






def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if not head:
		return True
	# split the list to two parts
	__lowercase							,    __lowercase   							=	head.next, head
	while fast and fast.next:
		__lowercase   							=	fast.next.next
		__lowercase   							=	slow.next
	__lowercase   							=	slow.next
	__lowercase   							=	None  # Don't forget here! But forget still works!
	# reverse the second part
	__lowercase   							=	None
	while second:
		__lowercase   							=	second.next
		__lowercase   							=	node
		__lowercase   							=	second
		__lowercase   							=	nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		__lowercase   							=	node.next
		__lowercase   							=	head.next
	return True





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	__lowercase   							=	__lowercase   							=	__lowercase   							=	head
	while fast and fast.next:
		__lowercase							,    __lowercase   							=	fast.next.next, slow.next

	# 2. Push the second half into the stack
	__lowercase   							=	[slow.val]
	while slow.next:
		__lowercase   							=	slow.next
		stack.append(slow.val       )

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		__lowercase   							=	cur.next

	return True





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if not head or not head.next:
		return True
	__lowercase   							=	{}
	__lowercase   							=	0
	while head:
		if head.val in d:
			d[head.val].append(lowerCamelCase       )
		else:
			__lowercase   							=	[pos]
		__lowercase   							=	head.next
		pos += 1
	__lowercase   							=	pos - 1
	__lowercase   							=	0
	for v in d.values():
		if len(lowerCamelCase       ) % 2 != 0:
			middle += 1
		else:
			__lowercase   							=	0
			for i in range(0					,	len(lowerCamelCase       )       ):
				if v[i] + v[len(lowerCamelCase       ) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					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.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					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)





