
"""simple docstring"""



import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=13     ,	UpperCamelCase_=32     ,	UpperCamelCase_=2     ,	UpperCamelCase_=3     ,	UpperCamelCase_=16     ,	UpperCamelCase_=[32, 64, 128]     ,	UpperCamelCase_=[1, 2, 1]     ,	UpperCamelCase_=[2, 2, 4]     ,	UpperCamelCase_=2     ,	UpperCamelCase_=2.0     ,	UpperCamelCase_=True     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=False     ,	UpperCamelCase_=True     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=1E-5     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_=10     ,	UpperCamelCase_=8     ,	UpperCamelCase_=["stage1", "stage2"]     ,	UpperCamelCase_=[1, 2]     ,	):
          UpperCAmelCase__	:      int     							=					parent
          UpperCAmelCase__	:      Tuple     							=					batch_size
          UpperCAmelCase__	:      Union[str, Any]     							=					image_size
          UpperCAmelCase__	:      Optional[int]     							=					patch_size
          UpperCAmelCase__	:      List[Any]     							=					num_channels
          UpperCAmelCase__	:      List[Any]     							=					embed_dim
          UpperCAmelCase__	:      int     							=					hidden_sizes
          UpperCAmelCase__	:      Union[str, Any]     							=					depths
          UpperCAmelCase__	:      Tuple     							=					num_heads
          UpperCAmelCase__	:      Optional[Any]     							=					window_size
          UpperCAmelCase__	:      Any     							=					mlp_ratio
          UpperCAmelCase__	:      Dict     							=					qkv_bias
          UpperCAmelCase__	:      Optional[Any]     							=					hidden_dropout_prob
          UpperCAmelCase__	:      List[Any]     							=					attention_probs_dropout_prob
          UpperCAmelCase__	:      Any     							=					drop_path_rate
          UpperCAmelCase__	:      Union[str, Any]     							=					hidden_act
          UpperCAmelCase__	:      Tuple     							=					use_absolute_embeddings
          UpperCAmelCase__	:      List[str]     							=					patch_norm
          UpperCAmelCase__	:      Union[str, Any]     							=					layer_norm_eps
          UpperCAmelCase__	:      Tuple     							=					initializer_range
          UpperCAmelCase__	:      str     							=					is_training
          UpperCAmelCase__	:      Dict     							=					scope
          UpperCAmelCase__	:      int     							=					use_labels
          UpperCAmelCase__	:      List[Any]     							=					type_sequence_label_size
          UpperCAmelCase__	:      Union[str, Any]     							=					encoder_stride
          UpperCAmelCase__	:      List[str]     							=					out_features
          UpperCAmelCase__	:      Optional[Any]     							=					out_indices



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

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

          UpperCAmelCase__	:      int     							=					self.get_config()

          return config, pixel_values, labels



      def  __snake_case							(   self      ):
          return FocalNetConfig(
              image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	embed_dim=self.embed_dim     ,	hidden_sizes=self.hidden_sizes     ,	depths=self.depths     ,	num_heads=self.num_heads     ,	window_size=self.window_size     ,	mlp_ratio=self.mlp_ratio     ,	qkv_bias=self.qkv_bias     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	drop_path_rate=self.drop_path_rate     ,	hidden_act=self.hidden_act     ,	use_absolute_embeddings=self.use_absolute_embeddings     ,	path_norm=self.patch_norm     ,	layer_norm_eps=self.layer_norm_eps     ,	initializer_range=self.initializer_range     ,	encoder_stride=self.encoder_stride     ,	out_features=self.out_features     ,	out_indices=self.out_indices     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					FocalNetModel(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      int     							=					model(UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
          UpperCAmelCase__	:      Optional[Any]     							=					int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, expected_seq_len, expected_dim)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Dict     							=					FocalNetBackbone(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      Optional[Any]     							=					model(UpperCamelCase_      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )     ,	len(config.out_features      )      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )     ,	[self.batch_size, self.image_size, 8, 8]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )     ,	len(config.out_features      )      )
          self.parent.assertListEqual(model.channels     ,	config.hidden_sizes[:-1]      )

