



from __future__ import annotations







def 				A			(		__UpperCAmelCase	)       ->       list[int]:



						'''simple docstring'''






						UpperCAmelCase_     							=     [0] * max_one

						for i in range(0							,					__UpperCAmelCase	):
												UpperCAmelCase_     							=     i

						for i in range(2							,					__UpperCAmelCase	):
												if totients[i] == i:
																		for j in range(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	):
																								totients[j] -= totients[j] // i

						return totients







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       bool:



						'''simple docstring'''






						return sorted(str(__UpperCAmelCase	)	) == sorted(str(__UpperCAmelCase	)	)







def 				A			(		__UpperCAmelCase = 1000_0000	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     1  # i
						UpperCAmelCase_     							=     0  # φ(i)
						UpperCAmelCase_     							=     get_totients(max_n + 1	)

						for i in range(2							,					max_n + 1	):
												UpperCAmelCase_     							=     totients[i]

												if i * min_denominator < min_numerator * t and has_same_digits(__UpperCAmelCase							,					__UpperCAmelCase	):
																		UpperCAmelCase_     							=     i
																		UpperCAmelCase_     							=     t

						return min_numerator


if __name__ == "__main__":
							print(f"{solution() = }")






lowerCamelCase__   = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase__   = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase__   = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




