
"""simple docstring"""



import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        VideoMAEForPreTraining,
        VideoMAEForVideoClassification,
        VideoMAEModel,
    )
    from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=13     ,	UpperCamelCase_=10     ,	UpperCamelCase_=3     ,	UpperCamelCase_=2     ,	UpperCamelCase_=2     ,	UpperCamelCase_=2     ,	UpperCamelCase_=True     ,	UpperCamelCase_=True     ,	UpperCamelCase_=32     ,	UpperCamelCase_=5     ,	UpperCamelCase_=4     ,	UpperCamelCase_=37     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=10     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=0.9     ,	UpperCamelCase_=None     ,	):
          UpperCAmelCase__	:      int     							=					parent
          UpperCAmelCase__	:      Tuple     							=					batch_size
          UpperCAmelCase__	:      Optional[Any]     							=					image_size
          UpperCAmelCase__	:      Any     							=					num_channels
          UpperCAmelCase__	:      List[str]     							=					patch_size
          UpperCAmelCase__	:      Optional[Any]     							=					tubelet_size
          UpperCAmelCase__	:      Optional[Any]     							=					num_frames
          UpperCAmelCase__	:      str     							=					is_training
          UpperCAmelCase__	:      Optional[Any]     							=					use_labels
          UpperCAmelCase__	:      List[str]     							=					hidden_size
          UpperCAmelCase__	:      Tuple     							=					num_hidden_layers
          UpperCAmelCase__	:      str     							=					num_attention_heads
          UpperCAmelCase__	:      Optional[Any]     							=					intermediate_size
          UpperCAmelCase__	:      Union[str, Any]     							=					hidden_act
          UpperCAmelCase__	:      str     							=					hidden_dropout_prob
          UpperCAmelCase__	:      str     							=					attention_probs_dropout_prob
          UpperCAmelCase__	:      List[Any]     							=					type_sequence_label_size
          UpperCAmelCase__	:      List[Any]     							=					initializer_range
          UpperCAmelCase__	:      int     							=					mask_ratio
          UpperCAmelCase__	:      Optional[Any]     							=					scope

          # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
          UpperCAmelCase__	:      Any     							=					(image_size // patch_size) ** 2
          UpperCAmelCase__	:      Optional[Any]     							=					(num_frames // tubelet_size) * self.num_patches_per_frame

          # use this variable to define bool_masked_pos
          UpperCAmelCase__	:      Any     							=					int(mask_ratio * self.seq_length      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor(
              [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

          UpperCAmelCase__	:      Any     							=					None
          if self.use_labels:
              UpperCAmelCase__	:      Any     							=					ids_tensor([self.batch_size]     ,	self.type_sequence_label_size      )

          UpperCAmelCase__	:      Union[str, Any]     							=					self.get_config()

          return config, pixel_values, labels



      def  __snake_case							(   self      ):
          return VideoMAEConfig(
              image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	num_frames=self.num_frames     ,	tubelet_size=self.tubelet_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	is_decoder=UpperCamelCase_     ,	initializer_range=self.initializer_range     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					VideoMAEModel(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      List[Any]     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					VideoMAEForPreTraining(UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          # important: each video needs to have the same number of masked patches
          # hence we define a single mask, which we then repeat for each example in the batch
          UpperCAmelCase__	:      Optional[Any]     							=					torch.ones((self.num_masks,)      )
          UpperCAmelCase__	:      Union[str, Any]     							=					torch.cat([mask, torch.zeros(self.seq_length - mask.size(0      )      )]      )
          UpperCAmelCase__	:      Union[str, Any]     							=					mask.expand(self.batch_size     ,	-1      ).bool()

          UpperCAmelCase__	:      Tuple     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )
          # model only returns predictions for masked patches
          UpperCAmelCase__	:      Union[str, Any]     							=					mask.sum().item()
          UpperCAmelCase__	:      Optional[int]     							=					3 * self.tubelet_size * self.patch_size**2
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, num_masked_patches, decoder_num_labels)      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					config_and_inputs
          UpperCAmelCase__	:      List[str]     							=					{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Union[str, Any]           =     (
          (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
      )
      UpperCamelCase							:   Dict           =     (
          {"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase							:   int           =     False
      UpperCamelCase							:   List[str]           =     False
      UpperCamelCase							:   Any           =     False
      UpperCamelCase							:   List[Any]           =     False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					VideoMAEModelTester(self      )
          UpperCAmelCase__	:      Optional[Any]     							=					ConfigTester(self     ,	config_class=UpperCamelCase_     ,	has_text_modality=UpperCamelCase_     ,	hidden_size=37      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=False      ):
          UpperCAmelCase__	:      int     							=					copy.deepcopy(UpperCamelCase_      )

