'''simple docstring'''

import warnings

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    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_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    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_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

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

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    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   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									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






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor






'''simple docstring'''

import warnings

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    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_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    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_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

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

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    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   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									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






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





