
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 						__magic_name__ (    ):



     '''simple docstring'''




     UpperCamelCase__				=				argparse.ArgumentParser()
     parser.add_argument("""--model_ckpt"""       ,		type=UpperCamelCase__       ,		default="""microsoft/unixcoder-base-nine"""						)
     parser.add_argument("""--num_epochs"""       ,		type=UpperCamelCase__       ,		default=5						)
     parser.add_argument("""--batch_size"""       ,		type=UpperCamelCase__       ,		default=6						)
     parser.add_argument("""--gradient_accumulation_steps"""       ,		type=UpperCamelCase__       ,		default=1						)
     parser.add_argument("""--freeze"""       ,		type=UpperCamelCase__       ,		default=UpperCamelCase__						)
     parser.add_argument("""--learning_rate"""       ,		type=UpperCamelCase__       ,		default=5E-4						)
     parser.add_argument("""--seed"""       ,		type=UpperCamelCase__       ,		default=0						)
     parser.add_argument("""--lr_scheduler_type"""       ,		type=UpperCamelCase__       ,		default="""cosine"""						)
     parser.add_argument("""--num_warmup_steps"""       ,		type=UpperCamelCase__       ,		default=10						)
     parser.add_argument("""--weight_decay"""       ,		type=UpperCamelCase__       ,		default=0.01						)
     parser.add_argument("""--output_dir"""       ,		type=UpperCamelCase__       ,		default="""./results"""						)
     return parser.parse_args()


lowerCamelCase_							=     load('''accuracy''')






def 						__magic_name__ (    __a			:       Dict						):



     '''simple docstring'''




     UpperCamelCase__		,       UpperCamelCase__				=				eval_pred
     UpperCamelCase__				=				np.argmax(UpperCamelCase__       ,		axis=1						)
     return metric.compute(predictions=UpperCamelCase__       ,		references=UpperCamelCase__						)




class 	__A(				a_ ):

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_						):
       super().__init__()
       UpperCamelCase__				=				trainer







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):







       if control.should_evaluate:
            UpperCamelCase__				=				deepcopy(_A						)
            self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix="""train"""						)
            return control_copy






def 						__magic_name__ (    ):



     '''simple docstring'''




     UpperCamelCase__				=				get_args()
     set_seed(args.seed						)

     UpperCamelCase__				=				load_dataset("""codeparrot/codecomplex"""       ,		split="""train"""						)
     UpperCamelCase__				=				dataset.train_test_split(test_size=0.2						)
     UpperCamelCase__				=				train_test["""test"""].train_test_split(test_size=0.5						)
     UpperCamelCase__				=				DatasetDict(
         {
             """train""": train_test["""train"""],
             """test""": test_validation["""train"""],
             """valid""": test_validation["""test"""],
         }						)

     print("""Loading tokenizer and model"""						)
     UpperCamelCase__				=				AutoTokenizer.from_pretrained(args.model_ckpt						)
     UpperCamelCase__				=				tokenizer.eos_token
     UpperCamelCase__				=				AutoModelForSequenceClassification.from_pretrained(args.model_ckpt       ,		num_labels=7						)
     UpperCamelCase__				=				model.config.eos_token_id

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

     UpperCamelCase__				=				ClassLabel(num_classes=7       ,		names=list(set(train_test_validation["""train"""]["""complexity"""]						)						)						)

     def tokenize(__a			:       Any						):
          UpperCamelCase__				=				tokenizer(example["""src"""]       ,		truncation=UpperCamelCase__       ,		max_length=1_024						)
          UpperCamelCase__				=				labels.straint(example["""complexity"""]						)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     UpperCamelCase__				=				train_test_validation.map(
         UpperCamelCase__       ,		batched=UpperCamelCase__       ,		remove_columns=train_test_validation["""train"""].column_names       ,		)
     UpperCamelCase__				=				DataCollatorWithPadding(tokenizer=UpperCamelCase__						)

     UpperCamelCase__				=				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"""       ,		)

     UpperCamelCase__				=				Trainer(
         model=UpperCamelCase__       ,		args=UpperCamelCase__       ,		train_dataset=tokenized_datasets["""train"""]       ,		eval_dataset=tokenized_datasets["""valid"""]       ,		tokenizer=UpperCamelCase__       ,		data_collator=UpperCamelCase__       ,		compute_metrics=UpperCamelCase__       ,		)

     print("""Training..."""						)
     trainer.add_callback(CustomCallback(UpperCamelCase__						)						)
     trainer.train()


if __name__ == "__main__":
    main()












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 	A_       :
	def __init__(    self							,					_A							,					_A=1_4							,					_A=7							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=9_9							,					_A=3_2							,					_A=5							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=5_1_2							,					_A=1_6							,					_A=2							,					_A=0.02							,					_A=3							,					_A=4							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_token_type_ids
				UpperCAmelCase   		=				use_input_mask
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				use_mc_token_ids
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				type_sequence_label_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				num_labels
				UpperCAmelCase   		=				num_choices
				UpperCAmelCase   		=				scope
				UpperCAmelCase   		=				self.vocab_size - 1
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size		)

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

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

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

				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.num_choices		)

				UpperCAmelCase   		=				self.get_config()

				UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







				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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModel(config=_A		)
				model.to(_A		)
				model.eval()

				model(_A							,					token_type_ids=_A							,					head_mask=_A		)
				model(_A							,					token_type_ids=_A		)
				UpperCAmelCase   		=				model(_A		)
				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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel(_A		)
				model.to(_A		)
				model.eval()

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






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()

				(
				    (
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			
				)   		=				config_and_inputs

				UpperCAmelCase   		=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict







	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.num_labels
				UpperCAmelCase   		=				CTRLForSequenceClassification(_A		)
				model.to(_A		)
				model.eval()
				UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
				UpperCAmelCase   		=				model(_A							,					token_type_ids=_A							,					labels=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)		)

@require_torch
class 	A_       (a_     ,							a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	UpperCAmelCase__					=			(CTRLLMHeadModel,) if is_torch_available() else ()
	UpperCAmelCase__					=			(
	    {
	        '''feature-extraction''': CTRLModel,
	        '''text-classification''': CTRLForSequenceClassification,
	        '''text-generation''': CTRLLMHeadModel,
	        '''zero-shot''': CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					n_embd=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







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






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_A		)
	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase   		=				CTRLModel.from_pretrained(_A		)
							self.assertIsNotNone(_A		)







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






				'''simple docstring'''







				pass

@require_torch
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel.from_pretrained('''ctrl'''		)
				model.to(_A		)
				UpperCAmelCase   		=				torch.tensor(
				    [[1_1_8_5_9, 0, 1_6_1_1, 8]]							,					dtype=torch.long							,					device=_A		)  # Legal the president is
				UpperCAmelCase   		=				[
				    1_1_8_5_9,
				    0,
				    1_6_1_1,
				    8,
				    5,
				    1_5_0,
				    2_6_4_4_9,
				    2,
				    1_9,
				    3_4_8,
				    4_6_9,
				    3,
				    2_5_9_5,
				    4_8,
				    2_0_7_4_0,
				    2_4_6_5_3_3,
				    2_4_6_5_3_3,
				    1_9,
				    3_0,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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