          if model_class == VideoMAEForPreTraining:
              # important: each video needs to have the same number of masked patches
              # hence we define a single mask, which we then repeat for each example in the batch
              UpperCAmelCase__	:      Dict     							=					torch.ones((self.model_tester.num_masks,)      )
              UpperCAmelCase__	:      Optional[int]     							=					torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0      )      )]      )
              UpperCAmelCase__	:      str     							=					mask.expand(self.model_tester.batch_size     ,	-1      ).bool()
              UpperCAmelCase__	:      str     							=					bool_masked_pos.to(UpperCamelCase_      )

          if return_labels:
              if model_class in [
                  *get_values(UpperCamelCase_      ),
              ]:
                  UpperCAmelCase__	:      Optional[int]     							=					torch.zeros(
                      self.model_tester.batch_size     ,	dtype=torch.long     ,	device=UpperCamelCase_      )

          return inputs_dict



      def  __snake_case							(   self      ):
          self.config_tester.run_common_tests()



      @unittest.skip(reason='VideoMAE does not use inputs_embeds'      )
      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      str     							=					model_class(UpperCamelCase_      )
              self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)      )
              UpperCAmelCase__	:      Dict     							=					model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCamelCase_     ,	nn.Linear      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      Union[str, Any]     							=					model_class(UpperCamelCase_      )
              UpperCAmelCase__	:      Optional[int]     							=					inspect.signature(model.forward      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase__	:      Optional[Any]     							=					[*signature.parameters.keys()]

              UpperCAmelCase__	:      Any     							=					['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__	:      List[Any]     							=					VideoMAEModel.from_pretrained(UpperCamelCase_      )
              self.assertIsNotNone(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.has_attentions:
              pass

          else:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self.model_tester.prepare_config_and_inputs_for_common()
              UpperCAmelCase__	:      Dict     							=					True

              for model_class in self.all_model_classes:
                  UpperCAmelCase__	:      str     							=					self.model_tester.seq_length - self.model_tester.num_masks
                  UpperCAmelCase__	:      List[Any]     							=					(
                      num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                  )

                  UpperCAmelCase__	:      List[str]     							=					True
                  UpperCAmelCase__	:      Any     							=					False
                  UpperCAmelCase__	:      Optional[Any]     							=					True
                  UpperCAmelCase__	:      Dict     							=					model_class(UpperCamelCase_      )
                  model.to(UpperCamelCase_      )
                  model.eval()
                  with torch.no_grad():
                      UpperCAmelCase__	:      str     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )
                  UpperCAmelCase__	:      str     							=					outputs.attentions
                  self.assertEqual(len(UpperCamelCase_      )     ,	self.model_tester.num_hidden_layers      )

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  UpperCAmelCase__	:      Optional[int]     							=					True
                  UpperCAmelCase__	:      int     							=					model_class(UpperCamelCase_      )
                  model.to(UpperCamelCase_      )
                  model.eval()
                  with torch.no_grad():
                      UpperCAmelCase__	:      Optional[Any]     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )
                  UpperCAmelCase__	:      Dict     							=					outputs.attentions
                  self.assertEqual(len(UpperCamelCase_      )     ,	self.model_tester.num_hidden_layers      )

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]      )     ,	[self.model_tester.num_attention_heads, seq_len, seq_len]     ,	)
                  UpperCAmelCase__	:      Optional[Any]     							=					len(UpperCamelCase_      )

                  # Check attention is always last and order is fine
                  UpperCAmelCase__	:      List[str]     							=					True
                  UpperCAmelCase__	:      List[Any]     							=					True
                  UpperCAmelCase__	:      Tuple     							=					model_class(UpperCamelCase_      )
                  model.to(UpperCamelCase_      )
                  model.eval()
                  with torch.no_grad():
                      UpperCAmelCase__	:      Dict     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )

                  self.assertEqual(out_len + 1     ,	len(UpperCamelCase_      )      )

                  UpperCAmelCase__	:      Any     							=					outputs.attentions

                  self.assertEqual(len(UpperCamelCase_      )     ,	self.model_tester.num_hidden_layers      )
                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]      )     ,	[self.model_tester.num_attention_heads, seq_len, seq_len]     ,	)



      def  __snake_case							(   self      ):
          def check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Any     							=					model_class(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              with torch.no_grad():
                  UpperCAmelCase__	:      List[str]     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )

