



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


_UpperCamelCase											=		load('accuracy')

def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			,__lowerCamelCase			:			List[str]       			=					eval_pred
  __lowerCamelCase			:			List[Any]       			=					np.argmax(_lowerCAmelCase			,axis=1			)
  return metric.compute(predictions=_lowerCAmelCase			,references=_lowerCAmelCase			)






class    lowerCamelCase_       (							__lowerCamelCase	):
  """simple docstring"""




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




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple     ,      _a :      List[str]     ,      _a :      Optional[int]     ,      **_a :      Union[str, Any]  )   ->   Optional[Any]:






    if control.should_evaluate:
      __lowerCamelCase			:			List[Any]       			=					deepcopy(_a  )
      self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,      metric_key_prefix='train'  )
      return control_copy




def 			a_	(				) -> List[Any]:
  __lowerCamelCase			:			Union[str, Any]       			=					get_args()
  set_seed(args.seed			)

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

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

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

  __lowerCamelCase			:			Optional[Any]       			=					ClassLabel(num_classes=7			,names=list(set(train_test_validation['train']['complexity']			)			)			)

  def tokenize(_lowerCAmelCase			):
    __lowerCamelCase			:			Union[str, Any]       			=					tokenizer(example['src']			,truncation=_lowerCAmelCase			,max_length=1024			)
    __lowerCamelCase			:			Union[str, Any]       			=					labels.straint(example['complexity']			)
    return {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "label": label,
    }

  __lowerCamelCase			:			Optional[int]       			=					train_test_validation.map(
      _lowerCAmelCase			,batched=_lowerCAmelCase			,remove_columns=train_test_validation['train'].column_names			,)
  __lowerCamelCase			:			List[str]       			=					DataCollatorWithPadding(tokenizer=_lowerCAmelCase			)

  __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			:			Dict       			=					Trainer(
      model=_lowerCAmelCase			,args=_lowerCAmelCase			,train_dataset=tokenized_datasets['train']			,eval_dataset=tokenized_datasets['valid']			,tokenizer=_lowerCAmelCase			,data_collator=_lowerCAmelCase			,compute_metrics=_lowerCAmelCase			,)

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


if __name__ == "__main__":
   main()








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase			)







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				self.check_over_configs(thresholding=lowercase			)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowercase   ,     prediction_type=lowercase   ,     sample_max_value=lowercase   ,     )







	def  snake_case__     (     self      :Union[str, Any]			)		->      List[Any]:







				"""simple docstring"""






				for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase			)







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

				with self.assertRaises(lowercase   ,     msg='''`custom_timesteps` must be in descending order.'''			):
							scheduler.set_timesteps(timesteps=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				with self.assertRaises(lowercase   ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''			):
							scheduler.set_timesteps(num_inference_steps=lowercase   ,     timesteps=lowercase			)







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    lowercase   ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,     ):
							scheduler.set_timesteps(timesteps=lowercase			)