





import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Dict			=   '''▁'''

SCREAMING_SNAKE_CASE_   :      int			=   {'''vocab_file''': '''prophetnet.tokenizer'''}

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

SCREAMING_SNAKE_CASE_   :      Any			=   {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

SCREAMING_SNAKE_CASE_   :      Dict			=   {
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    str:
		__lowercase											=     collections.OrderedDict()
		with open(snake_case						,   'r'						,   encoding='utf-8'							) as reader:
				__lowercase											=     reader.readlines()
		for index, token in enumerate(snake_case							):
				__lowercase											=     token.rstrip('\n'							)
				__lowercase											=     index
		return vocab


class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		VOCAB_FILES_NAMES
	__UpperCamelCase   =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase   =		['''input_ids''', '''attention_mask''']




	def __init__(						self							:     Any					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     str="[SEP]"					,      __lowerCamelCase							:     Any="[SEP]"					,      __lowerCamelCase							:     int="[SEP]"					,      __lowerCamelCase							:     Tuple="[UNK]"					,      __lowerCamelCase							:     Tuple="[PAD]"					,      __lowerCamelCase							:     Optional[int]="[CLS]"					,      __lowerCamelCase							:     Any="[MASK]"					,      __lowerCamelCase							:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase							:     str					,      )       -> None:

			'''simple docstring'''




			__lowercase											=     {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCamelCase					,      )

			try:
					import sentencepiece as spm
			except ImportError:
					logger.warning(
					    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
					    ' pip install sentencepiece'   )
					raise

			__lowercase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(str(__lowerCamelCase   )   )
			__lowercase											=     vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# put special tokens and [unused] tokens into the vocab
			__lowercase											=     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

			for i in range(10   ):
					__lowercase											=     F"[unused{i}]"
					__lowercase											=     5 + i

			# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
			__lowercase											=     12
			__lowercase											=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
			for k in self.fairseq_tokens_to_ids.keys():
					self.unique_no_split_tokens.append(__lowerCamelCase   )




	def __getstate__(						self							:     int   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     self.__dict__.copy()
			__lowercase											=     None
			return state




	def __setstate__(						self							:     Optional[int]					,      __lowerCamelCase							:     str   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     d
			try:
					import sentencepiece as spm
			except ImportError:
					logger.warning(
					    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
					    ' pip install sentencepiece'   )
					raise

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

			__lowercase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(self.vocab_file   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None					,      __lowerCamelCase							:     bool = False   )       -> List[int]:

			'''simple docstring'''




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

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




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None   )       -> List[int]:

			'''simple docstring'''




			__lowercase											=     [self.sep_token_id]

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




	@property
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Any:

			'''simple docstring'''




			return len(self.sp_model   ) + self.fairseq_offset




	def    UpperCAmelCase			(						self							:     Any   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     {self.convert_ids_to_tokens(__lowerCamelCase   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     str   )       -> str:

			'''simple docstring'''




			return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Union[str, Any]   )       -> Optional[int]:

			'''simple docstring'''




			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			__lowercase											=     self.sp_model.PieceToId(__lowerCamelCase   )

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset   )




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     ''.join(__lowerCamelCase   ).replace(__lowerCamelCase					,      ' '   ).strip()
			return out_string




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

			'''simple docstring'''




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

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

			return (out_vocab_file,)






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None   )       -> List[int]:

			'''simple docstring'''




			if token_ids_a is None:
					return token_ids_a + [self.sep_token_id]
			__lowercase											=     [self.sep_token_id]
			return token_ids_a + sep + token_ids_a + sep











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

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

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

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

				max_flow += path_flow
				__lowercase											=     sink

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


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [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],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




