





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	tempfile.mkdtemp()

          # fmt: off
          __A						:			str								=	['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          __A						:			int								=	dict(zip(_UpperCAmelCase						,     range(len(_UpperCAmelCase))))
          __A						:			Dict								=	['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          __A						:			List[str]								=	{'unk_token': '<unk>'}

          __A						:			Dict								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['vocab_file'])
          __A						:			Optional[int]								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file						,     'w'						,     encoding='utf-8') as fp:
               fp.write(json.dumps(_UpperCAmelCase) + '\n')
          with open(self.merges_file						,     'w'						,     encoding='utf-8') as fp:
               fp.write('\n'.join(_UpperCAmelCase))

          __A						:			List[Any]								=	{
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48145466, 0.4578275, 0.40821073],
              'image_std': [0.26862954, 0.26130258, 0.27577711],
          }
          __A						:			Optional[Any]								=	os.path.join(self.tmpdirname						,     _UpperCAmelCase)
          with open(self.image_processor_file						,     'w'						,     encoding='utf-8') as fp:
               json.dump(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizer.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return CLIPImageProcessor.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	[np.random.randint(255						,     size=(3, 30, 400)						,     dtype=np.uinta)]

          __A						:			str								=	[Image.fromarray(np.moveaxis(_UpperCAmelCase						,     0						,     -1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.get_tokenizer()
          __A						:			Dict								=	self.get_rust_tokenizer()
          __A						:			Optional[int]								=	self.get_image_processor()

          __A						:			str								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)
          processor_slow.save_pretrained(self.tmpdirname)
          __A						:			int								=	CLIPProcessor.from_pretrained(self.tmpdirname						,     use_fast=_UpperCAmelCase)

          __A						:			int								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          __A						:			Union[str, Any]								=	CLIPProcessor.from_pretrained(self.tmpdirname)

          self.assertEqual(processor_slow.tokenizer.get_vocab()						,     tokenizer_slow.get_vocab())
          self.assertEqual(processor_fast.tokenizer.get_vocab()						,     tokenizer_fast.get_vocab())
          self.assertEqual(tokenizer_slow.get_vocab()						,     tokenizer_fast.get_vocab())
          self.assertIsInstance(processor_slow.tokenizer						,     _UpperCAmelCase)
          self.assertIsInstance(processor_fast.tokenizer						,     _UpperCAmelCase)

          self.assertEqual(processor_slow.image_processor.to_json_string()						,     image_processor.to_json_string())
          self.assertEqual(processor_fast.image_processor.to_json_string()						,     image_processor.to_json_string())
          self.assertIsInstance(processor_slow.image_processor						,     _UpperCAmelCase)
          self.assertIsInstance(processor_fast.image_processor						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	CLIPProcessor(tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						:			Tuple								=	self.get_tokenizer(bos_token='(BOS)'						,     eos_token='(EOS)')
          __A						:			Optional[Any]								=	self.get_image_processor(do_normalize=_UpperCAmelCase						,     padding_value=1.0)

          __A						:			str								=	CLIPProcessor.from_pretrained(
              self.tmpdirname						,     bos_token='(BOS)'						,     eos_token='(EOS)'						,     do_normalize=_UpperCAmelCase						,     padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,     tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,     _UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,     image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.get_image_processor()
          __A						:			List[Any]								=	self.get_tokenizer()

          __A						:			int								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Tuple								=	self.prepare_image_inputs()

          __A						:			str								=	image_processor(_UpperCAmelCase						,     return_tensors='np')
          __A						:			str								=	processor(images=_UpperCAmelCase						,     return_tensors='np')

          for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()						,     input_processor[key].sum()						,     delta=1e-2)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.get_image_processor()
          __A						:			Union[str, Any]								=	self.get_tokenizer()

          __A						:			Any								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Dict								=	'lower newer'

          __A						:			Union[str, Any]								=	processor(text=_UpperCAmelCase)

          __A						:			Optional[Any]								=	tokenizer(_UpperCAmelCase)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]						,     encoded_processor[key])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.get_image_processor()
          __A						:			Any								=	self.get_tokenizer()

          __A						:			Tuple								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Optional[Any]								=	'lower newer'
          __A						:			Optional[int]								=	self.prepare_image_inputs()

          __A						:			Dict								=	processor(text=_UpperCAmelCase						,     images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,     ['input_ids', 'attention_mask', 'pixel_values'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.get_image_processor()
          __A						:			int								=	self.get_tokenizer()

          __A						:			Optional[int]								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Any								=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						:			Tuple								=	processor.batch_decode(_UpperCAmelCase)
          __A						:			Optional[int]								=	tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.get_image_processor()
          __A						:			List[str]								=	self.get_tokenizer()

          __A						:			List[Any]								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Tuple								=	'lower newer'
          __A						:			Optional[Any]								=	self.prepare_image_inputs()

          __A						:			Dict								=	processor(text=_UpperCAmelCase						,     images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,     processor.model_input_names)






'''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()