



'''simple docstring'''






import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						)

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  int	,    __snake_case    :  List[str]=14	,    __snake_case    :  List[Any]=7	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  List[Any]=True	,    __snake_case    :  Optional[int]=True	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  List[str]=True	,    __snake_case    :  Dict=99	,    __snake_case    :  Dict=32	,    __snake_case    :  Optional[Any]=5	,    __snake_case    :  Dict=4	,    __snake_case    :  Dict=37	,    __snake_case    :  str="gelu"	,    __snake_case    :  Tuple=0.1	,    __snake_case    :  Optional[Any]=0.1	,    __snake_case    :  Optional[Any]=512	,    __snake_case    :  Optional[Any]=16	,    __snake_case    :  str=2	,    __snake_case    :  Optional[int]=0.02	,    __snake_case    :  List[Any]=3	,    __snake_case    :  Tuple=4	,    __snake_case    :  Optional[int]=None	,    )    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	parent
								UpperCAmelCase				: Tuple											=	batch_size
								UpperCAmelCase				: str											=	seq_length
								UpperCAmelCase				: str											=	is_training
								UpperCAmelCase				: Dict											=	use_token_type_ids
								UpperCAmelCase				: str											=	use_input_mask
								UpperCAmelCase				: Optional[Any]											=	use_labels
								UpperCAmelCase				: List[str]											=	use_mc_token_ids
								UpperCAmelCase				: int											=	vocab_size
								UpperCAmelCase				: str											=	hidden_size
								UpperCAmelCase				: str											=	num_hidden_layers
								UpperCAmelCase				: Tuple											=	num_attention_heads
								UpperCAmelCase				: Optional[int]											=	intermediate_size
								UpperCAmelCase				: Union[str, Any]											=	hidden_act
								UpperCAmelCase				: Dict											=	hidden_dropout_prob
								UpperCAmelCase				: Optional[int]											=	attention_probs_dropout_prob
								UpperCAmelCase				: List[Any]											=	max_position_embeddings
								UpperCAmelCase				: Dict											=	type_vocab_size
								UpperCAmelCase				: Tuple											=	type_sequence_label_size
								UpperCAmelCase				: List[Any]											=	initializer_range
								UpperCAmelCase				: Any											=	num_labels
								UpperCAmelCase				: Union[str, Any]											=	num_choices
								UpperCAmelCase				: List[str]											=	scope
								UpperCAmelCase				: List[str]											=	self.vocab_size - 1







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: int											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								UpperCAmelCase				: Union[str, Any]											=	None
								if self.use_input_mask:
															UpperCAmelCase				: Tuple											=	random_attention_mask([self.batch_size, self.seq_length]	)

								UpperCAmelCase				: Optional[int]											=	None
								if self.use_token_type_ids:
															UpperCAmelCase				: Tuple											=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size	)

								UpperCAmelCase				: Tuple											=	None
								if self.use_mc_token_ids:
															UpperCAmelCase				: str											=	ids_tensor([self.batch_size, self.num_choices]	,    self.seq_length	)

								UpperCAmelCase				: Optional[int]											=	None
								UpperCAmelCase				: int											=	None
								UpperCAmelCase				: List[Any]											=	None
								if self.use_labels:
															UpperCAmelCase				: List[Any]											=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size	)
															UpperCAmelCase				: Dict											=	ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels	)
															UpperCAmelCase				: List[str]											=	ids_tensor([self.batch_size]	,    self.num_choices	)

								UpperCAmelCase				: Any											=	self.get_config()

								UpperCAmelCase				: Tuple											=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,    2	)

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)







	def   A			(		self    :  Any	)    ->	List[Any]:
								return CTRLConfig(
								    vocab_size=self.vocab_size	,    n_embd=self.hidden_size	,    n_layer=self.num_hidden_layers	,    n_head=self.num_attention_heads	,    n_positions=self.max_position_embeddings	,    pad_token_id=self.pad_token_id	,    )







	def   A			(		self    :  Optional[int]	,    __snake_case    :  List[str]	,    __snake_case    :  str	,    __snake_case    :  Any	,    __snake_case    :  str	,    __snake_case    :  Optional[int]	,    *__snake_case    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: List[Any]											=	CTRLModel(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()

								model(__snake_case	,    token_type_ids=__snake_case	,    head_mask=__snake_case	)
								model(__snake_case	,    token_type_ids=__snake_case	)
								UpperCAmelCase				: str											=	model(__snake_case	)
								self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)
								self.parent.assertEqual(len(result.past_key_values	)	,    config.n_layer	)







	def   A			(		self    :  int	,    __snake_case    :  str	,    __snake_case    :  Dict	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Any	,    __snake_case    :  int	,    *__snake_case    :  Union[str, Any]	)    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	CTRLLMHeadModel(__snake_case	)
								model.to(__snake_case	)
								model.eval()

