

from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()

			parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

			parser.add_argument(
			    '--txt2img_unclip',
			    default='kakaobrain/karlo-v1-alpha',
			    type=str,
			    required=False,
			    help='The pretrained txt2img unclip.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			a_      =     CLIPImageProcessor()
			a_      =     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

			a_      =     UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)