"""simple docstring"""



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					['''image_processor''', '''tokenizer''']
    UpperCamelCase_	:					Union[str, Any]							=					'''BlipImageProcessor'''
    UpperCamelCase_	:					List[Any]							=					'''AutoTokenizer'''
    def __init__(		self							:     Optional[int]	,      A_							:     Any	,      A_							:     List[Any]	,      A_							:     Dict    )    ->       Dict:
         super().__init__(A_	,      A_    )

         # add QFormer tokenizer
         __snake_case				=							qformer_tokenizer
    def __call__(		self							:     str	,      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_							:     Tuple	,      )    ->       BatchFeature:
         if images is None and text is None:
              raise ValueError('''You have to specify at least images or text.'''    )

         __snake_case				=							BatchFeature()

         if text is not None:
              __snake_case				=							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_	,      )
              encoding.update(A_    )
              __snake_case				=							self.qformer_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_	,      )
              __snake_case				=							qformer_text_encoding.pop('''input_ids'''    )
              __snake_case				=							qformer_text_encoding.pop('''attention_mask'''    )

         if images is not None:
              __snake_case				=							self.image_processor(A_	,      return_tensors=A_    )
              encoding.update(A_    )

         return encoding
    def  lowercase					(		self							:     str	,      *A_							:     str	,      **A_							:     Optional[Any]    )    ->       str:
         return self.tokenizer.batch_decode(*A_	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      *A_							:     Any	,      **A_							:     int    )    ->       int:
         return self.tokenizer.decode(*A_	,      **A_    )
    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def  lowercase					(		self							:     Optional[Any]    )    ->       int:
         __snake_case				=							self.tokenizer.model_input_names
         __snake_case				=							self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     Optional[int]	,      **A_							:     str    )    ->       Union[str, Any]:
         if os.path.isfile(A_    ):
              raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"    )
         os.makedirs(A_	,      exist_ok=A_    )
         __snake_case				=							os.path.join(A_	,      '''qformer_tokenizer'''    )
         self.qformer_tokenizer.save_pretrained(A_    )
         return super().save_pretrained(A_	,      **A_    )







    @classmethod
    def  lowercase					(		cls							:     Dict	,      A_							:     str	,      **A_							:     int    )    ->       Union[str, Any]:
         __snake_case				=							AutoTokenizer.from_pretrained(A_	,      subfolder='''qformer_tokenizer'''    )
         __snake_case				=							cls._get_arguments_from_pretrained(A_	,      **A_    )
         args.append(A_    )
         return cls(*A_    )
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Dict														=							{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["VisionTextDualEncoderModel"]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["FlaxVisionTextDualEncoderModel"]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Any														=							["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
       from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
       from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
       import sys

       __lowercase		:		Tuple														=							_LazyModule(__name__, globals()["__file__"], _import_structure)