


'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				str									=		{
    """A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
    """H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
    """O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
    """V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
    """2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
    """8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
    """:""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
    """?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
    """(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE					:				int									=		{value: key for key, value in MORSE_CODE_DICT.items()}







def       a_				(	UpperCamelCase_ ):
							return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )







def       a_				(	UpperCamelCase_ ):
							return "".join(REVERSE_DICT[char] for char in message.split() )







def       a_				(	):
							A_			    =		"""Morse code here!"""
							print(UpperCamelCase_ )
							A_			    =		encrypt(UpperCamelCase_ )
							print(UpperCamelCase_ )
							A_			    =		decrypt(UpperCamelCase_ )
							print(UpperCamelCase_ )


if __name__ == "__main__":
							main()












"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



