

'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline

class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      def 						__UpperCAmelCase      (       self			:		List[Any]  ,       snake_case			:		Optional[Any]=None  ,       snake_case			:		str=None  ,       snake_case			:		Dict=None  ,       **snake_case			:		Optional[int] ):



          """simple docstring"""





          if tokenize_kwargs is None:
              _snake_case			:			List[Any]				       =		{}

          if truncation is not None:
              if "truncation" in tokenize_kwargs:
                  raise ValueError(
                      'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
              _snake_case			:			Dict				       =		truncation

          _snake_case			:			Dict				       =		tokenize_kwargs

          _snake_case			:			Tuple				       =		{}
          if return_tensors is not None:
              _snake_case			:			Optional[Any]				       =		return_tensors

          return preprocess_params, {}, postprocess_params




      def 						__UpperCAmelCase      (       self			:		Any  ,       snake_case			:		Optional[Any]  ,       **snake_case			:		Tuple ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		self.framework
          _snake_case			:			Union[str, Any]				       =		self.tokenizer(_lowercase  ,       return_tensors=_lowercase  ,       **_lowercase )
          return model_inputs




      def 						__UpperCAmelCase      (       self			:		List[str]  ,       snake_case			:		str ):



          """simple docstring"""





          _snake_case			:			str				       =		self.model(**_lowercase )
          return model_outputs




      def 						__UpperCAmelCase      (       self			:		Optional[int]  ,       snake_case			:		Dict  ,       snake_case			:		Optional[Any]=False ):



          """simple docstring"""





          if return_tensors:
              return model_outputs[0]
          if self.framework == "pt":
              return model_outputs[0].tolist()
          elif self.framework == "tf":
              return model_outputs[0].numpy().tolist()




      def __call__(       self			:		Dict  ,       *snake_case			:		Union[str, Any]  ,       **snake_case			:		int ):



          """simple docstring"""





          return super().__call__(*_lowercase  ,       **_lowercase )





'''simple docstring'''



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


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
