




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 __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  ['''image_processor''', '''tokenizer''']
   UpperCAmelCase__  :    Dict          =  '''FlavaImageProcessor'''
   UpperCAmelCase__  :    Dict          =  ('''BertTokenizer''', '''BertTokenizerFast''')







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=None					,						_snake_case		:						str=None					,						**_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							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					,						)
             UpperCAmelCase_          =							kwargs.pop('''feature_extractor''')

        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							self.image_processor







   def __call__(      self		:						List[Any]					,						_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		:						Any					,						):

        """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:
             UpperCAmelCase_          =							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:
             UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[Any]					,						**_snake_case		:						int):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Optional[int]					,						*_snake_case		:						int					,						**_snake_case		:						Dict):

        """simple docstring"""


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







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer.model_input_names
        UpperCAmelCase_          =							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







   @property
   def lowerCamelCase						(      self		:						str):

        """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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






