





'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						:			Union[str, Any]								=	UNetaDModel(
              block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,     )
          return model


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.dummy_uncond_unet
          __A						:			Optional[Any]								=	KarrasVeScheduler()

          __A						:			List[str]								=	KarrasVePipeline(unet=_UpperCAmelCase						,     scheduler=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			Optional[Any]								=	torch.manual_seed(0)
          __A						:			int								=	pipe(num_inference_steps=2						,     generator=_UpperCAmelCase						,     output_type='numpy').images

          __A						:			List[Any]								=	torch.manual_seed(0)
          __A						:			Optional[int]								=	pipe(num_inference_steps=2						,     generator=_UpperCAmelCase						,     output_type='numpy'						,     return_dict=_UpperCAmelCase)[0]

          __A						:			Any								=	image[0, -3:, -3:, -1]
          __A						:			Optional[Any]								=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __A						:			Union[str, Any]								=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	'google/ncsnpp-celebahq-256'
          __A						:			Optional[Any]								=	UNetaDModel.from_pretrained(_UpperCAmelCase)
          __A						:			List[Any]								=	KarrasVeScheduler()

          __A						:			Optional[int]								=	KarrasVePipeline(unet=_UpperCAmelCase						,     scheduler=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			Optional[int]								=	torch.manual_seed(0)
          __A						:			Optional[int]								=	pipe(num_inference_steps=20						,     generator=_UpperCAmelCase						,     output_type='numpy').images

          __A						:			List[Any]								=	image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          __A						:			int								=	np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






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


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
     __A			,__A						:			List[Any]								=	eval_pred
     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						:			str								=	deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix='train')
               return control_copy






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			List[Any]								=	get_args()
     set_seed(args.seed      )

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

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

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

     __A						:			Dict								=	ClassLabel(num_classes=7			,							names=list(set(train_test_validation['train']['complexity']      )      )      )

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

     __A						:			str								=	train_test_validation.map(
         __snake_case			,							batched=__snake_case			,							remove_columns=train_test_validation['train'].column_names			,							)
     __A						:			str								=	DataCollatorWithPadding(tokenizer=__snake_case      )

     __A						:			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						:			Tuple								=	Trainer(
         model=__snake_case			,							args=__snake_case			,							train_dataset=tokenized_datasets['train']			,							eval_dataset=tokenized_datasets['valid']			,							tokenizer=__snake_case			,							data_collator=__snake_case			,							compute_metrics=__snake_case			,							)

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


if __name__ == "__main__":
  main()