



'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


snake_case_			:     List[Any]  	=  logging.getLogger(__name__)






class 							lowercase__     (							__snake_case       ):
	lowercase__      =      """token-classification"""





	def __init__(			self :    int			,lowerCamelCase__ :    Tuple			):







							'''simple docstring'''


							if type(UpperCamelCase__			) == dict:
													_UpperCamelCase :		Optional[Any]			    =       Namespace(**UpperCamelCase__			)
							_UpperCamelCase :		Any			    =       import_module('tasks'			)
							try:
													_UpperCamelCase :		Optional[int]			    =       getattr(UpperCamelCase__			,hparams.task_type			)
													_UpperCamelCase :		TokenClassificationTask			    =       token_classification_task_clazz()
							except AttributeError:
													raise ValueError(
													    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
													    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'			)
							_UpperCamelCase :		List[str]			    =       self.token_classification_task.get_labels(hparams.labels			)
							_UpperCamelCase :		Any			    =       CrossEntropyLoss().ignore_index
							super().__init__(UpperCamelCase__			,len(self.labels			)			,self.mode			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							return self.model(**UpperCamelCase__			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
							if self.config.model_type != "distilbert":
													_UpperCamelCase :		int			    =       (
													    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
													)  # XLM and RoBERTa don"t use token_type_ids

							_UpperCamelCase :		Any			    =       self(**UpperCamelCase__			)
							_UpperCamelCase :		Dict			    =       outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.hparams
							for mode in ["train", "dev", "test"]:
													_UpperCamelCase :		Dict			    =       self._feature_file(UpperCamelCase__			)
													if os.path.exists(UpperCamelCase__			) and not args.overwrite_cache:
																			logger.info('Loading features from cached file %s'			,UpperCamelCase__			)
																			_UpperCamelCase :		List[str]			    =       torch.load(UpperCamelCase__			)
													else:
																			logger.info('Creating features from dataset file at %s'			,args.data_dir			)
																			_UpperCamelCase :		Any			    =       self.token_classification_task.read_examples_from_file(args.data_dir			,UpperCamelCase__			)
																			_UpperCamelCase :		Any			    =       self.token_classification_task.convert_examples_to_features(
																			    UpperCamelCase__			,self.labels			,args.max_seq_length			,self.tokenizer			,cls_token_at_end=bool(self.config.model_type in ['xlnet']			)			,cls_token=self.tokenizer.cls_token			,cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0			,sep_token=self.tokenizer.sep_token			,sep_token_extra=UpperCamelCase__			,pad_on_left=bool(self.config.model_type in ['xlnet']			)			,pad_token=self.tokenizer.pad_token_id			,pad_token_segment_id=self.tokenizer.pad_token_type_id			,pad_token_label_id=self.pad_token_label_id			,)
																			logger.info('Saving features into cached file %s'			,UpperCamelCase__			)
																			torch.save(UpperCamelCase__			,UpperCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    int			,lowerCamelCase__ :    int			,lowerCamelCase__ :    bool = False			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       self._feature_file(UpperCamelCase__			)
							logger.info('Loading features from cached file %s'			,UpperCamelCase__			)
							_UpperCamelCase :		Tuple			    =       torch.load(UpperCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       torch.tensor([f.input_ids for f in features]			,dtype=torch.long			)
							_UpperCamelCase :		int			    =       torch.tensor([f.attention_mask for f in features]			,dtype=torch.long			)
							if features[0].token_type_ids is not None:
													_UpperCamelCase :		int			    =       torch.tensor([f.token_type_ids for f in features]			,dtype=torch.long			)
							else:
													_UpperCamelCase :		str			    =       torch.tensor([0 for f in features]			,dtype=torch.long			)
													# HACK(we will not use this anymore soon)
							_UpperCamelCase :		List[str]			    =       torch.tensor([f.label_ids for f in features]			,dtype=torch.long			)
							return DataLoader(
							    TensorDataset(UpperCamelCase__			,UpperCamelCase__			,UpperCamelCase__			,UpperCamelCase__			)			,batch_size=UpperCamelCase__			)





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							"""Compute validation""" ""
							_UpperCamelCase :		str			    =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
							if self.config.model_type != "distilbert":
													_UpperCamelCase :		Dict			    =       (
													    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
													)  # XLM and RoBERTa don"t use token_type_ids
							_UpperCamelCase :		List[str]			    =       self(**UpperCamelCase__			)
							_UpperCamelCase :		Any			    =       outputs[:2]
							_UpperCamelCase :		Union[str, Any]			    =       logits.detach().cpu().numpy()
							_UpperCamelCase :		Optional[Any]			    =       inputs["labels"].detach().cpu().numpy()
							return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       torch.stack([x['val_loss'] for x in outputs]			).mean()
							_UpperCamelCase :		Dict			    =       np.concatenate([x['pred'] for x in outputs]			,axis=0			)
							_UpperCamelCase :		List[Any]			    =       np.argmax(UpperCamelCase__			,axis=2			)
							_UpperCamelCase :		List[str]			    =       np.concatenate([x['target'] for x in outputs]			,axis=0			)

							_UpperCamelCase :		Tuple			    =       dict(enumerate(self.labels			)			)
							_UpperCamelCase :		Union[str, Any]			    =       [[] for _ in range(out_label_ids.shape[0]			)]
							_UpperCamelCase :		int			    =       [[] for _ in range(out_label_ids.shape[0]			)]

							for i in range(out_label_ids.shape[0]			):
													for j in range(out_label_ids.shape[1]			):
																			if out_label_ids[i, j] != self.pad_token_label_id:
																									out_label_list[i].append(label_map[out_label_ids[i][j]]			)
																									preds_list[i].append(label_map[preds[i][j]]			)

