



"""simple docstring"""

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  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase     :    Optional[Any]	      =	'''BlipImageProcessor'''
 _lowerCAmelCase     :    str	      =	'''AutoTokenizer'''

 def __init__(  self	: List[Any]						,  lowercase_	: Tuple						,  lowercase_	: str       ):
     snake_case_				:  List[Any]       =			False
     super().__init__(A__						,  A__       )
     snake_case_				:  Optional[Any]       =			self.image_processor

 def __call__(  self	: Optional[int]						,  lowercase_	: List[str] = None						,  lowercase_	: Any = None						,  lowercase_	: Dict = True						,  lowercase_	: Tuple = False						,  lowercase_	: List[str] = None						,  lowercase_	: str = None						,  lowercase_	: List[str] = 0						,  lowercase_	: Union[str, Any] = None						,  lowercase_	: Tuple = None						,  lowercase_	: Dict = False						,  lowercase_	: Optional[Any] = False						,  lowercase_	: Optional[Any] = False						,  lowercase_	: int = False						,  lowercase_	: Any = False						,  lowercase_	: List[str] = True						,  lowercase_	: Dict = None						,  **lowercase_	: List[Any]						,  ):
     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_				:  Optional[int]       =			self.tokenizer
         snake_case_				:  int       =			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_				:  Union[str, Any]       =			self.image_processor(A__						,  return_tensors=A__       )

     if text is not None:
         snake_case_				:  Tuple       =			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_				:  List[str]       =			None

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

     return encoding_image_processor

 def 		_snake_case      (  self	: Optional[Any]						,  *lowercase_	: str						,  **lowercase_	: Union[str, Any]       ):
     return self.tokenizer.batch_decode(*A__						,  **A__       )

 def 		_snake_case      (  self	: int						,  *lowercase_	: Tuple						,  **lowercase_	: Tuple       ):
     return self.tokenizer.decode(*A__						,  **A__       )






 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  str       =			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       )       )










"""simple docstring"""

lowercase__					:						Union[str, Any]												=			{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}





