmethod
				def       _lowercase  (_A       :   ArgumentParser)					->			Tuple:
					raise NotImplementedError()






				@abstractmethod
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					raise NotImplementedError()





