
"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


_a								=						logging.get_logger(__name__)

_a								=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_a								=						{
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

_a								=						{
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

_a								=						{
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = VOCAB_FILES_NAMES
		lowercase__         = PRETRAINED_VOCAB_FILES_MAP
		lowercase__         = PRETRAINED_INIT_CONFIGURATION
		lowercase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__         = RealmTokenizer



		def __init__(   self							,						__a=None							,						__a=None							,						__a=True							,						__a="[UNK]"							,						__a="[SEP]"							,						__a="[PAD]"							,						__a="[CLS]"							,						__a="[MASK]"							,						__a=True							,						__a=None							,						**__a							,						)			->			str:






							'''simple docstring'''
							super().__init__(
							    __a							,						tokenizer_file=__a							,						do_lower_case=__a							,						unk_token=__a							,						sep_token=__a							,						pad_token=__a							,						cls_token=__a							,						mask_token=__a							,						tokenize_chinese_chars=__a							,						strip_accents=__a							,						**__a							,						)

							_UpperCamelCase									=    json.loads(self.backend_tokenizer.normalizer.__getstate__())
							if (
							    normalizer_state.get('''lowercase'''							,						__a) != do_lower_case
							    or normalizer_state.get('''strip_accents'''							,						__a) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''							,						__a) != tokenize_chinese_chars
							):
												_UpperCamelCase									=    getattr(__a							,						normalizer_state.pop('''type'''))
												_UpperCamelCase									=    do_lower_case
												_UpperCamelCase									=    strip_accents
												_UpperCamelCase									=    tokenize_chinese_chars
												_UpperCamelCase									=    normalizer_class(**__a)

							_UpperCamelCase									=    do_lower_case



		def 		UpperCAmelCase				(   self							,						__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    PaddingStrategy.MAX_LENGTH

							_UpperCamelCase									=    text
							_UpperCamelCase									=    kwargs.pop('''text_pair'''							,						__a)
							_UpperCamelCase									=    kwargs.pop('''return_tensors'''							,						__a)

							_UpperCamelCase									=    {
							    '''input_ids''': [],
							    '''attention_mask''': [],
							    '''token_type_ids''': [],
							}

							for idx, candidate_text in enumerate(__a):
												if batch_text_pair is not None:
																	_UpperCamelCase									=    batch_text_pair[idx]
												else:
																	_UpperCamelCase									=    None

												_UpperCamelCase									=    super().__call__(__a							,						__a							,						return_tensors=__a							,						**__a)

												_UpperCamelCase									=    encoded_candidates.get('''input_ids''')
												_UpperCamelCase									=    encoded_candidates.get('''attention_mask''')
												_UpperCamelCase									=    encoded_candidates.get('''token_type_ids''')

												if encoded_input_ids is not None:
																	output_data["input_ids"].append(__a)
												if encoded_attention_mask is not None:
																	output_data["attention_mask"].append(__a)
												if encoded_token_type_ids is not None:
																	output_data["token_type_ids"].append(__a)

							_UpperCamelCase									=    {key: item for key, item in output_data.items() if len(__a) != 0}

							return BatchEncoding(__a							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a=None)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output



		def 		UpperCAmelCase				(   self							,						__a							,						__a = None)			->			List[int]:






							'''simple docstring'''
							_UpperCamelCase									=    [self.sep_token_id]
							_UpperCamelCase									=    [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) * [0] + len(token_ids_a + sep) * [1]




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None)			->			Tuple[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self._tokenizer.model.save(__a							,						name=__a)
							return tuple(__a)








"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






