

import warnings
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 					lowercase							(			_UpperCamelCase     ):
				__SCREAMING_SNAKE_CASE   :					int			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''FlavaImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(   self ,  snake_case=None ,  snake_case=None ,  **snake_case			):
											snake_case_     							=       None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.' ,  UpperCamelCase__ ,  )
																		snake_case_     							=       kwargs.pop('feature_extractor'			)

											snake_case_     							=       image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'			)
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'			)

											super().__init__(UpperCamelCase__ ,  UpperCamelCase__			)
											snake_case_     							=       self.image_processor




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

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(
																		    text=UpperCamelCase__ ,  add_special_tokens=UpperCamelCase__ ,  padding=UpperCamelCase__ ,  truncation=UpperCamelCase__ ,  max_length=UpperCamelCase__ ,  stride=UpperCamelCase__ ,  pad_to_multiple_of=UpperCamelCase__ ,  return_token_type_ids=UpperCamelCase__ ,  return_attention_mask=UpperCamelCase__ ,  return_overflowing_tokens=UpperCamelCase__ ,  return_special_tokens_mask=UpperCamelCase__ ,  return_offsets_mapping=UpperCamelCase__ ,  return_length=UpperCamelCase__ ,  verbose=UpperCamelCase__ ,  return_tensors=UpperCamelCase__ ,  **UpperCamelCase__ ,  )
											if images is not None:
																		snake_case_     							=       self.image_processor(
																		    UpperCamelCase__ ,  return_image_mask=UpperCamelCase__ ,  return_codebook_pixels=UpperCamelCase__ ,  return_tensors=UpperCamelCase__ ,  **UpperCamelCase__ ,  )

											if text is not None and images is not None:
																		encoding.update(UpperCamelCase__			)
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**UpperCamelCase__			) ,  tensor_type=UpperCamelCase__			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*UpperCamelCase__ ,  **UpperCamelCase__			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*UpperCamelCase__ ,  **UpperCamelCase__			)




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




				@property
				def        a				(   self			):
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,  UpperCamelCase__ ,  )
											return self.image_processor_class




				@property
				def        a				(   self			):
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,  UpperCamelCase__ ,  )
											return self.image_processor





'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     str         =       {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Any         =       ['''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

   _UpperCAmelCase    :     List[str]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



