As a helpful and smart assistant, your task is to parse a given text in any language to Universal Dependency (UD) format including POS tags and dependency relations (HEAD and DEPREL).
Key Components of UD Annotation:
1. Tokens: The fundamental units within UD are tokens. These can be individual words, punctuation marks, or even parts of multi-word expressions. Each token has several associated attributes.
2. UPOS Tags: The UPOS field in the CoNLL-U format represents the universal part-of-speech tag assigned to each word. This tag is part of a universal POS tag set designed to be applicable across different languages. The UPOS tags include categories such as NOUN, VERB, ADJ (adjective), ADV (adverb), PRON (pronoun), DET (determiner), and more. 
3. Dependency Relations: Dependency relations are the core of the syntactic representation. They form a tree structure over the tokens in a sentence. Here's how they work:
    - HEAD: This field indicates the head of the current word. The head is the word that the current word is connected to in the dependency parse tree. The value is an integer that corresponds to the ID of the head word within the same sentence. A value of zero (0) indicates that the word is the root of the sentence.
    - DEPREL: The DEPREL field specifies the type of dependency relation between the current word and its head (as specified in the HEAD field). The value is a universal dependency relation or a language-specific subtype of such a relation.
Note: Blank lines are used to mark the boundaries between sentences.
Example:
Let's analyze this sentence with its UD annotation:
"The big dog chased the cat."
1. Tokenization:
- The
- big
- dog
- chased
- the
- cat
- .
2. UPOS Tags:
- The: DET
- big: ADJ
- dog: NOUN
- chased: VERB
- the: DET
- cat: NOUN
- .: PUNCT
3. Head node (parent):
- The: the parent of this word is "dog" at position 3
- big: the parent of this word is "dog" at position 3
- dog: the parent of this word is "chased" at position 4
- chased: this is the root node, therefore its head is 0
- the: the parent of this word is "cat" at position 6
- cat: the parent of this word is "chased" at position 4
- .: the parent of this word is "chased" at position 4
3. Dependency Relations:
- root(ROOT, chased)
- det(dog, The)
- amod(dog, big)
- nsubj(chased, dog)
- det(cat, the)
- obj(chased, cat)
Combining the above annotations, the final output in UD format is:
Position	Tokenization	UPOS	Head	Deprel
1	The	DET	3	det
2	big	ADJ	3	amod
3	dog	NOUN	4	nsubj
4	chased	VERB	0	ROOT
5	the	DET	6	det
6	cat	NOUN	4	obj
7	.	PUNCT	4	punct
Generate UD annotations for the following tokenized text:
La misma foto de Aznar y Bush y las banderas de España y EEUU en la Casa Blanca que ocupó muchas portadas de diarios hace tres días, llevó a Haro Tecglen a escribir ayer en El País: "Podrá ser que el periodismo se haya convertido en una ciencia exacta y, realmente, la foto del día sea siempre una y trina.
Postion	Tokenization
1	La
2	misma
3	foto
4	de
5	Aznar
6	y
7	Bush
8	y
9	las
10	banderas
11	de
12	España
13	y
14	EEUU
15	en
16	la
17	Casa
18	Blanca
19	que
20	ocupó
21	muchas
22	portadas
23	de
24	diarios
25	hace
26	tres
27	días
28	,
29	llevó
30	a
31	Haro
32	Tecglen
33	a
34	escribir
35	ayer
36	en
37	El
38	País
39	:
40	"
41	Podrá
42	ser
43	que
44	el
45	periodismo
46	se
47	haya
48	convertido
49	en
50	una
51	ciencia
52	exacta
53	y
54	,
55	realmente
56	,
57	la
58	foto
59	del
60	día
61	sea
62	siempre
63	una
64	y
65	trina
66	.
Position	Tokenization	UPOS	Head	Deprel
1	La	DET	3	det
2	misma	DET	1	det
3	foto	NOUN	29	nsubj
4	de	ADP	5	case
5	Aznar	PROPN	3	nmod
6	y	CCONJ	7	cc
7	Bush	PROPN	5	conj
8	y	CCONJ	10	cc
9	las	DET	10	det
10	banderas	NOUN	5	conj
11	de	ADP	12	case
12	España	PROPN	10	nmod
13	y	CCONJ	14	cc
14	EEUU	PROPN	12	conj
15	en	ADP	17	case
16	la	DET	17	det
17	Casa	PROPN	3	nmod
18	Blanca	PROPN	17	flat
19	que	PRON	20	nsubj
20	ocupó	VERB	3	acl
21	muchas	DET	22	det
22	portadas	NOUN	20	obj
23	de	ADP	24	case
24	diarios	NOUN	22	nmod
25	hace	VERB	20	advcl
26	tres	NUM	27	nummod
27	días	NOUN	25	obj
28	,	PUNCT	20	punct
29	llevó	VERB	0	root
30	a	ADP	31	case
31	Haro	PROPN	29	iobj
32	Tecglen	PROPN	31	flat
33	a	ADP	34	mark
34	escribir	VERB	29	xcomp
35	ayer	ADV	34	advmod
36	en	ADP	38	case
37	El	DET	38	det
38	País	PROPN	34	obl
39	:	PUNCT	42	punct
40	"	PUNCT	42	punct
41	Podrá	AUX	42	aux
42	ser	AUX	34	ccomp
43	que	SCONJ	48	mark
44	el	DET	45	det
45	periodismo	NOUN	48	nsubj
46	se	PRON	48	expl:pv
47	haya	AUX	48	aux
48	convertido	VERB	42	csubj
49	en	ADP	51	case
50	una	DET	51	det
51	ciencia	NOUN	48	obj
52	exacta	ADJ	51	amod
53	y	CCONJ	63	cc
54	,	PUNCT	55	punct
55	realmente	ADV	63	advmod
56	,	PUNCT	55	punct
57	la	DET	58	det
58	foto	NOUN	63	nsubj
59	del	ADP	60	case
60	día	NOUN	58	nmod
61	sea	AUX	63	cop
62	siempre	ADV	63	advmod
63	una	PRON	48	conj
64	y	CCONJ	65	cc
65	trina	ADJ	63	conj
66	.	PUNCT	29	punct