






import warnings
from typing import List, Optional, Union

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





class     SCREAMING_SNAKE_CASE					(			snake_case       ):





							"""simple docstring"""

							A_    	=       ["image_processor", "tokenizer"]
							A_    	=       "FlavaImageProcessor"
							A_    	=       ("BertTokenizer", "BertTokenizerFast")

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

												_A										=			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__(a_		,  a_ )
												_A										=			self.image_processor

							def __call__(	self: Any		,  __A: Dict = None		,  __A: Optional[Any] = None		,  __A: Union[str, Any] = True		,  __A: Dict = False		,  __A: Union[str, Any] = False		,  __A: Union[str, Any] = None		,  __A: List[str] = 0		,  __A: Dict = None		,  __A: List[str] = None		,  __A: Union[str, Any] = None		,  __A: List[Any] = None		,  __A: List[str] = None		,  __A: List[str] = False		,  __A: Optional[Any] = False		,  __A: List[Any] = False		,  __A: List[str] = False		,  __A: Optional[int] = True		,  __A: Union[str, Any] = None		,  **__A: Optional[Any]		,  )     ->					Optional[int]:

												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:
																	_A										=			self.tokenizer(
																	    text=a_		,  add_special_tokens=a_		,  padding=a_		,  truncation=a_		,  max_length=a_		,  stride=a_		,  pad_to_multiple_of=a_		,  return_token_type_ids=a_		,  return_attention_mask=a_		,  return_overflowing_tokens=a_		,  return_special_tokens_mask=a_		,  return_offsets_mapping=a_		,  return_length=a_		,  verbose=a_		,  return_tensors=a_		,  **a_		,  )
												if images is not None:
																	_A										=			self.image_processor(
																	    a_		,  return_image_mask=a_		,  return_codebook_pixels=a_		,  return_tensors=a_		,  **a_		,  )

												if text is not None and images is not None:
																	encoding.update(a_ )
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**a_ )		,  tensor_type=a_ )

							def    __A			(	self: str		,  *__A: Any		,  **__A: Optional[Any] )     ->					Optional[Any]:
												return self.tokenizer.batch_decode(*a_		,  **a_ )

							def    __A			(	self: Union[str, Any]		,  *__A: Dict		,  **__A: List[Any] )     ->					Optional[int]:
												return self.tokenizer.decode(*a_		,  **a_ )

							@property
							def    __A			(	self: Dict )     ->					Tuple:
												_A										=			self.tokenizer.model_input_names
												_A										=			self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )

							@property
							def    __A			(	self: Dict )     ->					List[str]:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,  a_		,  )
												return self.image_processor_class



							@property
							def    __A			(	self: int )     ->					str:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,  a_		,  )
												return self.image_processor






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import _LazyModule


_a					:			Tuple							=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
  from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
  import sys

  _a					:			int							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


