

"""simple docstring"""





import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Optional[Any]  				=	argparse.ArgumentParser()
	parser.add_argument('''--model_ckpt'''						,     type=A_						,     default='''microsoft/unixcoder-base-nine''' )
	parser.add_argument('''--num_epochs'''						,     type=A_						,     default=5 )
	parser.add_argument('''--batch_size'''						,     type=A_						,     default=6 )
	parser.add_argument('''--gradient_accumulation_steps'''						,     type=A_						,     default=1 )
	parser.add_argument('''--freeze'''						,     type=A_						,     default=A_ )
	parser.add_argument('''--learning_rate'''						,     type=A_						,     default=5e-4 )
	parser.add_argument('''--seed'''						,     type=A_						,     default=0 )
	parser.add_argument('''--lr_scheduler_type'''						,     type=A_						,     default='''cosine''' )
	parser.add_argument('''--num_warmup_steps'''						,     type=A_						,     default=10 )
	parser.add_argument('''--weight_decay'''						,     type=A_						,     default=0.01 )
	parser.add_argument('''--output_dir'''						,     type=A_						,     default='''./results''' )
	return parser.parse_args()


__UpperCamelCase  :      List[Any]							      =		load('''accuracy''')

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	eval_pred
	lowerCAmelCase__	:						str  				=	np.argmax(A_						,     axis=1 )
	return metric.compute(predictions=A_						,     references=A_ )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]		):
						super().__init__()
						lowerCAmelCase__	:						List[Any]  				=	trainer







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str   ,lowercase_			:				Any   ,lowercase_			:				Dict   ,**lowercase_			:				Dict		):
						if control.should_evaluate:
							lowerCAmelCase__	:						Tuple  				=	deepcopy(lowercase_		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,metric_key_prefix='''train'''		)
							return control_copy

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						int  				=	get_args()
	set_seed(args.seed )

	lowerCAmelCase__	:						Optional[Any]  				=	load_dataset('''codeparrot/codecomplex'''						,     split='''train''' )
	lowerCAmelCase__	:						int  				=	dataset.train_test_split(test_size=0.2 )
	lowerCAmelCase__	:						Any  				=	train_test['''test'''].train_test_split(test_size=0.5 )
	lowerCAmelCase__	:						Union[str, Any]  				=	DatasetDict(
	    {
	        '''train''': train_test['''train'''],
	        '''test''': test_validation['''train'''],
	        '''valid''': test_validation['''test'''],
	    } )

	print('''Loading tokenizer and model''' )
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained(args.model_ckpt )
	lowerCAmelCase__	:						Any  				=	tokenizer.eos_token
	lowerCAmelCase__	:						Tuple  				=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt						,     num_labels=7 )
	lowerCAmelCase__	:						Tuple  				=	model.config.eos_token_id

	if args.freeze:
		for param in model.roberta.parameters():
			lowerCAmelCase__	:						Optional[int]  				=	False

	lowerCAmelCase__	:						Union[str, Any]  				=	ClassLabel(num_classes=7						,     names=list(set(train_test_validation['''train''']['''complexity'''] ) ) )

	def tokenize(A_ ):
		lowerCAmelCase__	:						Dict  				=	tokenizer(example['''src''']						,     truncation=A_						,     max_length=10_24 )
		lowerCAmelCase__	:						str  				=	labels.straint(example['''complexity'''] )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	lowerCAmelCase__	:						Optional[int]  				=	train_test_validation.map(
	    A_						,     batched=A_						,     remove_columns=train_test_validation['''train'''].column_names						,     )
	lowerCAmelCase__	:						Optional[int]  				=	DataCollatorWithPadding(tokenizer=A_ )

	lowerCAmelCase__	:						Union[str, Any]  				=	TrainingArguments(
	    output_dir=args.output_dir						,     learning_rate=args.learning_rate						,     lr_scheduler_type=args.lr_scheduler_type						,     evaluation_strategy='''epoch'''						,     save_strategy='''epoch'''						,     logging_strategy='''epoch'''						,     per_device_train_batch_size=args.batch_size						,     per_device_eval_batch_size=args.batch_size						,     num_train_epochs=args.num_epochs						,     gradient_accumulation_steps=args.gradient_accumulation_steps						,     weight_decay=0.01						,     metric_for_best_model='''accuracy'''						,     run_name='''complexity-java'''						,     report_to='''wandb'''						,     )

	lowerCAmelCase__	:						Any  				=	Trainer(
	    model=A_						,     args=A_						,     train_dataset=tokenized_datasets['''train''']						,     eval_dataset=tokenized_datasets['''valid''']						,     tokenizer=A_						,     data_collator=A_						,     compute_metrics=A_						,     )

	print('''Training...''' )
	trainer.add_callback(CustomCallback(A_ ) )
	trainer.train()


if __name__ == "__main__":
							main()











class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "",		__magic_name__ = False							) ->      None:






