




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""AutoFeatureExtractor"""
					_lowerCamelCase						    =					"""Speech2Text2Tokenizer"""

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				self.feature_extractor
						lowerCamelCase_				:						List[Any]				  =				False

					def __call__(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						if self._in_target_context_manager:
							return self.current_processor(*__lowercase					,			**__lowercase   )

						if "raw_speech" in kwargs:
							warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
							lowerCamelCase_				:						int				  =				kwargs.pop('raw_speech'   )
						else:
							lowerCamelCase_				:						Optional[Any]				  =				kwargs.pop('audio'					,			__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				kwargs.pop('sampling_rate'					,			__lowercase   )
						lowerCamelCase_				:						int				  =				kwargs.pop('text'					,			__lowercase   )
						if len(__lowercase   ) > 0:
							lowerCamelCase_				:						Optional[int]				  =				args[0]
							lowerCamelCase_				:						Any				  =				args[1:]

						if audio is None and text is None:
							raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

						if audio is not None:
							lowerCamelCase_				:						Any				  =				self.feature_extractor(__lowercase					,			*__lowercase					,			sampling_rate=__lowercase					,			**__lowercase   )
						if text is not None:
							lowerCamelCase_				:						List[str]				  =				self.tokenizer(__lowercase					,			**__lowercase   )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							lowerCamelCase_				:						Optional[int]				  =				encodings['input_ids']
							return inputs

					def       lowercase_				(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




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





					@contextmanager
					def       lowercase_				(							self   ):







						'''simple docstring'''




						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'   )
						lowerCamelCase_				:						Tuple				  =				True
						lowerCamelCase_				:						Optional[Any]				  =				self.tokenizer
						yield
						lowerCamelCase_				:						str				  =				self.feature_extractor
						lowerCamelCase_				:						Optional[int]				  =				False

import warnings

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __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."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    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       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           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       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

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

       @property
       def 			snake_case__    (							self					:     str			):
           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 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



