'''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 			__lowercase		(						a__						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE__		   =						'''BlipImageProcessor'''
     SCREAMING_SNAKE_CASE__		   =						'''AutoTokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_A     ,			_A    )

         # add QFormer tokenizer
         __UpperCamelCase	:	str		    =  qformer_tokenizer







     def __call__(		self     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 0     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False     ,			_lowerCamelCase = False     ,			_lowerCamelCase = False     ,			_lowerCamelCase = False     ,			_lowerCamelCase = False     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):

         if images is None and text is None:
             raise ValueError('You have to specify at least images or text.'    )

         __UpperCamelCase	:	List[str]		    =  BatchFeature()

         if text is not None:
             __UpperCamelCase	:	Union[str, Any]		    =  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    )
             __UpperCamelCase	:	Any		    =  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     ,			)
             __UpperCamelCase	:	List[str]		    =  qformer_text_encoding.pop('input_ids'    )
             __UpperCamelCase	:	Optional[int]		    =  qformer_text_encoding.pop('attention_mask'    )

         if images is not None:
             __UpperCamelCase	:	Optional[Any]		    =  self.image_processor(_A     ,			return_tensors=_A    )
             encoding.update(_A    )

         return encoding







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_A     ,			**_A    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_A     ,			**_A    )







     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.tokenizer.model_input_names
         __UpperCamelCase	:	str		    =  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):

         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    )
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(_A     ,			'qformer_tokenizer'    )
         self.qformer_tokenizer.save_pretrained(_A    )
         return super().save_pretrained(_A     ,			**_A    )







     @classmethod
     def lowerCAmelCase     (		cls     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  AutoTokenizer.from_pretrained(_A     ,			subfolder='qformer_tokenizer'    )
         __UpperCamelCase	:	Dict		    =  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_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



