
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase     				=			None


UpperCAmelCase     				=			logging.get_logger(__name__)

UpperCAmelCase     				=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase     				=			{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase     				=			{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Tuple										=	VOCAB_FILES_NAMES
	_UpperCamelCase					:					int										=	PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase					:					List[str]										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase					:					str										=	["""input_ids""", """attention_mask"""]
	_UpperCamelCase					:					Optional[int]										=	XLMRobertaTokenizer





	def __init__(	self		,   snake_case=None		,   snake_case=None		,   snake_case="<s>"		,   snake_case="</s>"		,   snake_case="</s>"		,   snake_case="<s>"		,   snake_case="<unk>"		,   snake_case="<pad>"		,   snake_case="<mask>"		,   **snake_case		,   ):
		# Mask token behave like a normal word, i.e. include the space before it
		lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else mask_token

		super().__init__(
		    snake_case		,   tokenizer_file=snake_case		,   bos_token=snake_case		,   eos_token=snake_case		,   sep_token=snake_case		,   cls_token=snake_case		,   unk_token=snake_case		,   pad_token=snake_case		,   mask_token=snake_case		,   **snake_case		,   )

		lowercase					  =   vocab_file
		lowercase					  =   False if not self.vocab_file else True





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowercase					  =   [self.cls_token_id]
		lowercase					  =   [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):
		lowercase					  =   [self.sep_token_id]
		lowercase					  =   [self.cls_token_id]

		if token_ids_a is None:
			return len(cls + token_ids_a + sep	) * [0]
		return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
			    'tokenizer.'	)

		if not os.path.isdir(snake_case	):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''	)
			return
		lowercase					  =   os.path.join(
		    snake_case		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

		if os.path.abspath(self.vocab_file	) != os.path.abspath(snake_case	):
			copyfile(self.vocab_file		,   snake_case	)

		return (out_vocab_file,)



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

