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:
Toujours en juin, Chris Zylka rejoint la troupe pour jouer dans le rôle de Daniel, mais il sera cependant remplacé plus tard par un acteur non mentionné, à cause de sa réticence au fait de se raser les cheveux, pour le personnage.
Postion	Tokenization
1	Toujours
2	en
3	juin
4	,
5	Chris
6	Zylka
7	rejoint
8	la
9	troupe
10	pour
11	jouer
12	dans
13	le
14	rôle
15	de
16	Daniel
17	,
18	mais
19	il
20	sera
21	cependant
22	remplacé
23	plus
24	tard
25	par
26	un
27	acteur
28	non
29	mentionné
30	,
31	à
32	cause
33	de
34	sa
35	réticence
36	au
37	à
38	le
39	fait
40	de
41	se
42	raser
43	les
44	cheveux
45	,
46	pour
47	le
48	personnage
49	.
Position	Tokenization	UPOS	Head	Deprel
1	Toujours	ADV	3	advmod
2	en	ADP	3	case
3	juin	NOUN	7	obl:mod
4	,	PUNCT	3	punct
5	Chris	PROPN	7	nsubj
6	Zylka	PROPN	5	flat:name
7	rejoint	VERB	0	root
8	la	DET	9	det
9	troupe	NOUN	7	obj
10	pour	ADP	11	mark
11	jouer	VERB	7	advcl
12	dans	ADP	14	case
13	le	DET	14	det
14	rôle	NOUN	11	obl:arg
15	de	ADP	16	case
16	Daniel	PROPN	14	nmod
17	,	PUNCT	22	punct
18	mais	CCONJ	22	cc
19	il	PRON	22	nsubj:pass
20	sera	AUX	22	aux:pass
21	cependant	ADV	22	advmod
22	remplacé	VERB	7	conj
23	plus	ADV	24	advmod
24	tard	ADV	22	advmod
25	par	ADP	27	case
26	un	DET	27	det
27	acteur	NOUN	22	obl:agent
28	non	ADV	29	advmod
29	mentionné	VERB	27	acl
30	,	PUNCT	31	punct
31	à	ADP	22	advmod
32	cause	NOUN	31	fixed
33	de	ADP	35	case
34	sa	DET	35	det
35	réticence	NOUN	31	obl:arg
36	au	_	None	_
37	à	ADP	38	case
38	le	DET	38	det
39	fait	NOUN	35	nmod
40	de	ADP	41	mark
41	se	PRON	41	obl:arg
42	raser	VERB	38	acl
43	les	DET	43	det
44	cheveux	NOUN	41	obj
45	,	PUNCT	47	punct
46	pour	ADP	47	case
47	le	DET	47	det
48	personnage	NOUN	41	obl:mod
49	.	PUNCT	7	punct