


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 							_UpperCAmelCase				():
       _A      :       Union[str, Any]				    = 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()


lowerCAmelCase__      		= load('accuracy')
def 							_UpperCAmelCase				(UpperCamelCase__     :  int						):
       _A    ,			_A      :       List[Any]				    = eval_pred
       _A      :       Tuple				    = np.argmax(UpperCamelCase__				,							axis=1						)
       return metric.compute(predictions=UpperCamelCase__				,							references=UpperCamelCase__						)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase)			->				None:
            super().__init__()
            _A      :       Union[str, Any]				    = trainer


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if control.should_evaluate:
                   _A      :       Optional[Any]				    = deepcopy(__lowerCamelCase)
                   self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,						metric_key_prefix="train")
                   return control_copy




def 							_UpperCAmelCase				():
       _A      :       List[Any]				    = get_args()
       set_seed(args.seed						)

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

       print("Loading tokenizer and model"						)
       _A      :       List[str]				    = AutoTokenizer.from_pretrained(args.model_ckpt						)
       _A      :       Tuple				    = tokenizer.eos_token
       _A      :       Optional[int]				    = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,							num_labels=7						)
       _A      :       Union[str, Any]				    = model.config.eos_token_id

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

       _A      :       List[Any]				    = ClassLabel(num_classes=7				,							names=list(set(train_test_validation["train"]["complexity"]						)						)						)

       def tokenize(UpperCamelCase__     :  Any						):
              _A      :       Optional[int]				    = tokenizer(example["src"]				,							truncation=UpperCamelCase__				,							max_length=1024						)
              _A      :       str				    = labels.straint(example["complexity"]						)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       _A      :       Union[str, Any]				    = train_test_validation.map(
           UpperCamelCase__				,							batched=UpperCamelCase__				,							remove_columns=train_test_validation["train"].column_names				,							)
       _A      :       str				    = DataCollatorWithPadding(tokenizer=UpperCamelCase__						)

       _A      :       List[str]				    = 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"				,							)

       _A      :       str				    = 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()








from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
