


"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_       							=    logging.get_logger(__name__)


a_       							=    {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

a_       							=    {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

a_       							=    {
    "allenai/longformer-base-4096": 40_96,
    "allenai/longformer-large-4096": 40_96,
    "allenai/longformer-large-4096-finetuned-triviaqa": 40_96,
    "allenai/longformer-base-4096-extra.pos.embd.only": 40_96,
    "allenai/longformer-large-4096-extra.pos.embd.only": 40_96,
}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      a__ (				)	->	Tuple:
				_A					  =			(
				    list(range(ord("!"   )     ,			ord("~"   ) + 1   )   ) + list(range(ord("¡"   )     ,			ord("¬"   ) + 1   )   ) + list(range(ord("®"   )     ,			ord("ÿ"   ) + 1   )   )
				)
				_A					  =			bs[:]
				_A					  =			0
				for b in range(2**8   ):
								if b not in bs:
												bs.append(__lowercase   )
												cs.append(2**8 + n   )
												n += 1
				_A					  =			[chr(__lowercase   ) for n in cs]
				return dict(zip(__lowercase     ,			__lowercase   )   )






def      a__ (				__lowercase   )	->	Union[str, Any]:
				_A					  =			set()
				_A					  =			word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								_A					  =			char
				return pairs




class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Optional[Any]    ,   a__      :       Optional[int]    ,   a__      :       Any="replace"    ,   a__      :       Optional[int]="<s>"    ,   a__      :       Optional[int]="</s>"    ,   a__      :       Union[str, Any]="</s>"    ,   a__      :       Optional[Any]="<s>"    ,   a__      :       Tuple="<unk>"    ,   a__      :       int="<pad>"    ,   a__      :       Union[str, Any]="<mask>"    ,   a__      :       Any=False    ,   **a__      :       Union[str, Any]    ,   )       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else bos_token
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else eos_token
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else sep_token
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else cls_token
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else unk_token
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else mask_token

						super().__init__(
						    errors=a__    ,   bos_token=a__    ,   eos_token=a__    ,   unk_token=a__    ,   sep_token=a__    ,   cls_token=a__    ,   pad_token=a__    ,   mask_token=a__    ,   add_prefix_space=a__    ,   **a__    ,   )

						with open(a__    ,   encoding="utf-8"		) as vocab_handle:
										_A					  =			json.load(a__		)
						_A					  =			{v: k for k, v in self.encoder.items()}
						_A					  =			errors  # how to handle errors in decoding
						_A					  =			bytes_to_unicode()
						_A					  =			{v: k for k, v in self.byte_encoder.items()}
						with open(a__    ,   encoding="utf-8"		) as merges_handle:
										_A					  =			merges_handle.read().split("\n"		)[1:-1]
						_A					  =			[tuple(merge.split()		) for merge in bpe_merges]
						_A					  =			dict(zip(a__    ,   range(len(a__		)		)		)		)
						_A					  =			{}
						_A					  =			add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						_A					  =			re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)







		@property
		def  a_ (					self      :       Optional[Any]		)       ->						str:





						'''simple docstring'''







						return len(self.encoder		)







		def  a_ (					self      :       int		)       ->						Tuple:





						'''simple docstring'''







						return dict(self.encoder    ,   **self.added_tokens_encoder		)







		def  a_ (					self      :       List[str]    ,   a__      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						if token in self.cache:
										return self.cache[token]
						_A					  =			tuple(a__		)
						_A					  =			get_pairs(a__		)

						if not pairs:
										return token

						while True:
										_A					  =			min(a__    ,   key=lambda a__		: self.bpe_ranks.get(a__    ,   float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
														break
										_A  ,		_A					  =			bigram
										_A					  =			[]
										_A					  =			0
										while i < len(a__		):
														try:
																		_A					  =			word.index(a__    ,   a__		)
														except ValueError:
																		new_word.extend(word[i:]		)
																		break
														else:
																		new_word.extend(word[i:j]		)
																		_A					  =			j

														if word[i] == first and i < len(a__		) - 1 and word[i + 1] == second:
																		new_word.append(first + second		)
																		i += 2
														else:
																		new_word.append(word[i]		)
																		i += 1
										_A					  =			tuple(a__		)
										_A					  =			new_word
										if len(a__		) == 1:
														break
										else:
														_A					  =			get_pairs(a__		)
						_A					  =			" ".join(a__		)
						_A					  =			word
						return word







		def  a_ (					self      :       str    ,   a__      :       List[str]		)       ->						str:





