





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  (	)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				str           =   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							:       Optional[Any]					 =	load("""accuracy""")






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    List[Any]:

						"""simple docstring"""





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





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   trainer






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




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






def 		lowerCAmelCase  (	)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				str           =   get_args()
						set_seed(args.seed					)

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

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

						if args.freeze:
												for param in model.roberta.parameters():
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   False

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

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

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   train_test_validation.map(
						    UpperCamelCase__       ,			batched=UpperCamelCase__       ,			remove_columns=train_test_validation['''train'''].column_names       ,			)
						__SCREAMING_SNAKE_CASE:				Tuple           =   DataCollatorWithPadding(tokenizer=UpperCamelCase__					)

						__SCREAMING_SNAKE_CASE:				Any           =   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'''       ,			)

						__SCREAMING_SNAKE_CASE:				List[str]           =   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 argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



