


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      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      argparse.ArgumentParser()
     parser.add_argument('''--model_ckpt'''      , type=_lowercase      , default='''microsoft/unixcoder-base-nine'''  )
     parser.add_argument('''--num_epochs'''      , type=_lowercase      , default=5  )
     parser.add_argument('''--batch_size'''      , type=_lowercase      , default=6  )
     parser.add_argument('''--gradient_accumulation_steps'''      , type=_lowercase      , default=1  )
     parser.add_argument('''--freeze'''      , type=_lowercase      , default=_lowercase  )
     parser.add_argument('''--learning_rate'''      , type=_lowercase      , default=5E-4  )
     parser.add_argument('''--seed'''      , type=_lowercase      , default=0  )
     parser.add_argument('''--lr_scheduler_type'''      , type=_lowercase      , default='''cosine'''  )
     parser.add_argument('''--num_warmup_steps'''      , type=_lowercase      , default=10  )
     parser.add_argument('''--weight_decay'''      , type=_lowercase      , default=0.01  )
     parser.add_argument('''--output_dir'''      , type=_lowercase      , default='''./results'''  )
     return parser.parse_args()


__UpperCAmelCase      :str			    =       load("accuracy")




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      eval_pred
     __UpperCAmelCase     :			Any  							=      np.argmax(_lowercase      , axis=1  )
     return metric.compute(predictions=_lowercase      , references=_lowercase  )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		str      )						->							None:
          super().__init__()
          __UpperCAmelCase     :			Optional[Any]  							=      trainer






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




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      get_args()
     set_seed(args.seed  )

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

     print('''Loading tokenizer and model'''  )
     __UpperCAmelCase     :			List[str]  							=      AutoTokenizer.from_pretrained(args.model_ckpt  )
     __UpperCAmelCase     :			str  							=      tokenizer.eos_token
     __UpperCAmelCase     :			Dict  							=      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     :			List[str]  							=      False

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

     def tokenize(_lowercase :     Union[str, Any]  ):
          __UpperCAmelCase     :			Optional[int]  							=      tokenizer(example['''src''']      , truncation=_lowercase      , max_length=1024  )
          __UpperCAmelCase     :			Union[str, Any]  							=      labels.straint(example['''complexity''']  )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __UpperCAmelCase     :			Tuple  							=      train_test_validation.map(
         _lowercase      , batched=_lowercase      , remove_columns=train_test_validation['''train'''].column_names      , )
     __UpperCAmelCase     :			Optional[Any]  							=      DataCollatorWithPadding(tokenizer=_lowercase  )

     __UpperCAmelCase     :			Optional[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'''      , )

     __UpperCAmelCase     :			Tuple  							=      Trainer(
         model=_lowercase      , args=_lowercase      , train_dataset=tokenized_datasets['''train''']      , eval_dataset=tokenized_datasets['''valid''']      , tokenizer=_lowercase      , data_collator=_lowercase      , compute_metrics=_lowercase      , )

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


if __name__ == "__main__":
      main()



'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__UpperCAmelCase      :Tuple			    =       "Create a default config file for Accelerate with only a few flags set."




def      _a     (						_lowercase :     List[Any]="no"      , _lowercase :     str = default_json_config_file      , _lowercase :     bool = False  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      Path(_lowercase  )
     path.parent.mkdir(parents=_lowercase      , exist_ok=_lowercase  )
     if path.exists():
          print(
              F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'  )
          return False
     __UpperCAmelCase     :			List[str]  							=      mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'  )
     __UpperCAmelCase     :			int  							=      {
         '''compute_environment''': '''LOCAL_MACHINE''',
         '''mixed_precision''': mixed_precision,
     }
     if torch.cuda.is_available():
          __UpperCAmelCase     :			Optional[Any]  							=      torch.cuda.device_count()
          __UpperCAmelCase     :			List[str]  							=      num_gpus
          __UpperCAmelCase     :			int  							=      False
          if num_gpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_GPU'''
          else:
               __UpperCAmelCase     :			int  							=      '''NO'''
     elif is_xpu_available() and use_xpu:
          __UpperCAmelCase     :			List[Any]  							=      torch.xpu.device_count()
          __UpperCAmelCase     :			List[Any]  							=      num_xpus
          __UpperCAmelCase     :			Optional[int]  							=      False
          if num_xpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_XPU'''
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      '''NO'''
     elif is_npu_available():
          __UpperCAmelCase     :			Dict  							=      torch.npu.device_count()
          __UpperCAmelCase     :			Any  							=      num_npus
          __UpperCAmelCase     :			Any  							=      False
          if num_npus > 1:
               __UpperCAmelCase     :			Dict  							=      '''MULTI_NPU'''
          else:
               __UpperCAmelCase     :			Optional[int]  							=      '''NO'''
     else:
          __UpperCAmelCase     :			List[str]  							=      0
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			Dict  							=      1
          __UpperCAmelCase     :			Tuple  							=      '''NO'''
     __UpperCAmelCase     :			List[Any]  							=      ClusterConfig(**_lowercase  )
     config.to_json_file(_lowercase  )
     return path




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      parser.add_parser('''default'''      , parents=_lowercase      , help=_lowercase      , formatter_class=_lowercase  )
     parser.add_argument(
         '''--config_file'''      , default=_lowercase      , help=(
             '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
             '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
             '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
             '''with \'huggingface\'.'''
         )      , dest='''save_location'''      , )

     parser.add_argument(
         '''--mixed_precision'''      , choices=['''no''', '''fp16''', '''bf16''']      , type=_lowercase      , help='''Whether or not to use mixed precision training. '''
         '''Choose between FP16 and BF16 (bfloat16) training. '''
         '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''      , default='''no'''      , )
     parser.set_defaults(func=_lowercase  )
     return parser




def      _a     (						_lowercase :     List[Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      write_basic_config(args.mixed_precision      , args.save_location  )
     if config_file:
          print(F'accelerate configuration saved at {config_file}'  )