						'''simple docstring'''







						_A					  =			[]
						for token in re.findall(self.pat    ,   a__		):
										_A					  =			"".join(
										    self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__		).split(" "		)		)
						return bpe_tokens







		def  a_ (					self      :       Optional[int]    ,   a__      :       Any		)       ->						Any:





						'''simple docstring'''







						return self.encoder.get(a__    ,   self.encoder.get(self.unk_token		)		)







		def  a_ (					self      :       Tuple    ,   a__      :       Optional[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						return self.decoder.get(a__		)







		def  a_ (					self      :       Tuple    ,   a__      :       Dict		)       ->						Dict:





						'''simple docstring'''







						_A					  =			"".join(a__		)
						_A					  =			bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"    ,   errors=self.errors		)
						return text







		def  a_ (					self      :       Dict    ,   a__      :       str    ,   a__      :       Optional[str] = None		)       ->						Tuple[str]:





						'''simple docstring'''







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

						with open(a__    ,   "w"    ,   encoding="utf-8"		) as f:
										f.write(json.dumps(self.encoder    ,   indent=2    ,   sort_keys=a__    ,   ensure_ascii=a__		) + "\n"		)

						_A					  =			0
						with open(a__    ,   "w"    ,   encoding="utf-8"		) as writer:
										writer.write("#version: 0.2\n"		)
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,   key=lambda a__		: kv[1]		):
														if index != token_index:
																		logger.warning(
																		    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																		    " Please check that the tokenizer is not corrupted!"		)
																		_A					  =			token_index
														writer.write(" ".join(a__		) + "\n"		)
														index += 1

						return vocab_file, merge_file







		def  a_ (					self      :       Tuple    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None    ,   a__      :       bool = False		)       ->						List[int]:





						'''simple docstring'''







						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=a__    ,   token_ids_a=a__    ,   already_has_special_tokens=a__		)

						if token_ids_a is None:
										return [1] + ([0] * len(a__		)) + [1]
						return [1] + ([0] * len(a__		)) + [1, 1] + ([0] * len(a__		)) + [1]







		def  a_ (					self      :       Optional[int]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			[self.sep_token_id]
						_A					  =			[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  a_ (					self      :       Dict    ,   a__      :       List[str]    ,   a__      :       Dict=False    ,   **a__      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			kwargs.pop("add_prefix_space"    ,   self.add_prefix_space		)
						if (is_split_into_words or add_prefix_space) and (len(a__		) > 0 and not text[0].isspace()):
										_A					  =			" " + text
						return (text, kwargs)



"""simple docstring"""


def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	str:
				# Return True if there is node that has not iterated.
				_A					  =			[False] * len(__lowercase   )
				_A					  =			[]
				queue.append(__lowercase   )
				_A					  =			True

				while queue:
								_A					  =			queue.pop(0   )
								for ind in range(len(graph[u]   )   ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(__lowercase   )
																_A					  =			True
																_A					  =			u

				return visited[t]






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				# This array is filled by BFS and to store path
				_A					  =			[-1] * (len(__lowercase   ))
				_A					  =			0
				while bfs(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   ):
								_A					  =			float("Inf"   )
								_A					  =			sink

								while s != source:
												# Find the minimum value in select path
												_A					  =			min(__lowercase     ,			graph[parent[s]][s]   )
												_A					  =			parent[s]

								max_flow += path_flow
								_A					  =			sink

								while v != source:
												_A					  =			parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												_A					  =			parent[v]
				return max_flow


a_       							=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a_							,     a_       							=    0, 5




print(ford_fulkerson(graph, source, sink))