       """simple docstring"""
       # Mapping from the first character of the prefix of the node
       UpperCamelCase__							:				dict[str, RadixNode]          =      {}

       # A node will be a leaf if the tree contains its word
       UpperCamelCase__							:				Optional[Any]          =      is_leaf

       UpperCamelCase__							:				List[str]          =      prefix




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      tuple[str, str, str]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      0
       for q, w in zip(self.prefix,		__magic_name__							):
           if q != w:
               break

           x += 1

       return self.prefix[:x], self.prefix[x:], word[x:]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       for word in words:
           self.insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # Case 1: If the word is the prefix of the node
       # Solution: We set the current node as leaf
       if self.prefix == word:
           UpperCamelCase__							:				Union[str, Any]          =      True

       # Case 2: The node has no edges that have a prefix to the word
       # Solution: We create an edge from the current node to a new one
       # containing the word
       elif word[0] not in self.nodes:
           UpperCamelCase__							:				Tuple          =      RadixNode(prefix=__magic_name__,		is_leaf=__magic_name__							)

       else:
           UpperCamelCase__							:				Any          =      self.nodes[word[0]]
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
               self.nodes[matching_string[0]].insert(__magic_name__							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
               UpperCamelCase__							:				Tuple          =      remaining_prefix

               UpperCamelCase__							:				Tuple          =      self.nodes[matching_string[0]]
               UpperCamelCase__							:				List[Any]          =      RadixNode(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				str          =      aux_node

               if remaining_word == "":
                   UpperCamelCase__							:				Any          =      True
               else:
                   self.nodes[matching_string[0]].insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
               return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
               return incoming_node.find(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
               return incoming_node.delete(__magic_name__							)
           else:
               # If it is not a leaf, we don't have to delete
               if not incoming_node.is_leaf:
                   return False
               else:
                   # We delete the nodes if no edges go from it
                   if len(incoming_node.nodes							) == 0:
                       del self.nodes[word[0]]
                       # We merge the current node with its only child
                       if len(self.nodes							) == 1 and not self.is_leaf:
                           UpperCamelCase__							:				Optional[Any]          =      list(self.nodes.values()							)[0]
                           UpperCamelCase__							:				Union[str, Any]          =      merging_node.is_leaf
                           self.prefix += merging_node.prefix
                           UpperCamelCase__							:				int          =      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                   elif len(incoming_node.nodes							) > 1:
                       UpperCamelCase__							:				Any          =      False
                   # If there is 1 edge, we merge it with its child
                   else:
                       UpperCamelCase__							:				Union[str, Any]          =      list(incoming_node.nodes.values()							)[0]
                       UpperCamelCase__							:				List[str]          =      merging_node.is_leaf
                       incoming_node.prefix += merging_node.prefix
                       UpperCamelCase__							:				int          =      merging_node.nodes

                   return True




   def       UpperCamelCase__  (    self,		__magic_name__ = 0							) ->      None:






       """simple docstring"""
       if self.prefix != "":
           print('''-''' * height,		self.prefix,		'''  (leaf)''' if self.is_leaf else ''''''							)

       for value in self.nodes.values():
           value.print_tree(height + 1							)




def 		lowerCAmelCase_    (  )      ->	bool:
    UpperCamelCase__							:				Optional[int]          =      '''banana bananas bandana band apple all beast'''.split()
    UpperCamelCase__							:				Optional[int]          =      RadixNode()
    root.insert_many(__UpperCAmelCase      )

    assert all(root.find(__UpperCAmelCase      ) for word in words      )
    assert not root.find('''bandanas'''      )
    assert not root.find('''apps'''      )
    root.delete('''all'''      )
    assert not root.find('''all'''      )
    root.delete('''banana'''      )
    assert not root.find('''banana'''      )
    assert root.find('''bananas'''      )

    return True




def 		lowerCAmelCase_    (  )      ->	None:
    assert test_trie()




def 		lowerCAmelCase_    (  )      ->	None:
    UpperCamelCase__							:				int          =      RadixNode()
    UpperCamelCase__							:				Any          =      '''banana bananas bandanas bandana band apple all beast'''.split()
    root.insert_many(__UpperCAmelCase      )

    print('''Words:'''		,   __UpperCAmelCase      )
    print('''Tree:'''      )
    root.print_tree()


if __name__ == "__main__":
  main()

