


"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


a_       							=    logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
a_       							=    {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

a_       							=    {
    "RUCAIBox/mvp": 10_24,
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']
		__UpperCamelCase           =			MvpTokenizer







		def __init__(					self      :       List[Any]    ,   a__      :       Any=None    ,   a__      :       Optional[int]=None    ,   a__      :       int=None    ,   a__      :       Dict="replace"    ,   a__      :       List[Any]="<s>"    ,   a__      :       Dict="</s>"    ,   a__      :       int="</s>"    ,   a__      :       int="<s>"    ,   a__      :       Optional[int]="<unk>"    ,   a__      :       Tuple="<pad>"    ,   a__      :       List[Any]="<mask>"    ,   a__      :       Optional[Any]=False    ,   a__      :       Union[str, Any]=True    ,   **a__      :       Union[str, Any]    ,   )       ->						Optional[int]:





						'''simple docstring'''







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

						_A					  =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get("add_prefix_space"    ,   a__		) != add_prefix_space:
										_A					  =			getattr(a__    ,   pre_tok_state.pop("type"		)		)
										_A					  =			add_prefix_space
										_A					  =			pre_tok_class(**a__		)

						_A					  =			add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						_A					  =			"post_processor"
						_A					  =			getattr(self.backend_tokenizer    ,   a__    ,   a__		)
						if tokenizer_component_instance:
										_A					  =			json.loads(tokenizer_component_instance.__getstate__()		)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
														_A					  =			tuple(state["sep"]		)
										if "cls" in state:
														_A					  =			tuple(state["cls"]		)

										_A					  =			False

										if state.get("add_prefix_space"    ,   a__		) != add_prefix_space:
														_A					  =			add_prefix_space
														_A					  =			True

										if state.get("trim_offsets"    ,   a__		) != trim_offsets:
														_A					  =			trim_offsets
														_A					  =			True

										if changes_to_apply:
														_A					  =			getattr(a__    ,   state.pop("type"		)		)
														_A					  =			component_class(**a__		)
														setattr(self.backend_tokenizer    ,   a__    ,   a__		)







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





						'''simple docstring'''







						if self._mask_token is None:
										if self.verbose:
														logger.error("Using mask_token, but it is not set yet."		)
										return None
						return str(self._mask_token		)







		@mask_token.setter
		def  a_ (					self      :       List[Any]    ,   a__      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else value
						_A					  =			value







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





						'''simple docstring'''







						_A					  =			kwargs.get("is_split_into_words"    ,   a__		)

						if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."		)

						return super()._batch_encode_plus(*a__    ,   **a__		)







		def  a_ (					self      :       Optional[Any]    ,   *a__      :       Tuple    ,   **a__      :       List[Any]		)       ->						BatchEncoding:





						'''simple docstring'''







						_A					  =			kwargs.get("is_split_into_words"    ,   a__		)

						if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."		)

						return super()._encode_plus(*a__    ,   **a__		)







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





						'''simple docstring'''







						_A					  =			self._tokenizer.model.save(a__    ,   name=a__		)
						return tuple(a__		)







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





						'''simple docstring'''







						_A					  =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
										return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







		def  a_ (					self      :       Any    ,   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]



"""simple docstring"""


class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			None
						_A					  =			None
						_A					  =			graph

						self._normalize_graph(a__    ,   a__		)
						_A					  =			len(a__		)
						_A					  =			None







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





						'''simple docstring'''







						if sources is int:
										_A					  =			[sources]
						if sinks is int:
										_A					  =			[sinks]

						if len(a__		) == 0 or len(a__		) == 0:
										return

						_A					  =			sources[0]
						_A					  =			sinks[0]

						# make fake vertex if there are more
						# than one source or sink
						if len(a__		) > 1 or len(a__		) > 1:
										_A					  =			0
										for i in sources:
														max_input_flow += sum(self.graph[i]		)

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.insert(0    ,   0		)
										self.graph.insert(0    ,   [0] * size		)
										for i in sources:
														_A					  =			max_input_flow
										_A					  =			0

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.append(0		)
										self.graph.append([0] * size		)
										for i in sinks:
														_A					  =			max_input_flow
										_A					  =			size - 1







