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:
Elle a obtenu un diplôme d'école de médecine et est maintenant une énième stagiaire à Denver Mémorial Hospital, où elle aura la chance de travailler avec le célèbre cardiologue Gina Beckett et où, par hasard, Will Rider, celui pour qui elle avait craqué en fac de médecine est aussi un stagiaire.
Postion	Tokenization
1	Elle
2	a
3	obtenu
4	un
5	diplôme
6	d'
7	école
8	de
9	médecine
10	et
11	est
12	maintenant
13	une
14	énième
15	stagiaire
16	à
17	Denver
18	Mémorial
19	Hospital
20	,
21	où
22	elle
23	aura
24	la
25	chance
26	de
27	travailler
28	avec
29	le
30	célèbre
31	cardiologue
32	Gina
33	Beckett
34	et
35	où
36	,
37	par
38	hasard
39	,
40	Will
41	Rider
42	,
43	celui
44	pour
45	qui
46	elle
47	avait
48	craqué
49	en
50	fac
51	de
52	médecine
53	est
54	aussi
55	un
56	stagiaire
57	.
Position	Tokenization	UPOS	Head	Deprel
1	Elle	PRON	3	nsubj
2	a	AUX	3	aux:tense
3	obtenu	VERB	0	root
4	un	DET	5	det
5	diplôme	NOUN	3	obj
6	d'	ADP	7	case
7	école	NOUN	5	nmod
8	de	ADP	9	case
9	médecine	NOUN	7	nmod
10	et	CCONJ	15	cc
11	est	AUX	15	cop
12	maintenant	ADV	15	advmod
13	une	DET	15	det
14	énième	ADJ	15	amod
15	stagiaire	NOUN	3	conj
16	à	ADP	17	case
17	Denver	PROPN	15	nmod
18	Mémorial	PROPN	17	flat:name
19	Hospital	PROPN	18	flat:name
20	,	PUNCT	23	punct
21	où	ADV	23	advmod
22	elle	PRON	23	nsubj
23	aura	VERB	17	acl:relcl
24	la	DET	25	det
25	chance	NOUN	23	obj
26	de	ADP	27	mark
27	travailler	VERB	25	acl
28	avec	ADP	31	case
29	le	DET	31	det
30	célèbre	ADJ	31	amod
31	cardiologue	NOUN	27	obl:arg
32	Gina	PROPN	31	appos
33	Beckett	PROPN	32	flat:name
34	et	CCONJ	56	cc
35	où	ADV	56	advmod
36	,	PUNCT	35	punct
37	par	ADP	38	case
38	hasard	NOUN	56	obl:mod
39	,	PUNCT	38	punct
40	Will	PROPN	56	nsubj
41	Rider	PROPN	40	flat:name
42	,	PUNCT	43	punct
43	celui	PRON	40	appos
44	pour	ADP	45	case
45	qui	PRON	48	obl:mod
46	elle	PRON	48	nsubj
47	avait	AUX	48	aux:tense
48	craqué	VERB	43	acl:relcl
49	en	ADP	50	case
50	fac	NOUN	48	obl:mod
51	de	ADP	52	case
52	médecine	NOUN	50	nmod
53	est	AUX	56	cop
54	aussi	ADV	56	advmod
55	un	DET	56	det
56	stagiaire	NOUN	23	conj
57	.	PUNCT	3	punct