							_UpperCamelCase :		Tuple			    =       {
							    "val_loss": val_loss_mean,
							    "accuracy_score": accuracy_score(UpperCamelCase__			,UpperCamelCase__			),
							    "precision": precision_score(UpperCamelCase__			,UpperCamelCase__			),
							    "recall": recall_score(UpperCamelCase__			,UpperCamelCase__			),
							    "f1": fa_score(UpperCamelCase__			,UpperCamelCase__			),
							}

							_UpperCamelCase :		str			    =       dict(results.items()			)
							_UpperCamelCase :		List[str]			    =       results
							return ret, preds_list, out_label_list





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       self._eval_end(UpperCamelCase__			)
							_UpperCamelCase :		Tuple			    =       ret["log"]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       self._eval_end(UpperCamelCase__			)

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							_UpperCamelCase :		List[Any]			    =       ret["log"]
							# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
							return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							BaseTransformer.add_model_specific_args(UpperCamelCase__			,UpperCamelCase__			)
							parser.add_argument(
							    '--task_type'			,default='NER'			,type=UpperCamelCase__			,help='Task type to fine tune in training (e.g. NER, POS, etc)'			)
							parser.add_argument(
							    '--max_seq_length'			,default=128			,type=UpperCamelCase__			,help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )			,)

							parser.add_argument(
							    '--labels'			,default=''			,type=UpperCamelCase__			,help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'			,)
							parser.add_argument(
							    '--gpus'			,default=0			,type=UpperCamelCase__			,help='The number of GPUs allocated for this, it is by default 0 meaning none'			,)

							parser.add_argument(
							    '--overwrite_cache'			,action='store_true'			,help='Overwrite the cached training and evaluation sets'			)

							return parser


if __name__ == "__main__":
					snake_case_			:     Optional[Any]  	=  argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					snake_case_			:     str  	=  NERTransformer.add_model_specific_args(parser, os.getcwd())
					snake_case_			:     Optional[Any]  	=  parser.parse_args()
					snake_case_			:     Optional[int]  	=  NERTransformer(args)
					snake_case_			:     Dict  	=  generic_train(model, args)

					if args.do_predict:
										# See https://github.com/huggingface/transformers/issues/3159
										# pl use this default format to create a checkpoint:
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
										# /pytorch_lightning/callbacks/model_checkpoint.py#L322
										snake_case_			:     List[str]  	=  sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
										snake_case_			:     Optional[Any]  	=  model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)







'''simple docstring'''
class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       None
							_UpperCamelCase :		List[Any]			    =       None
							_UpperCamelCase :		int			    =       graph

							self._normalize_graph(lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       len(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       None





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							if sources is int:
													_UpperCamelCase :		Optional[int]			    =       [sources]
							if sinks is int:
													_UpperCamelCase :		Union[str, Any]			    =       [sinks]

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

							_UpperCamelCase :		List[str]			    =       sources[0]
							_UpperCamelCase :		str			    =       sinks[0]

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

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

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





	def      UpperCamelCase_  (			self :    List[str]			):







							'''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      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       algorithm(self			)






class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       flow_network
							_UpperCamelCase :		List[str]			    =       flow_network.verticesCount
							_UpperCamelCase :		List[str]			    =       flow_network.sourceIndex
							_UpperCamelCase :		Any			    =       flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							_UpperCamelCase :		List[Any]			    =       flow_network.graph
							_UpperCamelCase :		Any			    =       False





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							if not self.executed:
													self._algorithm()
													_UpperCamelCase :		Any			    =       True





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							pass






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)
							# use this to save your result
							_UpperCamelCase :		Tuple			    =       -1





	def      UpperCamelCase_  (			self :    Dict			):







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

							return self.maximum_flow






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       [[0] * self.verticies_count for i in range(self.verticies_count			)]

							_UpperCamelCase :		Optional[Any]			    =       [0] * self.verticies_count
							_UpperCamelCase :		List[str]			    =       [0] * self.verticies_count





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       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
							_UpperCamelCase :		List[str]			    =       [
							    i
							    for i in range(self.verticies_count			)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							_UpperCamelCase :		int			    =       0
							while i < len(lowerCamelCase__			):
													_UpperCamelCase :		List[Any]			    =       vertices_list[i]
													_UpperCamelCase :		str			    =       self.heights[vertex_index]
													self.process_vertex(lowerCamelCase__			)
													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(lowerCamelCase__			)			)
																			_UpperCamelCase :		Dict			    =       0
													else:
																			i += 1

							_UpperCamelCase :		Optional[Any]			    =       sum(self.preflow[self.source_index]			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Dict			):







							'''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(lowerCamelCase__			,lowerCamelCase__			)

													self.relabel(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       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      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       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):
																			_UpperCamelCase :		List[Any]			    =       self.heights[to_index]

							if min_height is not None:
													_UpperCamelCase :		Any			    =       min_height + 1


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  [0]
					snake_case_			:     int  	=  [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],
					# ]
					snake_case_			:     int  	=  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					snake_case_			:     List[Any]  	=  FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					snake_case_			:     Tuple  	=  flow_network.find_maximum_flow()

					print(F"""maximum flow is {maximum_flow}""")


