


'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self		)       ->    List[str]:
          _a						=		{}






     def  a_					(			self		)       ->    None:
          print(self.vertex		)
          for i in self.vertex:
               print(__UpperCamelCase     ,							" -> "     ,							" -> ".join([str(__UpperCamelCase		) for j in self.vertex[i]]		)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    None:
          # check if vertex is already present,
          if from_vertex in self.vertex:
               self.vertex[from_vertex].append(__UpperCamelCase		)
          else:
               # else make a new vertex
               _a						=		[to_vertex]






     def  a_					(			self		)       ->    None:
          # visited array for storing already visited nodes
          _a						=		[False] * len(self.vertex		)

          # call the recursive helper function
          for i in range(len(self.vertex		)		):
               if not visited[i]:
                    self.dfs_recursive(__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    None:
          # mark start vertex as visited
          _a						=		True

          print(__UpperCamelCase     ,							end=" "		)

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
               if not visited[i]:
                    self.dfs_recursive(__UpperCamelCase     ,							__UpperCamelCase		)


if __name__ == "__main__":
   lowercase__				     =		Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print("DFS:")
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3










'''simple docstring'''

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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: PreTrainedTokenizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[int] = None      ,  )      ->     Tuple:







     '''simple docstring'''






     _a						=		{}

     if train_file is not None:
          _a						=		[train_file]
     if eval_file is not None:
          _a						=		[eval_file]
     if test_file is not None:
          _a						=		[test_file]

     _a						=		datasets.load_dataset("csv"      ,  data_files=__lowerCamelCase				)
     _a						=		list(ds[list(files.keys()				)[0]].features.keys()				)
     _a						=		features_name.pop(__lowerCamelCase				)
     _a						=		list(set(ds[list(files.keys()				)[0]][label_name]				)				)
     _a						=		{label: i for i, label in enumerate(__lowerCamelCase				)}
     _a						=		tokenizer.model_input_names
     _a						=		{}

     if len(__lowerCamelCase				) == 1:
          for k in files.keys():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: tokenizer.batch_encode_plus(
                       example[features_name[0]]      ,  truncation=__lowerCamelCase      ,  max_length=__lowerCamelCase      ,  padding="max_length"				)      ,  batched=__lowerCamelCase      ,  )
     elif len(__lowerCamelCase				) == 2:
          for k in files.keys():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: tokenizer.batch_encode_plus(
                       (example[features_name[0]], example[features_name[1]])      ,  truncation=__lowerCamelCase      ,  max_length=__lowerCamelCase      ,  padding="max_length"      ,  )      ,  batched=__lowerCamelCase      ,  )

     def gen_train():
          for ex in transformed_ds[datasets.Split.TRAIN]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     def gen_val():
          for ex in transformed_ds[datasets.Split.VALIDATION]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     def gen_test():
          for ex in transformed_ds[datasets.Split.TEST]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.TRAIN in transformed_ds
         else None
     )

     if train_ds is not None:
          _a						=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.VALIDATION in transformed_ds
         else None
     )

     if val_ds is not None:
          _a						=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.TEST in transformed_ds
         else None
     )

     if test_ds is not None:
          _a						=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

     return train_ds, val_ds, test_ds, labelaid


lowercase__				     =		logging.getLogger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(metadata={'''help''': '''Which column contains the label'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the training file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the development file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the test file'''} )
     UpperCAmelCase 					=				field(
         default=128 ,			metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         } ,			)
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,			)







def __UpperCamelCase   (			)      ->     str:







     '''simple docstring'''






     _a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
     _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      ,  )
     logger.info(
         F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, "
         F"16-bits training: {training_args.fpaa}"				)
     logger.info(F"Training/evaluation parameters {training_args}"				)

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

     _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			,			_a			,			_a			,			_a						=		get_tfds(
         train_file=data_args.train_file      ,  eval_file=data_args.dev_file      ,  test_file=data_args.test_file      ,  tokenizer=__lowerCamelCase      ,  label_column_id=data_args.label_column_id      ,  max_seq_length=data_args.max_seq_length      ,  )

     _a						=		AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,  num_labels=len(__lowerCamelCase				)      ,  labelaid=__lowerCamelCase      ,  idalabel={id: label for label, id in labelaid.items()}      ,  finetuning_task="text-classification"      ,  cache_dir=model_args.cache_dir      ,  )

     with training_args.strategy.scope():
          _a						=		TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path      ,  from_pt=bool(".bin" in model_args.model_name_or_path				)      ,  config=__lowerCamelCase      ,  cache_dir=model_args.cache_dir      ,  )

     def compute_metrics(__lowerCamelCase		: EvalPrediction				) -> Dict:
          _a						=		np.argmax(p.predictions      ,  axis=1				)

          return {"acc": (preds == p.label_ids).mean()}

     # Initialize our Trainer
     _a						=		TFTrainer(
         model=__lowerCamelCase      ,  args=__lowerCamelCase      ,  train_dataset=__lowerCamelCase      ,  eval_dataset=__lowerCamelCase      ,  compute_metrics=__lowerCamelCase      ,  )

     # Training
     if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir				)

     # Evaluation
     _a						=		{}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"				)
          _a						=		trainer.evaluate()
          _a						=		os.path.join(training_args.output_dir      ,  "eval_results.txt"				)

          with open(__lowerCamelCase      ,  "w"				) as writer:
               logger.info("***** Eval results *****"				)

               for key, value in result.items():
                    logger.info(F"  {key} = {value}"				)
                    writer.write(F"{key} = {value}\n"				)

               results.update(__lowerCamelCase				)

     return results


if __name__ == "__main__":
   main()