								UpperCAmelCase				: int											=	model(__snake_case	,    token_type_ids=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.loss.shape	,    ()	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







	def   A			(		self    :  str	)    ->	Dict:
								UpperCAmelCase				: List[str]											=	self.prepare_config_and_inputs()

								(
								    (
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	(
								    UpperCAmelCase
								)							,	
								)				: Any											=	config_and_inputs

								UpperCAmelCase				: Optional[Any]											=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Dict	,    __snake_case    :  int	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	,    *__snake_case    :  List[Any]	)    ->	Dict:
								UpperCAmelCase				: List[str]											=	self.num_labels
								UpperCAmelCase				: Dict											=	CTRLForSequenceClassification(__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Tuple											=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size	)
								UpperCAmelCase				: Tuple											=	model(__snake_case	,    token_type_ids=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)	)

@require_torch
class        SCREAMING_SNAKE_CASE(							A__							,	A__							,	A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	lowerCamelCase__    				= (CTRLLMHeadModel,) if is_torch_available() else ()
	lowerCamelCase__    				= (
	    {
	        """feature-extraction""": CTRLModel,
	        """text-classification""": CTRLForSequenceClassification,
	        """text-generation""": CTRLLMHeadModel,
	        """zero-shot""": CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCamelCase__    				= True
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False







	def   A			(		self    :  Optional[int]	,    __snake_case    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[int]	)    ->	Optional[Any]:
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
															# config could not be created.
															return True

								return False







	def   A			(		self    :  int	)    ->	str:
								UpperCAmelCase				: List[str]											=	CTRLModelTester(self	)
								UpperCAmelCase				: List[str]											=	ConfigTester(self	,    config_class=__snake_case	,    n_embd=37	)







	def   A			(		self    :  Dict	)    ->	int:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()







	def   A			(		self    :  Optional[int]	)    ->	Optional[int]:
								self.config_tester.run_common_tests()







	def   A			(		self    :  Optional[Any]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Union[str, Any]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*__snake_case	)







	def   A			(		self    :  Tuple	)    ->	List[Any]:
								UpperCAmelCase				: Optional[int]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*__snake_case	)







	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
	def   A			(		self    :  Optional[Any]	)    ->	Optional[int]:
								pass







	@slow
	def   A			(		self    :  Optional[int]	)    ->	int:
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase				: Optional[Any]											=	CTRLModel.from_pretrained(__snake_case	)
															self.assertIsNotNone(__snake_case	)







	@unittest.skip('''The model doesn\'t support left padding'''	)  # and it's not used enough to be worth fixing :)
	def   A			(		self    :  Dict	)    ->	str:
								pass

@require_torch
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Any	)    ->	List[str]:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()







	@slow
	def   A			(		self    :  List[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Optional[int]											=	CTRLLMHeadModel.from_pretrained('''ctrl'''	)
								model.to(__snake_case	)
								UpperCAmelCase				: List[str]											=	torch.tensor(
								    [[11859, 0, 1611, 8]]	,    dtype=torch.long	,    device=__snake_case	)  # Legal the president is
								UpperCAmelCase				: Optional[Any]											=	[
								    11859,
								    0,
								    1611,
								    8,
								    5,
								    150,
								    26449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2595,
								    48,
								    20740,
								    246533,
								    246533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								UpperCAmelCase				: Dict											=	model.generate(__snake_case	,    do_sample=__snake_case	)
								self.assertListEqual(output_ids[0].tolist()	,    __snake_case	)










'''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 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	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=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


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

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

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

							if args.freeze:
														for param in model.roberta.parameters():
																					UpperCAmelCase				: Any											=	False

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

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

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

							UpperCAmelCase				: Tuple											=	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=0.0_1     ,   metric_for_best_model='''accuracy'''     ,   run_name='''complexity-java'''     ,   report_to='''wandb'''     ,   )

							UpperCAmelCase				: int											=	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()





