"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )







class  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=7  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=99  ,__UpperCamelCase=32  ,__UpperCamelCase=2  ,__UpperCamelCase=4  ,__UpperCamelCase=37  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=512  ,__UpperCamelCase=16  ,__UpperCamelCase=2  ,__UpperCamelCase=0.02  ,__UpperCamelCase=3  ,__UpperCamelCase=4  ,__UpperCamelCase=None  ,)			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			parent
        lowercase_			:       Optional[Any]									=			13
        lowercase_			:       Tuple									=			7
        lowercase_			:       Any									=			True
        lowercase_			:       List[Any]									=			True
        lowercase_			:       Tuple									=			True
        lowercase_			:       int									=			True
        lowercase_			:       Optional[int]									=			99
        lowercase_			:       int									=			32
        lowercase_			:       List[str]									=			2
        lowercase_			:       Optional[int]									=			4
        lowercase_			:       Optional[int]									=			37
        lowercase_			:       Optional[Any]									=			'gelu'
        lowercase_			:       List[str]									=			0.1
        lowercase_			:       Union[str, Any]									=			0.1
        lowercase_			:       Dict									=			512
        lowercase_			:       Optional[int]									=			16
        lowercase_			:       int									=			2
        lowercase_			:       Optional[Any]									=			0.02
        lowercase_			:       Any									=			3
        lowercase_			:       Dict									=			4
        lowercase_			:       Tuple									=			None
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Dict									=			ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

        lowercase_			:       str									=			None
        if self.use_input_mask:
             lowercase_			:       Union[str, Any]									=			random_attention_mask([self.batch_size, self.seq_length]    )

        lowercase_			:       Union[str, Any]									=			None
        if self.use_token_type_ids:
             lowercase_			:       Optional[Any]									=			ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size    )

        lowercase_			:       int									=			None
        lowercase_			:       Any									=			None
        lowercase_			:       Tuple									=			None
        if self.use_labels:
             lowercase_			:       Union[str, Any]									=			ids_tensor([self.batch_size]  ,self.type_sequence_label_size    )
             lowercase_			:       int									=			ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels    )
             lowercase_			:       Optional[int]									=			ids_tensor([self.batch_size]  ,self.num_choices    )

        lowercase_			:       str									=			RoFormerConfig(
            vocab_size=self.vocab_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  ,max_position_embeddings=self.max_position_embeddings  ,type_vocab_size=self.type_vocab_size  ,initializer_range=self.initializer_range  ,return_dict=__UpperCamelCase  ,)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       str									=			TFRoFormerModel(config=__UpperCamelCase    )
        lowercase_			:       Any									=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        lowercase_			:       int									=			[input_ids, input_mask]
        lowercase_			:       str									=			model(__UpperCamelCase    )

        lowercase_			:       List[str]									=			model(__UpperCamelCase    )

        self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       int									=			True
        lowercase_			:       str									=			TFRoFormerForCausalLM(config=__UpperCamelCase    )
        lowercase_			:       List[str]									=			{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_			:       int									=			model(__UpperCamelCase    )['logits']
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape    )  ,[self.batch_size, self.seq_length, self.vocab_size]    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        lowercase_			:       Tuple									=			TFRoFormerForMaskedLM(config=__UpperCamelCase    )
        lowercase_			:       Dict									=			{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_			:       int									=			model(__UpperCamelCase    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.num_labels
        lowercase_			:       Dict									=			TFRoFormerForSequenceClassification(config=__UpperCamelCase    )
        lowercase_			:       Any									=			{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        lowercase_			:       Dict									=			model(__UpperCamelCase    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.num_choices
        lowercase_			:       Union[str, Any]									=			TFRoFormerForMultipleChoice(config=__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			tf.tile(tf.expand_dims(__UpperCamelCase  ,1    )  ,(1, self.num_choices, 1)    )
        lowercase_			:       str									=			tf.tile(tf.expand_dims(__UpperCamelCase  ,1    )  ,(1, self.num_choices, 1)    )
        lowercase_			:       int									=			tf.tile(tf.expand_dims(__UpperCamelCase  ,1    )  ,(1, self.num_choices, 1)    )
        lowercase_			:       Union[str, Any]									=			{
            'input_ids': multiple_choice_inputs_ids,
            'attention_mask': multiple_choice_input_mask,
            'token_type_ids': multiple_choice_token_type_ids,
        }
        lowercase_			:       Tuple									=			model(__UpperCamelCase    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Any									=			self.num_labels
        lowercase_			:       Optional[Any]									=			TFRoFormerForTokenClassification(config=__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_			:       int									=			model(__UpperCamelCase    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			TFRoFormerForQuestionAnswering(config=__UpperCamelCase    )
        lowercase_			:       Dict									=			{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        lowercase_			:       Optional[int]									=			model(__UpperCamelCase    )
        self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)    )
        self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)    )







   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,						(
            lowercase_
        )							,						(
            lowercase_
        )							,						(
            lowercase_
        )							,						(
            lowercase_
        )							,						(
            lowercase_
        )							,						(
            lowercase_
        )							,						
        )			:       List[Any]									=			config_and_inputs
        lowercase_			:       Any									=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						(
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   lowercase							=						(
       {
           'feature-extraction': TFRoFormerModel,
           'fill-mask': TFRoFormerForMaskedLM,
           'question-answering': TFRoFormerForQuestionAnswering,
           'text-classification': TFRoFormerForSequenceClassification,
           'text-generation': TFRoFormerForCausalLM,
           'token-classification': TFRoFormerForTokenClassification,
           'zero-shot': TFRoFormerForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   lowercase							=						False
   lowercase							=						False
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             return True

        return False
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       str									=			TFRoFormerModelTester(self    )
        lowercase_			:       List[Any]									=			ConfigTester(self  ,config_class=__UpperCamelCase  ,hidden_size=37    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       str									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase    )







