

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


UpperCAmelCase__ :     Union[str, Any]				    =    load("""accuracy""")






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Any       =   eval_pred
      SCREAMING_SNAKE_CASE :      List[Any]       =   np.argmax(_A						,    axis=1			)
      return metric.compute(predictions=_A						,    references=_A			)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  def __init__(			self    : Optional[int]				,				UpperCAmelCase__    : Dict		)					->None:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE :      Tuple       =   trainer




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : int				,				**UpperCAmelCase__    : Optional[Any]		)					->Dict:
        """simple docstring"""







        if control.should_evaluate:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   deepcopy(UpperCAmelCase__		)
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,				metric_key_prefix="""train"""		)
              return control_copy






def 					__lowercase      (     )     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   get_args()
      set_seed(args.seed			)

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

      print("""Loading tokenizer and model"""			)
      SCREAMING_SNAKE_CASE :      Dict       =   AutoTokenizer.from_pretrained(args.model_ckpt			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   tokenizer.eos_token
      SCREAMING_SNAKE_CASE :      Any       =   AutoModelForSequenceClassification.from_pretrained(args.model_ckpt						,    num_labels=7			)
      SCREAMING_SNAKE_CASE :      int       =   model.config.eos_token_id

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

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

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

      SCREAMING_SNAKE_CASE :      List[str]       =   train_test_validation.map(
          _A						,    batched=_A						,    remove_columns=train_test_validation["""train"""].column_names						,    )
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   DataCollatorWithPadding(tokenizer=_A			)

      SCREAMING_SNAKE_CASE :      List[str]       =   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 :      Dict       =   Trainer(
          model=_A						,    args=_A						,    train_dataset=tokenized_datasets["""train"""]						,    eval_dataset=tokenized_datasets["""valid"""]						,    tokenizer=_A						,    data_collator=_A						,    compute_metrics=_A						,    )

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


if __name__ == "__main__":
  main()







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




