





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       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     ["""image_processor""", """tokenizer"""]
       lowerCAmelCase__      		=     """BlipImageProcessor"""
       lowerCAmelCase__      		=     ("""BertTokenizer""", """BertTokenizerFast""")




       def __init__(  self :    Dict							,				A :    Tuple							,				A :    Tuple		):
         __snake_case:						Tuple   =						False
         super().__init__(A							,				A		)
         __snake_case:						List[Any]   =						self.image_processor




       def __call__(  self :    Dict							,				A :    ImageInput = None							,				A :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,				A :    bool = True							,				A :    Union[bool, str, PaddingStrategy] = False							,				A :    Union[bool, str, TruncationStrategy] = None							,				A :    Optional[int] = None							,				A :    int = 0							,				A :    Optional[int] = None							,				A :    Optional[bool] = None							,				A :    bool = False							,				A :    bool = False							,				A :    bool = False							,				A :    bool = False							,				A :    bool = False							,				A :    bool = True							,				A :    Optional[Union[str, TensorType]] = None							,				**A :    Dict							,				):
         if images is None and text is None:
           raise ValueError("""You have to specify either images or text."""		)

         # Get only text
         if images is None:
           __snake_case:						Any   =						self.tokenizer
           __snake_case:						str   =						self.tokenizer(
               text=A							,				add_special_tokens=A							,				padding=A							,				truncation=A							,				max_length=A							,				stride=A							,				pad_to_multiple_of=A							,				return_attention_mask=A							,				return_overflowing_tokens=A							,				return_special_tokens_mask=A							,				return_offsets_mapping=A							,				return_token_type_ids=A							,				return_length=A							,				verbose=A							,				return_tensors=A							,				**A							,				)
           return text_encoding

         # add pixel_values
         __snake_case:						Optional[Any]   =						self.image_processor(A							,				return_tensors=A		)

         if text is not None:
           __snake_case:						List[str]   =						self.tokenizer(
               text=A							,				add_special_tokens=A							,				padding=A							,				truncation=A							,				max_length=A							,				stride=A							,				pad_to_multiple_of=A							,				return_attention_mask=A							,				return_overflowing_tokens=A							,				return_special_tokens_mask=A							,				return_offsets_mapping=A							,				return_token_type_ids=A							,				return_length=A							,				verbose=A							,				return_tensors=A							,				**A							,				)
         else:
           __snake_case:						Dict   =						None

         if text_encoding is not None:
           encoding_image_processor.update(A		)

         return encoding_image_processor




       def 							UpperCAmelCase__ (  self :    Any							,				*A :    Dict							,				**A :    Union[str, Any]		):
         return self.tokenizer.batch_decode(*A							,				**A		)




       def 							UpperCAmelCase__ (  self :    int							,				*A :    Optional[int]							,				**A :    int		):
         return self.tokenizer.decode(*A							,				**A		)






       @property
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						self.tokenizer.model_input_names
         __snake_case:						Union[str, Any]   =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






