




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__       				=	logging.getLogger(__name__)
@dataclass
class 							__lowercase   :
     _lowerCAmelCase		       =						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     _lowerCAmelCase		       =						field(
         default=_snake_case	,		metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     _lowerCAmelCase		       =						field(
         default=_snake_case	,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     _lowerCAmelCase		       =						field(
         default=_snake_case	,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,		)



@dataclass
class 							__lowercase   :
     _lowerCAmelCase		       =						field(
         metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()    )}    )
     _lowerCAmelCase		       =						field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}    )
     _lowerCAmelCase		       =						field(
         default=1_28	,		metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }	,		)
     _lowerCAmelCase		       =						field(
         default=_snake_case	,		metadata={"help": "Overwrite the cached training and evaluation sets"}    )






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  default_data_collator(__A			)
  a_		    =  batch.pop('''pairID'''      ,      __A			)
  return batch






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
  a_    , a_    , a_		    =  parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir			)
      and os.listdir(training_args.output_dir			)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
        ''' --overwrite_output_dir to overcome.'''			)

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''      ,      datefmt='''%m/%d/%Y %H:%M:%S'''      ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,      )
  logger.warning(
      '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''      ,      training_args.local_rank      ,      training_args.device      ,      training_args.n_gpu      ,      bool(training_args.local_rank != -1			)      ,      training_args.fpaa      ,      )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('''Training/evaluation parameters %s'''      ,      __A			)

  # Set seed
  set_seed(training_args.seed			)

  try:
    a_		    =  hans_tasks_num_labels[data_args.task_name]
  except KeyError:
    raise ValueError('''Task not found: %s''' % (data_args.task_name)			)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a_		    =  AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,      num_labels=__A      ,      finetuning_task=data_args.task_name      ,      cache_dir=model_args.cache_dir      ,      )
  a_		    =  AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,      cache_dir=model_args.cache_dir      ,      )
  a_		    =  AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path      ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)      ,      config=__A      ,      cache_dir=model_args.cache_dir      ,      )

  # Get datasets
  a_		    =  (
      HansDataset(
          data_dir=data_args.data_dir      ,      tokenizer=__A      ,      task=data_args.task_name      ,      max_seq_length=data_args.max_seq_length      ,      overwrite_cache=data_args.overwrite_cache      ,      )
      if training_args.do_train
      else None
  )
  a_		    =  (
      HansDataset(
          data_dir=data_args.data_dir      ,      tokenizer=__A      ,      task=data_args.task_name      ,      max_seq_length=data_args.max_seq_length      ,      overwrite_cache=data_args.overwrite_cache      ,      evaluate=__A      ,      )
      if training_args.do_eval
      else None
  )

  # Initialize our Trainer
  a_		    =  Trainer(
      model=__A      ,      args=__A      ,      train_dataset=__A      ,      eval_dataset=__A      ,      data_collator=__A      ,      )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''			)

    a_		    =  trainer.predict(__A			)
    a_		    =  output.predictions
    a_		    =  np.argmax(__A      ,      axis=1			)

    a_		    =  [ex.pairID for ex in eval_dataset]
    a_		    =  os.path.join(training_args.output_dir      ,      '''hans_predictions.txt'''			)
    a_		    =  eval_dataset.get_labels()
    if trainer.is_world_master():
      with open(__A      ,      '''w'''			) as writer:
        writer.write('''pairID,gold_label\n'''			)
        for pid, pred in zip(__A      ,      __A			):
          writer.write('''ex''' + str(__A			) + ''',''' + label_list[int(__A			)] + '''\n'''			)

    trainer._log(output.metrics			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  main()


if __name__ == "__main__":
  main()



lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



