




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__				):
     a		:      Any		 =     ["""image_processor""", """tokenizer"""]
     a		:      List[Any]		 =     """BlipImageProcessor"""
     a		:      Dict		 =     ("""BertTokenizer""", """BertTokenizerFast""")







     def __init__(     self  ,		A  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				False
          super().__init__(A  ,		A			)
          a     =				self.image_processor







     def __call__(     self  ,		A = None  ,		A = None  ,		A = True  ,		A = False  ,		A = None  ,		A = None  ,		A = 0  ,		A = None  ,		A = None  ,		A = False  ,		A = False  ,		A = False  ,		A = False  ,		A = False  ,		A = True  ,		A = None  ,		**A  ,		)  ->   BatchEncoding:



          '''simple docstring'''



          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:
               a     =				self.tokenizer
               a     =				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
          a     =				self.image_processor(A  ,		return_tensors=A			)

          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_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:
               a     =				None

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

          return encoding_image_processor







     def       lowerCAmelCase_    (     self  ,		*A  ,		**A			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		*A  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



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







lowercase__				:      Optional[int]	   = [
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

