



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 (			__lowerCamelCase					):
       UpperCamelCase__						:					List[Any]		      =		['''image_processor''', '''tokenizer''']
       UpperCamelCase__						:					str		      =		'''BlipImageProcessor'''
       UpperCamelCase__						:					int		      =		'''AutoTokenizer'''



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           super().__init__(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_)

           # add QFormer tokenizer
           __a		 =       qformer_tokenizer



       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  List[str] = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any] = False  ,	__SCREAMING_SNAKE_CASE							:  Any = None  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = 0  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = None  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = False  ,	__SCREAMING_SNAKE_CASE							:  List[str] = False  ,	__SCREAMING_SNAKE_CASE							:  int = False  ,	__SCREAMING_SNAKE_CASE							:  Any = False  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any] = False  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = True  ,	__SCREAMING_SNAKE_CASE							:  Dict = None  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

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

           __a		 =       BatchFeature()

           if text is not None:
               __a		 =       self.tokenizer(
                   text=SCREAMING_SNAKE_CASE_  ,	add_special_tokens=SCREAMING_SNAKE_CASE_  ,	padding=SCREAMING_SNAKE_CASE_  ,	truncation=SCREAMING_SNAKE_CASE_  ,	max_length=SCREAMING_SNAKE_CASE_  ,	stride=SCREAMING_SNAKE_CASE_  ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_  ,	return_attention_mask=SCREAMING_SNAKE_CASE_  ,	return_overflowing_tokens=SCREAMING_SNAKE_CASE_  ,	return_special_tokens_mask=SCREAMING_SNAKE_CASE_  ,	return_offsets_mapping=SCREAMING_SNAKE_CASE_  ,	return_token_type_ids=SCREAMING_SNAKE_CASE_  ,	return_length=SCREAMING_SNAKE_CASE_  ,	verbose=SCREAMING_SNAKE_CASE_  ,	return_tensors=SCREAMING_SNAKE_CASE_  ,	**SCREAMING_SNAKE_CASE_  ,	)
               encoding.update(SCREAMING_SNAKE_CASE_)
               __a		 =       self.qformer_tokenizer(
                   text=SCREAMING_SNAKE_CASE_  ,	add_special_tokens=SCREAMING_SNAKE_CASE_  ,	padding=SCREAMING_SNAKE_CASE_  ,	truncation=SCREAMING_SNAKE_CASE_  ,	max_length=SCREAMING_SNAKE_CASE_  ,	stride=SCREAMING_SNAKE_CASE_  ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_  ,	return_attention_mask=SCREAMING_SNAKE_CASE_  ,	return_overflowing_tokens=SCREAMING_SNAKE_CASE_  ,	return_special_tokens_mask=SCREAMING_SNAKE_CASE_  ,	return_offsets_mapping=SCREAMING_SNAKE_CASE_  ,	return_token_type_ids=SCREAMING_SNAKE_CASE_  ,	return_length=SCREAMING_SNAKE_CASE_  ,	verbose=SCREAMING_SNAKE_CASE_  ,	return_tensors=SCREAMING_SNAKE_CASE_  ,	**SCREAMING_SNAKE_CASE_  ,	)
               __a		 =       qformer_text_encoding.pop('''input_ids''')
               __a		 =       qformer_text_encoding.pop('''attention_mask''')

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

           return encoding



       def 	_lowerCamelCase    (	self							:  List[str]  ,	*__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	**__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Tuple  ,	*__SCREAMING_SNAKE_CASE							:  str  ,	**__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

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



       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''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))



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           if os.path.isfile(SCREAMING_SNAKE_CASE_):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(SCREAMING_SNAKE_CASE_  ,	exist_ok=SCREAMING_SNAKE_CASE_)
           __a		 =       os.path.join(SCREAMING_SNAKE_CASE_  ,	'''qformer_tokenizer''')
           self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_)
           return super().save_pretrained(SCREAMING_SNAKE_CASE_  ,	**SCREAMING_SNAKE_CASE_)





       @classmethod
       def 	_lowerCamelCase    (	cls							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	**__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  ,	subfolder='''qformer_tokenizer''')
           __a		 =       cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE_  ,	**SCREAMING_SNAKE_CASE_)
           args.append(SCREAMING_SNAKE_CASE_)
           return cls(*SCREAMING_SNAKE_CASE_)










from typing import TYPE_CHECKING

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


__snake_case		:Union[str, Any]   	=  {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[str]   	=  ['''ViTFeatureExtractor''']
 __snake_case		:Optional[Any]   	=  ['''ViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:str   	=  [
     '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTForImageClassification''',
     '''ViTForMaskedImageModeling''',
     '''ViTModel''',
     '''ViTPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''TFViTForImageClassification''',
     '''TFViTModel''',
     '''TFViTPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''FlaxViTForImageClassification''',
     '''FlaxViTModel''',
     '''FlaxViTPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 __snake_case		:Optional[int]   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