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





						'''simple docstring'''







						if self.maximum_flow_algorithm is None:
										raise Exception("You need to set maximum flow algorithm before."		)
						if self.source_index is None or self.sink_index is None:
										return 0

						self.maximum_flow_algorithm.execute()
						return self.maximum_flow_algorithm.getMaximumFlow()







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





						'''simple docstring'''







						_A					  =			algorithm(self		)





class snake_case      :







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





						'''simple docstring'''







						_A					  =			flow_network
						_A					  =			flow_network.verticesCount
						_A					  =			flow_network.sourceIndex
						_A					  =			flow_network.sinkIndex
						# it's just a reference, so you shouldn't change
						# it in your algorithms, use deep copy before doing that
						_A					  =			flow_network.graph
						_A					  =			False







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						if not self.executed:
										self._algorithm()
										_A					  =			True







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







						pass





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Optional[Any]    ,   a__      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						super().__init__(a__		)
						# use this to save your result
						_A					  =			-1







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





						'''simple docstring'''







						if not self.executed:
										raise Exception("You should execute algorithm before using its result!"		)

						return self.maximum_flow





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						super().__init__(a__		)

						_A					  =			[[0] * self.verticies_count for i in range(self.verticies_count		)]

						_A					  =			[0] * self.verticies_count
						_A					  =			[0] * self.verticies_count







		def  a_ (					self      :       Any		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.verticies_count

						# push some substance to graph
						for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]		):
										self.preflow[self.source_index][nextvertex_index] += bandwidth
										self.preflow[nextvertex_index][self.source_index] -= bandwidth
										self.excesses[nextvertex_index] += bandwidth

						# Relabel-to-front selection rule
						_A					  =			[
						    i
						    for i in range(self.verticies_count		)
						    if i != self.source_index and i != self.sink_index
						]

						# move through list
						_A					  =			0
						while i < len(a__		):
										_A					  =			vertices_list[i]
										_A					  =			self.heights[vertex_index]
										self.process_vertex(a__		)
										if self.heights[vertex_index] > previous_height:
														# if it was relabeled, swap elements
														# and start from 0 index
														vertices_list.insert(0    ,   vertices_list.pop(a__		)		)
														_A					  =			0
										else:
														i += 1

						_A					  =			sum(self.preflow[self.source_index]		)







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





						'''simple docstring'''







						while self.excesses[vertex_index] > 0:
										for neighbour_index in range(self.verticies_count		):
														# if it's neighbour and current vertex is higher
														if (
														    self.graph[vertex_index][neighbour_index]
														    - self.preflow[vertex_index][neighbour_index]
														    > 0
														    and self.heights[vertex_index] > self.heights[neighbour_index]
														):
																		self.push(a__    ,   a__		)

										self.relabel(a__		)







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





						'''simple docstring'''







						_A					  =			min(
						    self.excesses[from_index]    ,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]    ,   )
						self.preflow[from_index][to_index] += preflow_delta
						self.preflow[to_index][from_index] -= preflow_delta
						self.excesses[from_index] -= preflow_delta
						self.excesses[to_index] += preflow_delta







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





						'''simple docstring'''







						_A					  =			None
						for to_index in range(self.verticies_count		):
										if (
										    self.graph[vertex_index][to_index]
										    - self.preflow[vertex_index][to_index]
										    > 0
										) and (min_height is None or self.heights[to_index] < min_height):
														_A					  =			self.heights[to_index]

						if min_height is not None:
										_A					  =			min_height + 1


if __name__ == "__main__":
		a_       							=    [0]
		a_       							=    [3]
		# graph = [
		#     [0, 0, 4, 6, 0, 0],
		#     [0, 0, 5, 2, 0, 0],
		#     [0, 0, 0, 0, 4, 4],
		#     [0, 0, 0, 0, 6, 6],
		#     [0, 0, 0, 0, 0, 0],
		#     [0, 0, 0, 0, 0, 0],
		# ]
		a_       							=    [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		a_       							=    FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		a_       							=    flow_network.find_maximum_flow()

		print(f'''maximum flow is {maximum_flow}''')