





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  (      ):
   a__	:						int      		=  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()


SCREAMING_SNAKE_CASE__       :			Dict			      =						load("""accuracy""")






def       _A  (      lowerCamelCase   ):
   a__		,     a__	:						Tuple      		=  eval_pred
   a__	:						int      		=  np.argmax(UpperCamelCase__							, axis=1   )
   return metric.compute(predictions=UpperCamelCase__							, references=UpperCamelCase__   )







class   __lowerCAmelCase			(					UpperCAmelCase_  ):





   def __init__( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      super().__init__()
      a__	:						Optional[int]      		=  trainer





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			**snake_case      )				->  Tuple:
      """simple docstring"""





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







def       _A  (      ):
   a__	:						int      		=  get_args()
   set_seed(args.seed   )

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

   print("Loading tokenizer and model"   )
   a__	:						str      		=  AutoTokenizer.from_pretrained(args.model_ckpt   )
   a__	:						str      		=  tokenizer.eos_token
   a__	:						int      		=  AutoModelForSequenceClassification.from_pretrained(args.model_ckpt							, num_labels=7   )
   a__	:						Tuple      		=  model.config.eos_token_id

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

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

   def tokenize(lowerCamelCase   ):
      a__	:						Dict      		=  tokenizer(example["src"]							, truncation=UpperCamelCase__							, max_length=1024   )
      a__	:						List[str]      		=  labels.straint(example["complexity"]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

   a__	:						Optional[Any]      		=  train_test_validation.map(
       UpperCamelCase__							, batched=UpperCamelCase__							, remove_columns=train_test_validation["train"].column_names							, )
   a__	:						str      		=  DataCollatorWithPadding(tokenizer=UpperCamelCase__   )

   a__	:						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"							, )

   a__	:						Dict      		=  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()








# Copyright 2022 The HuggingFace Team and The OpenBMB 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

