

"""simple docstring"""



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


SCREAMING_SNAKE_CASE_					:	str							  =						trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE_					:	List[str]							  =						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_					:	str							  =						argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=3_8_4,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=1_2_8,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=2_0,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=3_0,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=4_2, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

SCREAMING_SNAKE_CASE_					:	int							  =						parser.parse_args()

if args.tokenizer_name:
 SCREAMING_SNAKE_CASE_					:	Any							  =						AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
 raise ValueError(
     'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
     'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
 )

logger.info('Training/evaluation parameters %s', args)

SCREAMING_SNAKE_CASE_					:	List[str]							  =						args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_					:	Dict							  =						True

SCREAMING_SNAKE_CASE_					:	Dict							  =						'temp_engine/bert-fp32.engine'
if args.fpaa:
 SCREAMING_SNAKE_CASE_					:	Any							  =						'temp_engine/bert-fp16.engine'
if args.inta:
 SCREAMING_SNAKE_CASE_					:	int							  =						'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
 os.makedirs('temp_engine')

SCREAMING_SNAKE_CASE_					:	Any							  =						1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
 with open(args.onnx_model_path, 'rb') as model:
  if not parser.parse(model.read()):
   for error in range(parser.num_errors):
    print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						[network.get_input(i) for i in range(network.num_inputs)]
 SCREAMING_SNAKE_CASE_					:	str							  =						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

 with builder.create_builder_config() as config:
  SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						1 << 5_0
  if STRICT_TYPES:
   config.set_flag(trt.BuilderFlag.STRICT_TYPES)
  if args.fpaa:
   config.set_flag(trt.BuilderFlag.FPaa)
  if args.inta:
   config.set_flag(trt.BuilderFlag.INTa)
  SCREAMING_SNAKE_CASE_					:	List[Any]							  =						builder.create_optimization_profile()
  config.add_optimization_profile(profile)
  for i in range(len(input_names)):
   profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
  SCREAMING_SNAKE_CASE_					:	Dict							  =						builder.build_engine(network, config)

  # serialize_engine and store in file (can be directly loaded and deserialized):
  with open(engine_name, 'wb') as f:
   f.write(engine.serialize())





def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  np.asarray(inputs["""input_ids"""]					,   dtype=np.intaa			)
     A__										=  np.asarray(inputs["""attention_mask"""]					,   dtype=np.intaa			)
     A__										=  np.asarray(inputs["""token_type_ids"""]					,   dtype=np.intaa			)

     # Copy inputs
     cuda.memcpy_htod_async(d_inputs[0]					,   input_ids.ravel()					,   UpperCAmelCase_			)
     cuda.memcpy_htod_async(d_inputs[1]					,   attention_mask.ravel()					,   UpperCAmelCase_			)
     cuda.memcpy_htod_async(d_inputs[2]					,   token_type_ids.ravel()					,   UpperCAmelCase_			)
     # start time
     A__										=  time.time()
     # Run inference
     context.execute_async(
         bindings=[int(UpperCAmelCase_			) for d_inp in d_inputs] + [int(UpperCAmelCase_			), int(UpperCAmelCase_			)]					,   stream_handle=stream.handle			)
     # Transfer predictions back from GPU
     cuda.memcpy_dtoh_async(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)
     cuda.memcpy_dtoh_async(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)
     # Synchronize the stream and take time
     stream.synchronize()
     # end time
     A__										=  time.time()
     A__										=  end_time - start_time
     A__										=  (h_outputa, h_outputa)
     # print(outputs)
     return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
SCREAMING_SNAKE_CASE_					:	Tuple							  =						Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
 datasets.utils.logging.set_verbosity_warning()
 transformers.utils.logging.set_verbosity_info()
else:
 datasets.utils.logging.set_verbosity_error()
 transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
 set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
 # Downloading and loading a dataset from the hub.
 SCREAMING_SNAKE_CASE_					:	int							  =						load_dataset(args.dataset_name, args.dataset_config_name)
else:
 raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

SCREAMING_SNAKE_CASE_					:	Tuple							  =						raw_datasets['validation'].column_names

SCREAMING_SNAKE_CASE_					:	Dict							  =						'question' if 'question' in column_names else column_names[0]
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						'context' if 'context' in column_names else column_names[1]
SCREAMING_SNAKE_CASE_					:	List[str]							  =						'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
 logger.warning(
     f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
     f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
 )

SCREAMING_SNAKE_CASE_					:	Tuple							  =						min(args.max_seq_length, tokenizer.model_max_length)





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     # Some of the questions have lots of whitespace on the left, which is not useful and will make the
     # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
     # left whitespace
     A__										=  [q.lstrip() for q in examples[question_column_name]]

     # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
     # in one example possible giving several features when a context is long, each of those features having a
     # context that overlaps a bit the context of the previous feature.
     A__										=  tokenizer(
         examples[question_column_name if pad_on_right else context_column_name]					,   examples[context_column_name if pad_on_right else question_column_name]					,   truncation="""only_second""" if pad_on_right else """only_first"""					,   max_length=UpperCAmelCase_					,   stride=args.doc_stride					,   return_overflowing_tokens=UpperCAmelCase_					,   return_offsets_mapping=UpperCAmelCase_					,   padding="""max_length"""					,   )

