


'''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_					):
	A_				=    ["image_processor", "tokenizer"]
	A_				=    "BlipImageProcessor"
	A_				=    ("BertTokenizer", "BertTokenizerFast")




	def __init__(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[str]					    =  False
						super().__init__(__A						,				__A							)
						__a			:       Dict					    =  self.image_processor




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

						'''simple docstring'''





						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			:       Optional[int]					    =  self.tokenizer
											__a			:       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						,				)
											return text_encoding

						# add pixel_values
						__a			:       int					    =  self.image_processor(__A						,				return_tensors=__A							)

						if text is not None:
											__a			:       Optional[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						,				)
						else:
											__a			:       List[str]					    =  None

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

						return encoding_image_processor




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  self.tokenizer.model_input_names
						__a			:       str					    =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







from typing import TYPE_CHECKING

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


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

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



