





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     SCREAMING_SNAKE_CASE__ (						a__      ):
 __SCREAMING_SNAKE_CASE							       =	['''image_processor''', '''tokenizer''']
 __SCREAMING_SNAKE_CASE							       =	'''BlipImageProcessor'''
 __SCREAMING_SNAKE_CASE							       =	('''BertTokenizer''', '''BertTokenizerFast''')



 def __init__(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						False
        super().__init__(_UpperCAmelCase,_UpperCAmelCase )
        A__											=						self.image_processor



 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 either images or text.''' )

        # Get only text
        if images is None:
               A__											=						self.tokenizer
               A__											=						self.tokenizer(
                   text=_UpperCAmelCase,add_special_tokens=_UpperCAmelCase,padding=_UpperCAmelCase,truncation=_UpperCAmelCase,max_length=_UpperCAmelCase,stride=_UpperCAmelCase,pad_to_multiple_of=_UpperCAmelCase,return_attention_mask=_UpperCAmelCase,return_overflowing_tokens=_UpperCAmelCase,return_special_tokens_mask=_UpperCAmelCase,return_offsets_mapping=_UpperCAmelCase,return_token_type_ids=_UpperCAmelCase,return_length=_UpperCAmelCase,verbose=_UpperCAmelCase,return_tensors=_UpperCAmelCase,**_UpperCAmelCase,)
               return text_encoding

        # add pixel_values
        A__											=						self.image_processor(_UpperCAmelCase,return_tensors=_UpperCAmelCase )

        if text is not None:
               A__											=						self.tokenizer(
                   text=_UpperCAmelCase,add_special_tokens=_UpperCAmelCase,padding=_UpperCAmelCase,truncation=_UpperCAmelCase,max_length=_UpperCAmelCase,stride=_UpperCAmelCase,pad_to_multiple_of=_UpperCAmelCase,return_attention_mask=_UpperCAmelCase,return_overflowing_tokens=_UpperCAmelCase,return_special_tokens_mask=_UpperCAmelCase,return_offsets_mapping=_UpperCAmelCase,return_token_type_ids=_UpperCAmelCase,return_length=_UpperCAmelCase,verbose=_UpperCAmelCase,return_tensors=_UpperCAmelCase,**_UpperCAmelCase,)
        else:
               A__											=						None

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

        return encoding_image_processor



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.tokenizer.batch_decode(*_UpperCAmelCase,**_UpperCAmelCase )



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.tokenizer.decode(*_UpperCAmelCase,**_UpperCAmelCase )




 @property
 def 				UpperCamelCase			(							self ):
        A__											=						self.tokenizer.model_input_names
        A__											=						self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )












'''simple docstring'''




lowercase__    :      Dict						=    {
    '''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''',
}