"""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__      (     )   ->			Optional[Any]:





      """simple docstring"""






      snake_case        =     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=1_0   )
      parser.add_argument('--weight_decay'	,     type=lowerCAmelCase__	,     default=0.01   )
      parser.add_argument('--output_dir'	,     type=lowerCAmelCase__	,     default='./results'   )
      return parser.parse_args()


SCREAMING_SNAKE_CASE__   =					load("accuracy")





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





      """simple docstring"""






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



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          super().__init__()
          snake_case        =     trainer

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






          """simple docstring"""

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





def 				lowerCAmelCase__      (     )   ->			int:





      """simple docstring"""






      snake_case        =     get_args()
      set_seed(args.seed   )

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

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

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

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

      def tokenize(_UpperCamelCase    :			str   ):
            snake_case        =     tokenizer(example['src']	,     truncation=lowerCAmelCase__	,     max_length=1_0_2_4   )
            snake_case        =     labels.straint(example['complexity']   )
            return {
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "label": label,
            }

      snake_case        =     train_test_validation.map(
          lowerCAmelCase__	,     batched=lowerCAmelCase__	,     remove_columns=train_test_validation['train'].column_names	,     )
      snake_case        =     DataCollatorWithPadding(tokenizer=lowerCAmelCase__   )

      snake_case        =     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'	,     )

      snake_case        =     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()



"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

				@register_to_config
				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = 5_02_57						,      lowerCAmelCase = 10_24						,      lowerCAmelCase = 7_68						,      lowerCAmelCase = 12						,      lowerCAmelCase = 12						,      lowerCAmelCase = None						,      lowerCAmelCase = "gelu_new"						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 0.02						,      lowerCAmelCase = True						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      ):






										"""simple docstring"""

										super().__init__()

										snake_case        =     prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																raise ValueError(
																    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																    F""" `n_embd`: {n_embd} are not equal."""      )

										snake_case        =     prefix_inner_dim
										snake_case        =     prefix_hidden_dim

										snake_case        =     (
										    nn.Linear(self.prefix_inner_dim						,      self.prefix_hidden_dim      )
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										snake_case        =     (
										    nn.Linear(self.prefix_hidden_dim						,      lowerCAmelCase      ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										snake_case        =     GPTaConfig(
										    vocab_size=lowerCAmelCase						,      n_positions=lowerCAmelCase						,      n_embd=lowerCAmelCase						,      n_layer=lowerCAmelCase						,      n_head=lowerCAmelCase						,      n_inner=lowerCAmelCase						,      activation_function=lowerCAmelCase						,      resid_pdrop=lowerCAmelCase						,      embd_pdrop=lowerCAmelCase						,      attn_pdrop=lowerCAmelCase						,      layer_norm_epsilon=lowerCAmelCase						,      initializer_range=lowerCAmelCase						,      scale_attn_weights=lowerCAmelCase						,      use_cache=lowerCAmelCase						,      scale_attn_by_inverse_layer_idx=lowerCAmelCase						,      reorder_and_upcast_attn=lowerCAmelCase						,      )
										snake_case        =     GPTaLMHeadModel(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )
										snake_case        =     self.encode_prefix(lowerCAmelCase      )
										snake_case        =     self.decode_prefix(lowerCAmelCase      )
										snake_case        =     torch.cat((prefix_embeds, embedding_text)						,      dim=1      )

										if labels is not None:
																snake_case        =     self.get_dummy_token(input_ids.shape[0]						,      input_ids.device      )
																snake_case        =     torch.cat((dummy_token, input_ids)						,      dim=1      )
										snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase						,      labels=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										if self.prefix_hidden_dim is not None:
																return out, hidden
										else:
																return out

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return torch.zeros(lowerCAmelCase						,      self.prefix_length						,      dtype=torch.intaa						,      device=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.encode_prefix(lowerCAmelCase      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     torch.split(lowerCAmelCase						,      1						,      dim=0      )
										snake_case        =     []
										snake_case        =     []
										for feature in features:
																snake_case        =     self.decode_prefix(feature.to(lowerCAmelCase      )      )  # back to the clip feature
																# Only support beam search for now
																snake_case   ,snake_case        =     self.generate_beam(
																    input_embeds=lowerCAmelCase						,      device=lowerCAmelCase						,      eos_token_id=lowerCAmelCase      )
																generated_tokens.append(output_tokens[0]      )
																generated_seq_lengths.append(seq_lengths[0]      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										return generated_tokens, generated_seq_lengths

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = 5						,      lowerCAmelCase = 67						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     eos_token_id
										snake_case        =     None
										snake_case        =     None
										snake_case        =     torch.ones(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.int      )
										snake_case        =     torch.zeros(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.bool      )

										if input_embeds is not None:
																snake_case        =     input_embeds
										else:
																snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )

										for i in range(lowerCAmelCase      ):
																snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase      )
																snake_case        =     outputs.logits
																snake_case        =     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																snake_case        =     logits.softmax(-1      ).log()

																if scores is None:
																						snake_case   ,snake_case        =     logits.topk(lowerCAmelCase						,      -1      )
																						snake_case        =     generated.expand(lowerCAmelCase						,      *generated.shape[1:]      )
																						snake_case   ,snake_case        =     next_tokens.permute(1						,      0      ), scores.squeeze(0      )
																						if tokens is None:
																												snake_case        =     next_tokens
																						else:
																												snake_case        =     tokens.expand(lowerCAmelCase						,      *tokens.shape[1:]      )
																												snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																else:
																						snake_case        =     -float(np.inf      )
																						snake_case        =     0
																						snake_case        =     scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						snake_case        =     scores_sum / seq_lengths[:, None]
																						snake_case   ,snake_case        =     scores_sum_average.view(-1      ).topk(lowerCAmelCase						,      -1      )
																						snake_case        =     next_tokens // scores_sum.shape[1]
																						snake_case        =     seq_lengths[next_tokens_source]
																						snake_case        =     next_tokens % scores_sum.shape[1]
																						snake_case        =     next_tokens.unsqueeze(1      )
																						snake_case        =     tokens[next_tokens_source]
																						snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																						snake_case        =     generated[next_tokens_source]
																						snake_case        =     scores_sum_average * seq_lengths
																						snake_case        =     is_stopped[next_tokens_source]

																snake_case        =     self.transformer.transformer.wte(next_tokens.squeeze()      ).view(generated.shape[0]						,      1						,      -1      )
																snake_case        =     torch.cat((generated, next_token_embed)						,      dim=1      )
																snake_case        =     is_stopped + next_tokens.eq(lowerCAmelCase      ).squeeze()
																if is_stopped.all():
																						break

										snake_case        =     scores / seq_lengths
										snake_case        =     scores.argsort(descending=lowerCAmelCase      )
										# tokens tensors are already padded to max_seq_length
										snake_case        =     [tokens[i] for i in order]
										snake_case        =     torch.stack(lowerCAmelCase						,      dim=0      )
										snake_case        =     torch.tensor([seq_lengths[i] for i in order]						,      dtype=seq_lengths.dtype      )
										return output_texts, seq_lengths


