




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 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					["""image_processor""", """tokenizer"""]
						UpperCamelCase     						=					"""FlavaImageProcessor"""
						UpperCamelCase     						=					("""BertTokenizer""", """BertTokenizerFast""")

						def __init__(		self   :Optional[Any]    , __snake_case   :str=None    , __snake_case   :str=None    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=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    , )
														__magic_name__	:	str								=kwargs.pop("""feature_extractor""" )

										__magic_name__	:	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__(__snake_case    , __snake_case )
										__magic_name__	:	List[Any]								=self.image_processor

						def __call__(		self   :Tuple    , __snake_case   :Optional[ImageInput] = None    , __snake_case   :Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None    , __snake_case   :bool = True    , __snake_case   :Union[bool, str, PaddingStrategy] = False    , __snake_case   :Union[bool, str, TruncationStrategy] = False    , __snake_case   :Optional[int] = None    , __snake_case   :int = 0    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = True    , __snake_case   :Optional[Union[str, TensorType]] = None    , **__snake_case   :Tuple    , ):
										'''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:
														__magic_name__	:	Any								=self.tokenizer(
														    text=__snake_case    , add_special_tokens=__snake_case    , padding=__snake_case    , truncation=__snake_case    , max_length=__snake_case    , stride=__snake_case    , pad_to_multiple_of=__snake_case    , return_token_type_ids=__snake_case    , return_attention_mask=__snake_case    , return_overflowing_tokens=__snake_case    , return_special_tokens_mask=__snake_case    , return_offsets_mapping=__snake_case    , return_length=__snake_case    , verbose=__snake_case    , return_tensors=__snake_case    , **__snake_case    , )
										if images is not None:
														__magic_name__	:	List[Any]								=self.image_processor(
														    __snake_case    , return_image_mask=__snake_case    , return_codebook_pixels=__snake_case    , return_tensors=__snake_case    , **__snake_case    , )

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

						def     A__						(		self   :List[str]    , *__snake_case   :Any    , **__snake_case   :Any ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[int]    , *__snake_case   :List[Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

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

						@property
						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	str								=self.tokenizer.model_input_names
										__magic_name__	:	Union[str, Any]								=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )

						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										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     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    , __snake_case    , )
										return self.image_processor










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




