
'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = "The quick brown fox jumps over the lazy dog"			,				)       ->	bool:
	_snake_case   						=		set()

	# Replace all the whitespace in our sentence
	_snake_case   						=		input_str.replace(' '			,				''	)
	for alpha in input_str:
		if "a" <= alpha.lower() <= "z":
			frequency.add(alpha.lower()	)
	return len(__A	) == 26







def 				SCREAMING_SNAKE_CASE__	(   __A = "The quick brown fox jumps over the lazy dog"			,				)       ->	bool:
	_snake_case   						=		[False] * 26
	for char in input_str:
		if char.islower():
			_snake_case   						=		True
		elif char.isupper():
			_snake_case   						=		True
	return all(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A = "The quick brown fox jumps over the lazy dog"			,				)       ->	bool:
	return len({char for char in input_str.lower() if char.isalpha()}	) == 26







def 				SCREAMING_SNAKE_CASE__	(   )       ->	None:
	from timeit import timeit

	_snake_case   						=		'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
	print(timeit('is_pangram()'			,				setup=__A	)	)
	print(timeit('is_pangram_faster()'			,				setup=__A	)	)
	print(timeit('is_pangram_fastest()'			,				setup=__A	)	)
	# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
	# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()



'''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

