
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowerCAmelCase					:    Optional[Any]           = logging.getLogger(__name__)

lowerCAmelCase					:    Union[str, Any]           = 'pytorch_model.bin'


@dataclasses.dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							dataclasses.field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}							,       )


@dataclasses.dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
				lowerCAmelCase_     			=							dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """A csv or a json file containing the validation data."""})
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """The name of the task to train on."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """The list of labels for the task."""})


@dataclasses.dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							dataclasses.field(
				    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
				lowerCAmelCase_     			=							dataclasses.field(
				    default="""accuracy"""							,       metadata={"""help""": """The evaluation metric used for the task."""})
				lowerCAmelCase_     			=							dataclasses.field(
				    default="""no"""							,       metadata={
				        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
				    }							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=10							,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=0.0							,       metadata={
				        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
				    }							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=0.0							,       metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=1_00							,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}							,       )
				lowerCAmelCase_     			=							dataclasses.field(
				    default=snake_case_							,       metadata={"""help""": """Random seed for initialization."""}							,       )







def 		A_(     A						:		List[str]		,     A						:		int		,     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		List[str]		,     A						:		Any):
							UpperCamelCase   			=     datasets.concatenate_datasets([infer_input, infer_output]		,     axis=1)

							if args.do_filter_by_confidence:
														UpperCamelCase   			=     dataset.filter(lambda A: example["probability"] > args.confidence_threshold)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														UpperCamelCase   			=     int(eval_result * len(A))
														print(A)
														UpperCamelCase   			=     dataset.sort('probability'		,     reverse=A)
														UpperCamelCase   			=     dataset.select(range(A))

							UpperCamelCase   			=     dataset.remove_columns(['label', 'probability'])
							UpperCamelCase   			=     dataset.rename_column('prediction'		,     'label')
							UpperCamelCase   			=     dataset.map(lambda A: {"label": idalabel[example["label"]]})
							UpperCamelCase   			=     dataset.shuffle(seed=args.seed)

							UpperCamelCase   			=     os.path.join(A		,     f'''train_pseudo.{args.data_file_extension}''')
							if args.data_file_extension == "csv":
														dataset.to_csv(A		,     index=A)
							else:
														dataset.to_json(A)







def 		A_(     A						:		Union[str, Any]		,     A						:		Tuple		,     A						:		Tuple		,     A						:		Any		,     **A						:		Any):
							UpperCamelCase   			=     Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,     datefmt='%m/%d/%Y %H:%M:%S'		,     level=logging.INFO		,     )
							logger.info(accelerator.state)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							UpperCamelCase   			=     STModelArguments(model_name_or_path=A)
							UpperCamelCase   			=     STDataArguments(train_file=A		,     infer_file=A)
							UpperCamelCase   			=     STTrainingArguments(output_dir=A)
							UpperCamelCase   			=     argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(A).items():
																					setattr(A		,     A		,     A)

							for key, value in kwargs.items():
														if hasattr(A		,     A):
																					setattr(A		,     A		,     A)

    # Sanity checks
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							UpperCamelCase   			=     args.train_file
							UpperCamelCase   			=     args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														UpperCamelCase   			=     args.eval_file

							for key in data_files:
														UpperCamelCase   			=     data_files[key].split('.')[-1]
														assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
														if args.data_file_extension is None:
																					UpperCamelCase   			=     extension
														else:
																					assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

							assert (
							    args.eval_metric in datasets.list_metrics()
							), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed)

							logger.info('Creating the initial data directory for self-training...')
							UpperCamelCase   			=     f'''{args.output_dir}/self-train_iter-{{}}'''.format
							UpperCamelCase   			=     data_dir_format(0)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir		,     exist_ok=A)
																					os.makedirs(A		,     exist_ok=A)
							accelerator.wait_for_everyone()

							UpperCamelCase   			=     None
							UpperCamelCase   			=     None
							UpperCamelCase   			=     0
							UpperCamelCase   			=     False
							# Show the progress bar
							UpperCamelCase   			=     tqdm(range(args.max_selftrain_iterations)		,     disable=not accelerator.is_local_main_process)

