




"""simple docstring"""





import heapq

def        _UpperCamelCase     (      UpperCamelCase				)      ->      set[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			list[list]												=       []

			# 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(UpperCamelCase					,							[-1 * len(UpperCamelCase				), (key, value)]				)

			# chosen_vertices = set of chosen vertices
			__UpperCAmelCase						:			List[str]												=       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						:			Tuple												=       heapq.heappop(UpperCamelCase				)[1][0]
						chosen_vertices.add(UpperCamelCase				)

						# 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						:			str												=       elem[1][1].index(UpperCamelCase				)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(UpperCamelCase				)
			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)}''')



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE		:     Dict         =	[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

SCREAMING_SNAKE_CASE		:     int         =	{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE		:     Any         =	{F'''funnel-transformer/{name}''': 512 for name in _model_names}
SCREAMING_SNAKE_CASE		:     Tuple         =	{F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}

class        UpperCamelCase						(				__a				):
						a__		:Dict      =			VOCAB_FILES_NAMES
						a__		:Dict      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Dict      =			PRETRAINED_INIT_CONFIGURATION
						a__		:str      =			FunnelTokenizer
						a__		:List[str]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:int      =			2





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<sep>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<cls>"			,     __UpperCamelCase="<mask>"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=None			,     __UpperCamelCase="##"			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(
												    __UpperCamelCase			,     tokenizer_file=__UpperCamelCase			,     do_lower_case=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     sep_token=__UpperCamelCase			,     pad_token=__UpperCamelCase			,     cls_token=__UpperCamelCase			,     mask_token=__UpperCamelCase			,     bos_token=__UpperCamelCase			,     eos_token=__UpperCamelCase			,     clean_text=__UpperCamelCase			,     tokenize_chinese_chars=__UpperCamelCase			,     strip_accents=__UpperCamelCase			,     wordpieces_prefix=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
												if (
												    normalizer_state.get("""lowercase"""			,     __UpperCamelCase						) != do_lower_case
												    or normalizer_state.get("""strip_accents"""			,     __UpperCamelCase						) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""			,     __UpperCamelCase						) != tokenize_chinese_chars
												):
																		UpperCamelCase_		:							List[Any]  =		getattr(__UpperCamelCase			,     normalizer_state.pop("""type"""						)						)
																		UpperCamelCase_		:							Union[str, Any]  =		do_lower_case
																		UpperCamelCase_		:							Tuple  =		strip_accents
																		UpperCamelCase_		:							Dict  =		tokenize_chinese_chars
																		UpperCamelCase_		:							Union[str, Any]  =		normalizer_class(**__UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		do_lower_case





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				Dict:
												UpperCamelCase_		:							Any  =		[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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[int]:
												UpperCamelCase_		:							Optional[Any]  =		[self.sep_token_id]
												UpperCamelCase_		:							Any  =		[self.cls_token_id]
												if token_ids_a is None:
																		return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0]
												return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												UpperCamelCase_		:							int  =		self._tokenizer.model.save(__UpperCamelCase			,     name=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)






