






__a      :       Tuple				  =   {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


__a      :       int				  =   {value: key for key, value in encode_dict.items()}



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict    )						->      str:





				"""simple docstring"""

				__A         =	""
				for letter in word.lower():
								if letter.isalpha() or letter == " ":
												encoded += encode_dict[letter]
								else:
												raise Exception("""encode() accepts only letters of the alphabet and spaces"""    )
				return encoded



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple    )						->      str:





				"""simple docstring"""

				if set(lowerCamelCase__    ) - {"A", "B", " "} != set():
								raise Exception("""decode() accepts only 'A', 'B' and spaces"""    )
				__A         =	""
				for word in coded.split():
								while len(lowerCamelCase__    ) != 0:
												decoded += decode_dict[word[:5]]
												__A         =	word[5:]
								decoded += " "
				return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()












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




