

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        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	__lowerCamelCase    :    int 	=			argparse.ArgumentParser()
	parser.add_argument('--model_ckpt'     ,			type=A__     ,			default='microsoft/unixcoder-base-nine'      )
	parser.add_argument('--num_epochs'     ,			type=A__     ,			default=5      )
	parser.add_argument('--batch_size'     ,			type=A__     ,			default=6      )
	parser.add_argument('--gradient_accumulation_steps'     ,			type=A__     ,			default=1      )
	parser.add_argument('--freeze'     ,			type=A__     ,			default=A__      )
	parser.add_argument('--learning_rate'     ,			type=A__     ,			default=5e-4      )
	parser.add_argument('--seed'     ,			type=A__     ,			default=0      )
	parser.add_argument('--lr_scheduler_type'     ,			type=A__     ,			default='cosine'      )
	parser.add_argument('--num_warmup_steps'     ,			type=A__     ,			default=1_0      )
	parser.add_argument('--weight_decay'     ,			type=A__     ,			default=0.01      )
	parser.add_argument('--output_dir'     ,			type=A__     ,			default='./results'      )
	return parser.parse_args()


a		=load("""accuracy""")






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






class 	A_		(	__lowerCamelCase    ):
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__()
								__lowerCamelCase    :    Optional[int] 	=			trainer


							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Any       ,**SCREAMING_SNAKE_CASE__		:      int):
								if control.should_evaluate:
									__lowerCamelCase    :    int 	=			deepcopy(UpperCamelCase_)
									self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,metric_key_prefix='train')
									return control_copy






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

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

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

	if args.freeze:
		for param in model.roberta.parameters():
			__lowerCamelCase    :    Any 	=			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=A__     ,			max_length=1_0_2_4      )
		__lowerCamelCase    :    Tuple 	=			labels.straint(example['complexity']      )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

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

	__lowerCamelCase    :    Optional[int] 	=			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    :    str 	=			Trainer(
	    model=A__     ,			args=A__     ,			train_dataset=tokenized_datasets['train']     ,			eval_dataset=tokenized_datasets['valid']     ,			tokenizer=A__     ,			data_collator=A__     ,			compute_metrics=A__     ,			)

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


if __name__ == "__main__":
					main()







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


a		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

													__lowerCamelCase    :    List[Any] 	=			tf.cast(input_ids_dense > 0       ,tf.intaa)
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    'This is a straightforward English test sentence.',
								    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
								    'Now we\'re going to add some Chinese: 一 二 三 一二三',
								    'And some much more rare Chinese: 齉 堃 齉堃',
								    'Je vais aussi écrire en français pour tester les accents',
								    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
								]
								__lowerCamelCase    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[Any] 	=			tf_outputs[key].numpy()

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
											self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			loaded_model.signatures['serving_default'](SCREAMING_SNAKE_CASE__)['output_0']
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output))
							@slow
							def    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

									for key in from_config_output.keys():
										self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    List[Any] 	=			out['input_ids'].numpy().shape[1]

										assert out_length == max_length