              UpperCAmelCase__	:      Union[str, Any]     							=					outputs.hidden_states
              UpperCAmelCase__	:      int     							=					self.model_tester.num_hidden_layers + 1
              self.assertEqual(len(UpperCamelCase_      )     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      str     							=					self.model_tester.seq_length - self.model_tester.num_masks
              UpperCAmelCase__	:      Tuple     							=					num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]      )     ,	[seq_length, self.model_tester.hidden_size]     ,	)

          UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      List[Any]     							=					True
              check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase__	:      Tuple     							=					True

              check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )



      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
      def  __snake_case							(   self      ):
          pass




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      str     							=					hf_hub_download(
        repo_id='hf-internal-testing/spaghetti-video'    ,filename='eating_spaghetti.npy'    ,repo_type='dataset' )
    UpperCAmelCase__	:      Dict     							=					np.load(_snake_case )
    return list(_snake_case )





@require_torch
@require_vision
class 				a			(					unittest.TestCase      ):



      @cached_property
      def  __snake_case							(   self      ):
          # logits were tested with a different mean and std, so we use the same here
          return (
              VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,	image_std=[0.5, 0.5, 0.5]      )
              if is_vision_available()
              else None
          )



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'      ).to(
              UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					self.default_image_processor
          UpperCAmelCase__	:      Tuple     							=					prepare_video()
          UpperCAmelCase__	:      Dict     							=					image_processor(UpperCamelCase_     ,	return_tensors='pt'      ).to(UpperCamelCase_      )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__	:      Dict     							=					model(**UpperCamelCase_      )

          # verify the logits
          UpperCAmelCase__	:      List[str]     							=					torch.Size((1, 400)      )
          self.assertEqual(outputs.logits.shape     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      List[str]     							=					torch.tensor([0.3669, -0.0688, -0.2421]      ).to(UpperCamelCase_      )

          self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	UpperCamelCase_     ,	atol=1E-4      )      )



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					self.default_image_processor
          UpperCAmelCase__	:      int     							=					prepare_video()
          UpperCAmelCase__	:      str     							=					image_processor(UpperCamelCase_     ,	return_tensors='pt'      ).to(UpperCamelCase_      )

          # add boolean mask, indicating which patches to mask
          UpperCAmelCase__	:      int     							=					hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'     ,	filename='bool_masked_pos.pt'      )
          UpperCAmelCase__	:      List[str]     							=					torch.load(UpperCamelCase_      )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__	:      Tuple     							=					model(**UpperCamelCase_      )

          # verify the logits
          UpperCAmelCase__	:      List[str]     							=					torch.Size([1, 1_408, 1_536]      )
          UpperCAmelCase__	:      List[Any]     							=					torch.tensor(
              [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]     ,	device=UpperCamelCase_      )
          self.assertEqual(outputs.logits.shape     ,	UpperCamelCase_      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,	UpperCamelCase_     ,	atol=1E-4      )      )

          # verify the loss (`config.norm_pix_loss` = `True`)
          UpperCAmelCase__	:      List[Any]     							=					torch.tensor([0.5142]     ,	device=UpperCamelCase_      )
          self.assertTrue(torch.allclose(outputs.loss     ,	UpperCamelCase_     ,	atol=1E-4      )      )

          # verify the loss (`config.norm_pix_loss` = `False`)
          UpperCAmelCase__	:      Tuple     							=					VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'     ,	norm_pix_loss=UpperCamelCase_      ).to(
              UpperCamelCase_      )

          with torch.no_grad():
              UpperCAmelCase__	:      List[str]     							=					model(**UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					torch.tensor(torch.tensor([0.6469]      )     ,	device=UpperCamelCase_      )
          self.assertTrue(torch.allclose(outputs.loss     ,	UpperCamelCase_     ,	atol=1E-4      )      )



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


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					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__	:      str     							=					AutoTokenizer.from_pretrained(args.model_ckpt )
    UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.eos_token
    UpperCAmelCase__	:      Optional[Any]     							=					AutoModelForSequenceClassification.from_pretrained(args.model_ckpt    ,num_labels=7 )
    UpperCAmelCase__	:      Dict     							=					model.config.eos_token_id

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

    def tokenize(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					tokenizer(example['src']    ,truncation=_snake_case    ,max_length=1024 )
        UpperCAmelCase__	:      Any     							=					labels.straint(example['complexity'] )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, 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__	:      Dict     							=					Trainer(
        model=_snake_case    ,args=_snake_case    ,train_dataset=tokenized_datasets['train']    ,eval_dataset=tokenized_datasets['valid']    ,tokenizer=_snake_case    ,data_collator=_snake_case    ,compute_metrics=_snake_case    ,)

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


if __name__ == "__main__":
    main()

