import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCamelCase_    :		Optional[int]							=	logging.get_logger(__name__)

lowerCamelCase_    :		Union[str, Any]							=	"""▁"""

lowerCamelCase_    :		Optional[int]							=	{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase_    :		Optional[Any]							=	{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

lowerCamelCase_    :		str							=	{
    """facebook/m2m100_418M""": 1_024,
}

# fmt: off
lowerCamelCase_    :		Union[str, Any]							=	{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}


class 						a__							(       __snake_case		):
			A__    :       Optional[int]        =      VOCAB_FILES_NAMES
			A__    :       Optional[int]        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__    :       int        =      PRETRAINED_VOCAB_FILES_MAP
			A__    :       Optional[int]        =      ['input_ids', 'attention_mask']

			A__    :       List[int]        =      []
			A__    :       List[int]        =      []



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="m2m100"				,						UpperCAmelCase = None				,						UpperCAmelCase=8				,						**UpperCAmelCase				,						)			->   None:
								__a		 =       {} if sp_model_kwargs is None else sp_model_kwargs

								__a		 =       language_codes
								__a		 =       FAIRSEQ_LANGUAGE_CODES[language_codes]
								__a		 =       {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

								__a		 =       kwargs.get('additional_special_tokens'				,						[]   )
								kwargs["additional_special_tokens"] += [
								    self.get_lang_token(UpperCAmelCase   )
								    for lang_code in fairseq_language_code
								    if self.get_lang_token(UpperCAmelCase   ) not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=UpperCAmelCase				,						tgt_lang=UpperCAmelCase				,						bos_token=UpperCAmelCase				,						eos_token=UpperCAmelCase				,						sep_token=UpperCAmelCase				,						unk_token=UpperCAmelCase				,						pad_token=UpperCAmelCase				,						language_codes=UpperCAmelCase				,						sp_model_kwargs=self.sp_model_kwargs				,						num_madeup_words=UpperCAmelCase				,						**UpperCAmelCase				,						)

								__a		 =       vocab_file
								__a		 =       load_json(UpperCAmelCase   )
								__a		 =       {v: k for k, v in self.encoder.items()}
								__a		 =       spm_file
								__a		 =       load_spm(UpperCAmelCase				,						self.sp_model_kwargs   )

								__a		 =       len(self.encoder   )

								__a		 =       {
								    self.get_lang_token(UpperCAmelCase   ): self.encoder_size + i for i, lang_code in enumerate(UpperCAmelCase   )
								}
								__a		 =       {lang_code: self.encoder_size + i for i, lang_code in enumerate(UpperCAmelCase   )}
								__a		 =       {v: k for k, v in self.lang_token_to_id.items()}

								__a		 =       src_lang if src_lang is not None else 'en'
								__a		 =       tgt_lang
								__a		 =       self.get_lang_id(self._src_lang   )
								self.set_src_lang_special_tokens(self._src_lang   )

								__a		 =       num_madeup_words



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return len(self.encoder   ) + len(self.lang_token_to_id   )



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								return self._src_lang



			@src_lang.setter
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								return self.sp_model.encode(UpperCAmelCase				,						out_type=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
								return self.encoder.get(UpperCAmelCase				,						self.encoder[self.unk_token]   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
								return self.decoder.get(UpperCAmelCase				,						self.unk_token   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[int]:
								__a		 =       []
								__a		 =       ''
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		out_string += self.sp_model.decode(UpperCAmelCase   ) + token
																		__a		 =       []
													else:
																		current_sub_tokens.append(UpperCAmelCase   )
								out_string += self.sp_model.decode(UpperCAmelCase   )
								return out_string.strip()



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

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

								__a		 =       [1] * len(self.prefix_tokens   )
								__a		 =       [1] * len(self.suffix_tokens   )
								if token_ids_a is None:
													return prefix_ones + ([0] * len(UpperCAmelCase   )) + suffix_ones
								return prefix_ones + ([0] * len(UpperCAmelCase   )) + ([0] * len(UpperCAmelCase   )) + suffix_ones



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
								if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       {self.convert_ids_to_tokens(UpperCAmelCase   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab



			def __getstate__(						self   )			->   Dict:
								__a		 =       self.__dict__.copy()
								__a		 =       None
								return state



			def __setstate__(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       d

								# for backward compatibility
								if not hasattr(self				,						'sp_model_kwargs'   ):
													__a		 =       {}

								__a		 =       load_spm(self.spm_file				,						self.sp_model_kwargs   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[str]:
								__a		 =       Path(UpperCAmelCase   )
								if not save_dir.is_dir():
													raise OSError(f'''{save_directory} should be a directory'''   )
								__a		 =       save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
								)
								__a		 =       save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
								)

								save_json(self.encoder				,						UpperCAmelCase   )

								if os.path.abspath(self.spm_file   ) != os.path.abspath(UpperCAmelCase   ) and os.path.isfile(self.spm_file   ):
													copyfile(self.spm_file				,						UpperCAmelCase   )
								elif not os.path.isfile(self.spm_file   ):
													with open(UpperCAmelCase				,						'wb'   ) as fi:
																		__a		 =       self.sp_model.serialized_model_proto()
																		fi.write(UpperCAmelCase   )

								return (str(UpperCAmelCase   ), str(UpperCAmelCase   ))



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = "en"				,						UpperCAmelCase = None				,						UpperCAmelCase = "ro"				,						**UpperCAmelCase				,						)			->   BatchEncoding:
								__a		 =       src_lang
								__a		 =       tgt_lang
								self.set_src_lang_special_tokens(self.src_lang   )
								return super().prepare_seqaseq_batch(UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
								__a		 =       src_lang
								__a		 =       self(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase				,						**UpperCAmelCase   )
								__a		 =       self.get_lang_id(UpperCAmelCase   )
								__a		 =       tgt_lang_id
								return inputs



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								self.set_src_lang_special_tokens(self.src_lang   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								self.set_tgt_lang_special_tokens(self.tgt_lang   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       self.get_lang_token(UpperCAmelCase   )
								__a		 =       self.lang_token_to_id[lang_token]
								__a		 =       [self.cur_lang_id]
								__a		 =       [self.eos_token_id]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       self.get_lang_token(UpperCAmelCase   )
								__a		 =       self.lang_token_to_id[lang_token]
								__a		 =       [self.cur_lang_id]
								__a		 =       [self.eos_token_id]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								return self.lang_code_to_token[lang]







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       self.get_lang_token(UpperCAmelCase   )
								return self.lang_token_to_id[lang_token]






def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       sentencepiece.SentencePieceProcessor(**__lowerCamelCase						)
					spm.Load(str(__lowerCamelCase						)						)
					return spm
def 							lowerCAmelCase(     __lowerCamelCase						):
					with open(__lowerCamelCase				,    'r'						) as f:
										return json.load(__lowerCamelCase						)



def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					with open(__lowerCamelCase				,    'w'						) as f:
										json.dump(__lowerCamelCase				,    __lowerCamelCase				,    indent=2						)





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__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(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    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


lowerCamelCase_    :		str							=	[
    [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],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




