





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					None




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						str			  =						self.feature_extraction_class(**self.feat_extract_dict		)
         __snake_case:						List[str]			  =						json.loads(feat_extract.to_json_string()		)
         for key, value in self.feat_extract_dict.items():
           self.assertEqual(obj[key]							,    A		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						List[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Any			  =						os.path.join(A							,    """feat_extract.json"""		)
           feat_extract_first.to_json_file(A		)
           __snake_case:						int			  =						self.feature_extraction_class.from_json_file(A		)

         self.assertEqual(feat_extract_second.to_dict()							,    feat_extract_first.to_dict()		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						Optional[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						List[str]			  =						feat_extract_first.save_pretrained(A		)[0]
           check_json_file_has_correct_format(A		)
           __snake_case:						int			  =						self.feature_extraction_class.from_pretrained(A		)

         self.assertEqual(feat_extract_second.to_dict()							,    feat_extract_first.to_dict()		)






       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Any			  =						self.feature_extraction_class()
         self.assertIsNotNone(A		)













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






