

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
else:
  from .modeling_text_unet import UNetFlatConditionModel
  from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
  from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
  from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
  from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline








from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






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






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