     # Since one example might give us several features if it has a long context, we need a map from a feature to
     # its corresponding example. This key gives us just that.
     A__										=  tokenized_examples.pop("""overflow_to_sample_mapping"""			)

     # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
     # corresponding example_id and we will store the offset mappings.
     A__										=  []

     for i in range(len(tokenized_examples["""input_ids"""]			)			):
          # Grab the sequence corresponding to that example (to know what is the context and what is the question).
          A__										=  tokenized_examples.sequence_ids(UpperCAmelCase_			)
          A__										=  1 if pad_on_right else 0

          # One example can give several spans, this is the index of the example containing this span of text.
          A__										=  sample_mapping[i]
          tokenized_examples["example_id"].append(examples["""id"""][sample_index]			)

          # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
          # position is part of the context or not.
          A__										=  [
              (o if sequence_ids[k] == context_index else None)
              for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]			)
          ]

     return tokenized_examples


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						raw_datasets['validation']
# Validation Feature Creation
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

SCREAMING_SNAKE_CASE_					:	str							  =						default_data_collator

SCREAMING_SNAKE_CASE_					:	Dict							  =						eval_dataset.remove_columns(['example_id', 'offset_mapping'])
SCREAMING_SNAKE_CASE_					:	str							  =						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Union[str, Any]="eval"			):
     # Post-processing: we match the start logits and end logits to answers in the original context.
     A__										=  postprocess_qa_predictions(
         examples=UpperCAmelCase_					,   features=UpperCAmelCase_					,   predictions=UpperCAmelCase_					,   version_2_with_negative=args.version_2_with_negative					,   n_best_size=args.n_best_size					,   max_answer_length=args.max_answer_length					,   null_score_diff_threshold=args.null_score_diff_threshold					,   output_dir=args.output_dir					,   prefix=UpperCAmelCase_					,   )
     # Format the result to the format the metric expects.
     if args.version_2_with_negative:
          A__										=  [
              {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
          ]
     else:
          A__										=  [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

     A__										=  [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
     return EvalPrediction(predictions=UpperCAmelCase_					,   label_ids=UpperCAmelCase_			)


SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
 # setup for TRT inferrence
 for i in range(len(input_names)):
  context.set_binding_shape(i, INPUT_SHAPE)
 assert context.all_binding_shapes_specified





 def  _snake_case     (    UpperCAmelCase_  :					List[Any]			):
      return trt.volume(engine.get_binding_shape(UpperCAmelCase_			)			) * engine.get_binding_dtype(UpperCAmelCase_			).itemsize

 # Allocate device memory for inputs and outputs.
 SCREAMING_SNAKE_CASE_					:	int							  =						[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

 # Allocate output buffer
 SCREAMING_SNAKE_CASE_					:	Dict							  =						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						cuda.mem_alloc(h_outputa.nbytes)
 SCREAMING_SNAKE_CASE_					:	Any							  =						cuda.mem_alloc(h_outputa.nbytes)

 # Create a stream in which to copy inputs/outputs and run inference.
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						cuda.Stream()

 # Evaluation
 logger.info('***** Running Evaluation *****')
 logger.info(f"""  Num examples = {len(eval_dataset)}""")
 logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

 SCREAMING_SNAKE_CASE_					:	Any							  =						0.0
 SCREAMING_SNAKE_CASE_					:	Dict							  =						0
 SCREAMING_SNAKE_CASE_					:	Dict							  =						timeit.default_timer()

 SCREAMING_SNAKE_CASE_					:	Dict							  =						None
 for step, batch in enumerate(eval_dataloader):
  SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
  total_time += infer_time
  niter += 1

  SCREAMING_SNAKE_CASE_					:	Tuple							  =						outputs
  SCREAMING_SNAKE_CASE_					:	int							  =						torch.tensor(start_logits)
  SCREAMING_SNAKE_CASE_					:	int							  =						torch.tensor(end_logits)

  # necessary to pad predictions and labels for being gathered
  SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

  SCREAMING_SNAKE_CASE_					:	int							  =						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

 if all_preds is not None:
  SCREAMING_SNAKE_CASE_					:	List[Any]							  =						nested_truncate(all_preds, len(eval_dataset))

 SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						timeit.default_timer() - start_time
 logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
 # Inference time from TRT
 logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_0_0_0 / niter))
 logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_0_0_0))

 logger.info('Total Number of Inference =  %d', niter)

SCREAMING_SNAKE_CASE_					:	str							  =						post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE_					:	Any							  =						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

logger.info(f"""Evaluation metrics: {eval_metric}""")







"""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  _snake_case     (    ):
     A__										=  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_					:	Union[str, Any]							  =						load('accuracy')





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     A__			,				A__										=  eval_pred
     A__										=  np.argmax(UpperCAmelCase_					,   axis=1			)
     return metric.compute(predictions=UpperCAmelCase_					,   references=UpperCAmelCase_			)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Optional[int]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         super().__init__()
         A__										=  trainer





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Dict    ,						**UpperCamelCase: int       ):






         """simple docstring"""


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





def  _snake_case     (    ):
     A__										=  get_args()
     set_seed(args.seed			)

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

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

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

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

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

     A__										=  train_test_validation.map(
         UpperCAmelCase_					,   batched=UpperCAmelCase_					,   remove_columns=train_test_validation["""train"""].column_names					,   )
     A__										=  DataCollatorWithPadding(tokenizer=UpperCAmelCase_			)

     A__										=  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__										=  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()






