

'''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 			lowercase__			(					):




   '''simple docstring'''





   __lowercase							      =				argparse.ArgumentParser()
   parser.add_argument("""--model_ckpt"""	,							type=_UpperCamelCase	,							default="""microsoft/unixcoder-base-nine"""		)
   parser.add_argument("""--num_epochs"""	,							type=_UpperCamelCase	,							default=5		)
   parser.add_argument("""--batch_size"""	,							type=_UpperCamelCase	,							default=6		)
   parser.add_argument("""--gradient_accumulation_steps"""	,							type=_UpperCamelCase	,							default=1		)
   parser.add_argument("""--freeze"""	,							type=_UpperCamelCase	,							default=_UpperCamelCase		)
   parser.add_argument("""--learning_rate"""	,							type=_UpperCamelCase	,							default=5E-4		)
   parser.add_argument("""--seed"""	,							type=_UpperCamelCase	,							default=0		)
   parser.add_argument("""--lr_scheduler_type"""	,							type=_UpperCamelCase	,							default="""cosine"""		)
   parser.add_argument("""--num_warmup_steps"""	,							type=_UpperCamelCase	,							default=10		)
   parser.add_argument("""--weight_decay"""	,							type=_UpperCamelCase	,							default=0.01		)
   parser.add_argument("""--output_dir"""	,							type=_UpperCamelCase	,							default="""./results"""		)
   return parser.parse_args()


snake_case  : Optional[int]					    =	load('accuracy')







def 			lowercase__			(					__UpperCamelCase  :							Optional[int]		):




   '''simple docstring'''





   __lowercase      ,				__lowercase							      =				eval_pred
   __lowercase							      =				np.argmax(_UpperCamelCase	,							axis=1		)
   return metric.compute(predictions=_UpperCamelCase	,							references=_UpperCamelCase		)




class 					lowerCamelCase__(    __lowerCAmelCase		):

 def __init__(      self			,							__UpperCAmelCase	):



    """simple docstring"""







    super().__init__()
    __lowercase							      =				trainer




 def   __magic_name__						(      self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							**__UpperCAmelCase	):



    """simple docstring"""







    if control.should_evaluate:
       __lowercase							      =				deepcopy(_SCREAMING_SNAKE_CASE	)
       self._trainer.evaluate(eval_dataset=self._trainer.train_dataset			,							metric_key_prefix="""train"""	)
       return control_copy







def 			lowercase__			(					):




   '''simple docstring'''





   __lowercase							      =				get_args()
   set_seed(args.seed		)

   __lowercase							      =				load_dataset("""codeparrot/codecomplex"""	,							split="""train"""		)
   __lowercase							      =				dataset.train_test_split(test_size=0.2		)
   __lowercase							      =				train_test["""test"""].train_test_split(test_size=0.5		)
   __lowercase							      =				DatasetDict(
       {
           """train""": train_test["""train"""],
           """test""": test_validation["""train"""],
           """valid""": test_validation["""test"""],
       }		)

   print("""Loading tokenizer and model"""		)
   __lowercase							      =				AutoTokenizer.from_pretrained(args.model_ckpt		)
   __lowercase							      =				tokenizer.eos_token
   __lowercase							      =				AutoModelForSequenceClassification.from_pretrained(args.model_ckpt	,							num_labels=7		)
   __lowercase							      =				model.config.eos_token_id

   if args.freeze:
      for param in model.roberta.parameters():
         __lowercase							      =				False

   __lowercase							      =				ClassLabel(num_classes=7	,							names=list(set(train_test_validation["""train"""]["""complexity"""]		)		)		)

   def tokenize(__UpperCamelCase  :							List[str]		):
      __lowercase							      =				tokenizer(example["""src"""]	,							truncation=_UpperCamelCase	,							max_length=1024		)
      __lowercase							      =				labels.straint(example["""complexity"""]		)
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

   __lowercase							      =				train_test_validation.map(
       _UpperCamelCase	,							batched=_UpperCamelCase	,							remove_columns=train_test_validation["""train"""].column_names	,							)
   __lowercase							      =				DataCollatorWithPadding(tokenizer=_UpperCamelCase		)

   __lowercase							      =				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"""	,							)

   __lowercase							      =				Trainer(
       model=_UpperCamelCase	,							args=_UpperCamelCase	,							train_dataset=tokenized_datasets["""train"""]	,							eval_dataset=tokenized_datasets["""valid"""]	,							tokenizer=_UpperCamelCase	,							data_collator=_UpperCamelCase	,							compute_metrics=_UpperCamelCase	,							)

   print("""Training..."""		)
   trainer.add_callback(CustomCallback(_UpperCamelCase		)		)
   trainer.train()


if __name__ == "__main__":
    main()









'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_		:     Optional[int]          =		object()

# For specifying empty leaf dict `{}`
UpperCamelCase_		:     Optional[int]          =		object()





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= tuple((re.compile(x + "$"       ) for x in qs)       )
     for i in range(len(_UpperCamelCase       ) - len(_UpperCamelCase       ) + 1       ):
          _snake_case				= [x.match(_UpperCamelCase       ) for x, y in zip(_UpperCamelCase   ,		ks[i:]       )]
          if matches and all(_UpperCamelCase       ):
               return True
     return False





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     def replace(_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[str]       ):
          for rule, replacement in rules:
               if _match(_UpperCamelCase   ,		_UpperCamelCase       ):
                    return replacement
          return val

     return replace





def       __a      (  )		->					Any:

     """simple docstring"""




     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         (("transformer", "wte", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("attention", "out_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("mlp", "c_fc", "bias"), P("mp"       )),
         (("mlp", "c_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Any:

     """simple docstring"""




     _snake_case				= _get_partition_rules()
     _snake_case				= _replacement_rules(_UpperCamelCase       )
     _snake_case				= {k: _unmatched for k in flatten_dict(_UpperCamelCase       )}
     _snake_case				= {k: replace(_UpperCamelCase   ,		_UpperCamelCase       ) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(_UpperCamelCase       )       )