   @slow
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'    )
        self.assertIsNotNone(__UpperCamelCase    )







@require_tf
class  UpperCamelCase	(    unittest.TestCase						):
   @slow
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
        lowercase_			:       List[Any]									=			tf.constant([[0, 1, 2, 3, 4, 5]]    )
        lowercase_			:       int									=			model(__UpperCamelCase    )[0]

        # TODO Replace vocab size
        lowercase_			:       Any									=			5_0000

        lowercase_			:       str									=			[1, 6, vocab_size]
        self.assertEqual(output.shape  ,__UpperCamelCase    )

        print(output[:, :3, :3]    )

        # TODO Replace values below with what was printed above.
        lowercase_			:       Tuple									=			tf.constant(
            [
                [
                    [-0.1205_3341, -1.026_4901, 0.2922_1946],
                    [-1.513_3783, 0.19_7433, 0.1519_0607],
                    [-5.013_5403, -3.90_0256, -0.8403_8764],
                ]
            ]    )
        tf.debugging.assert_near(output[:, :3, :3]  ,__UpperCamelCase  ,atol=1e-4    )







@require_tf
class  UpperCamelCase	(    unittest.TestCase						):
   lowercase							=						1e-4
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       str									=			tf.constant([[4, 10]]    )
        lowercase_			:       Union[str, Any]									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6  ,embedding_dim=6    )

        lowercase_			:       int									=			emba(input_ids.shape    )
        lowercase_			:       List[Any]									=			tf.constant(
            [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]    )

        tf.debugging.assert_near(__UpperCamelCase  ,__UpperCamelCase  ,atol=self.tolerance    )







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       str									=			tf.constant(
            [
                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
            ]    )
        lowercase_			:       int									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=512  ,embedding_dim=512    )
        emba([2, 16, 512]    )
        lowercase_			:       Tuple									=			emba.weight[:3, :5]

        tf.debugging.assert_near(__UpperCamelCase  ,__UpperCamelCase  ,atol=self.tolerance    )







@require_tf
class  UpperCamelCase	(    unittest.TestCase						):
   lowercase							=						1e-4
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Any									=			tf.reshape(tf.range(2 * 12 * 16 * 64  ,dtype=tf.floataa    )  ,shape=(2, 12, 16, 64)    ) / 100

        lowercase_			:       Dict									=			-tf.reshape(tf.range(2 * 12 * 16 * 64  ,dtype=tf.floataa    )  ,shape=(2, 12, 16, 64)    ) / 100

        lowercase_			:       str									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32  ,embedding_dim=64    )
        lowercase_			:       Dict									=			embed_positions([2, 16, 768]    )[None, None, :, :]

        lowercase_							,						lowercase_			:       int									=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
            __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Tuple									=			tf.constant(
            [
                [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
            ]    )
        lowercase_			:       int									=			tf.constant(
            [
                [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
            ]    )

        tf.debugging.assert_near(query_layer[0, 0, :6, :8]  ,__UpperCamelCase  ,atol=self.tolerance    )
        tf.debugging.assert_near(key_layer[0, 0, :6, :8]  ,__UpperCamelCase  ,atol=self.tolerance    )


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


__SCREAMING_SNAKE_CASE								=load("accuracy")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_							,						lowercase_			:       Any									=			eval_pred
     lowercase_			:       Optional[Any]									=			np.argmax(__SCREAMING_SNAKE_CASE  ,    axis=1   )
     return metric.compute(predictions=__SCREAMING_SNAKE_CASE  ,    references=__SCREAMING_SNAKE_CASE   )







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase    )			->   None:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       str									=			trainer







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if control.should_evaluate:
             lowercase_			:       List[str]									=			deepcopy(__UpperCamelCase    )
             self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,metric_key_prefix='train'    )
             return control_copy






def     lowercase__( ):
     lowercase_			:       Dict									=			get_args()
     set_seed(args.seed   )

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

     print('Loading tokenizer and model'   )
     lowercase_			:       int									=			AutoTokenizer.from_pretrained(args.model_ckpt   )
     lowercase_			:       Optional[int]									=			tokenizer.eos_token
     lowercase_			:       int									=			AutoModelForSequenceClassification.from_pretrained(args.model_ckpt  ,    num_labels=7   )
     lowercase_			:       Union[str, Any]									=			model.config.eos_token_id

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

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

     def tokenize(__SCREAMING_SNAKE_CASE							:  str   ):
          lowercase_			:       Dict									=			tokenizer(example['src']  ,    truncation=__SCREAMING_SNAKE_CASE  ,    max_length=10_24   )
          lowercase_			:       int									=			labels.straint(example['complexity']   )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     lowercase_			:       Optional[int]									=			train_test_validation.map(
         __SCREAMING_SNAKE_CASE  ,    batched=__SCREAMING_SNAKE_CASE  ,    remove_columns=train_test_validation['train'].column_names  ,    )
     lowercase_			:       Any									=			DataCollatorWithPadding(tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       int									=			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'  ,    )

     lowercase_			:       Optional[int]									=			Trainer(
         model=__SCREAMING_SNAKE_CASE  ,    args=__SCREAMING_SNAKE_CASE  ,    train_dataset=tokenized_datasets['train']  ,    eval_dataset=tokenized_datasets['valid']  ,    tokenizer=__SCREAMING_SNAKE_CASE  ,    data_collator=__SCREAMING_SNAKE_CASE  ,    compute_metrics=__SCREAMING_SNAKE_CASE  ,    )

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


if __name__ == "__main__":
       main()