							# Self-train
							for iteration in range(0		,     int(args.max_selftrain_iterations)):
														UpperCamelCase   			=     data_dir_format(A)
														assert os.path.exists(A)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														UpperCamelCase   			=     os.path.join(A		,     'stage-1')
														UpperCamelCase   			=     {
														    'accelerator': accelerator,
														    'model_name_or_path': args.model_name_or_path,
														    'cache_dir': args.cache_dir,
														    'do_train': True,
														    'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
														    'do_eval': True if args.eval_file is not None else False,
														    'eval_file': data_files['eval'],
														    'do_predict': True,
														    'infer_file': data_files['infer'],
														    'task_name': args.task_name,
														    'label_list': args.label_list,
														    'output_dir': current_output_dir,
														    'eval_metric': args.eval_metric,
														    'evaluation_strategy': args.evaluation_strategy,
														    'early_stopping_patience': args.early_stopping_patience,
														    'early_stopping_threshold': args.early_stopping_threshold,
														    'seed': args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(A		,     A):
																												arguments_dict.update({key: value})

														UpperCamelCase   			=     os.path.join(A		,     'best-checkpoint'		,     A)
														if os.path.exists(A):
																					logger.info(
																					    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'		,     A		,     A		,     )
														else:
																					logger.info('***** Running self-training: iteration: %d, stage: 1 *****'		,     A)
																					finetune(**A)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(A)
																					logger.info('Self-training job completed: iteration: %d, stage: 1.'		,     A)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					UpperCamelCase   			=     os.path.join(A		,     'best-checkpoint')
																					UpperCamelCase   			=     os.path.join(A		,     'stage-2')
																					# Update arguments_dict
																					UpperCamelCase   			=     model_path
																					UpperCamelCase   			=     data_files['train']
																					UpperCamelCase   			=     current_output_dir

																					UpperCamelCase   			=     os.path.join(A		,     'best-checkpoint'		,     A)
																					if os.path.exists(A):
																												logger.info(
																												    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'		,     A		,     A		,     )
																					else:
																												logger.info('***** Running self-training: iteration: %d, stage: 2 *****'		,     A)
																												finetune(**A)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(A)
																												logger.info('Self-training job completed: iteration: %d, stage: 2.'		,     A)

														UpperCamelCase   			=     iteration
														UpperCamelCase   			=     data_dir_format(iteration + 1)

														UpperCamelCase   			=     AutoConfig.from_pretrained(os.path.join(A		,     'best-checkpoint'))
														UpperCamelCase   			=     config.idalabel
														UpperCamelCase   			=     os.path.join(A		,     'eval_results_best-checkpoint.json')
														UpperCamelCase   			=     os.path.join(A		,     'test_results_best-checkpoint.json')
														assert os.path.exists(A)

														with open(A		,     'r') as f:
																					UpperCamelCase   			=     float(json.load(A)[args.eval_metric])
														UpperCamelCase   			=     os.path.join(A		,     'infer_output_best-checkpoint.csv')
														assert os.path.exists(A)
														# Loading the dataset from local csv or json files.
														UpperCamelCase   			=     load_dataset(args.data_file_extension		,     data_files={'data': data_files['infer']})['data']
														UpperCamelCase   			=     load_dataset('csv'		,     data_files={'data': infer_output_file})['data']

														if accelerator.is_main_process:
																					os.makedirs(A		,     exist_ok=A)
																					shutil.copy(A		,     os.path.join(A		,     f'''eval_results_iter-{iteration}.json'''))
																					if os.path.exists(A):
																												shutil.copy(A		,     os.path.join(A		,     f'''test_results_iter-{iteration}.json'''))
																					create_pseudo_labeled_data(A		,     A		,     A		,     A		,     A		,     A)
														accelerator.wait_for_everyone()

														UpperCamelCase   			=     os.path.join(A		,     f'''train_pseudo.{args.data_file_extension}''')

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					UpperCamelCase   			=     eval_result

																					if best_iteration is None:
																												UpperCamelCase   			=     new_iteration
																												UpperCamelCase   			=     new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			UpperCamelCase   			=     new_iteration
																																			UpperCamelCase   			=     new_eval_result
																																			UpperCamelCase   			=     0
																												else:
																																			if new_eval_result == best_eval_result:
																																										UpperCamelCase   			=     new_iteration
																																										UpperCamelCase   			=     new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			UpperCamelCase   			=     True

														progress_bar.update(1)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info('Best iteration: %d'		,     A)
														logger.info('Best evaluation result: %s = %f'		,     args.eval_metric		,     A)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(A		,     f'''eval_results_iter-{iteration}.json''')		,     os.path.join(A		,     'eval_results_best-iteration.json')		,     )
							else:
														# Assume that the last iteration is the best
														logger.info('Best iteration: %d'		,     args.max_selftrain_iterations - 1)
														logger.info('Best evaluation result: %s = %f'		,     args.eval_metric		,     A)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(A		,     f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''')		,     os.path.join(A		,     'eval_results_best-iteration.json')		,     )











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))