          # verify backbone works with out_features=None
          UpperCAmelCase__	:      List[Any]     							=					None
          UpperCAmelCase__	:      Union[str, Any]     							=					FocalNetBackbone(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      Optional[int]     							=					model(UpperCamelCase_      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )     ,	1      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )     ,	[self.batch_size, self.image_size * 2, 4, 4]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )     ,	1      )
          self.parent.assertListEqual(model.channels     ,	[config.hidden_sizes[-1]]      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Tuple     							=					FocalNetForMaskedImageModeling(config=UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      Optional[Any]     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(
              result.reconstruction.shape     ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

          # test greyscale images
          UpperCAmelCase__	:      List[Any]     							=					1
          UpperCAmelCase__	:      Tuple     							=					FocalNetForMaskedImageModeling(UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          UpperCAmelCase__	:      Any     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.reconstruction.shape     ,	(self.batch_size, 1, self.image_size, self.image_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Tuple     							=					self.type_sequence_label_size
          UpperCAmelCase__	:      Any     							=					FocalNetForImageClassification(UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()
          UpperCAmelCase__	:      List[str]     							=					model(UpperCamelCase_     ,	labels=UpperCamelCase_      )
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

          # test greyscale images
          UpperCAmelCase__	:      List[str]     							=					1
          UpperCAmelCase__	:      Optional[int]     							=					FocalNetForImageClassification(UpperCamelCase_      )
          model.to(UpperCamelCase_      )
          model.eval()

          UpperCAmelCase__	:      str     							=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          UpperCAmelCase__	:      Union[str, Any]     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.prepare_config_and_inputs()

          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					config_and_inputs
          UpperCAmelCase__	:      Dict     							=					{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   List[str]           =     (
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase							:   str           =     (
          {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCamelCase							:   Union[str, Any]           =     False

      UpperCamelCase							:   Optional[Any]           =     False
      UpperCamelCase							:   Any           =     False
      UpperCamelCase							:   str           =     False
      UpperCamelCase							:   Tuple           =     False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					FocalNetModelTester(self      )
          UpperCAmelCase__	:      Union[str, Any]     							=					ConfigTester(self     ,	config_class=UpperCamelCase_     ,	embed_dim=37     ,	has_text_modality=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()



      def  __snake_case							(   self      ):
          return



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase_      )



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



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



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



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



      @unittest.skip(reason='FocalNet does not use feedforward chunking'      )
      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
              UpperCAmelCase__	:      Optional[Any]     							=					model_class(UpperCamelCase_      )
              self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)      )
              UpperCAmelCase__	:      List[Any]     							=					model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCamelCase_     ,	nn.Linear      )      )



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

          for model_class in self.all_model_classes[:-1]:
              UpperCAmelCase__	:      Tuple     							=					model_class(UpperCamelCase_      )
              UpperCAmelCase__	:      Any     							=					inspect.signature(model.forward      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase__	:      int     							=					[*signature.parameters.keys()]

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



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

          with torch.no_grad():
              UpperCAmelCase__	:      Dict     							=					model(**self._prepare_for_class(UpperCamelCase_     ,	UpperCamelCase_      )      )

          UpperCAmelCase__	:      Tuple     							=					outputs.hidden_states

          UpperCAmelCase__	:      List[Any]     							=					getattr(
              self.model_tester     ,	'expected_num_hidden_layers'     ,	len(self.model_tester.depths      ) + 1      )
          self.assertEqual(len(UpperCamelCase_      )     ,	UpperCamelCase_      )

          # FocalNet has a different seq_length
          UpperCAmelCase__	:      List[Any]     							=					(
              config.patch_size
              if isinstance(config.patch_size     ,	collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          UpperCAmelCase__	:      List[str]     							=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

          UpperCAmelCase__	:      Optional[int]     							=					outputs.reshaped_hidden_states
          self.assertEqual(len(UpperCamelCase_      )     ,	UpperCamelCase_      )

          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					reshaped_hidden_states[0].shape
          UpperCAmelCase__	:      Any     							=					(
              reshaped_hidden_states[0].view(UpperCamelCase_     ,	UpperCamelCase_     ,	height * width      ).permute(0     ,	2     ,	1      )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]      )     ,	[num_patches, self.model_tester.embed_dim]     ,	)



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

          UpperCAmelCase__	:      Optional[Any]     							=					(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size     ,	collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes[:-1]:
              UpperCAmelCase__	:      Any     							=					True
              self.check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

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

              self.check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__	:      Dict     							=					3

          UpperCAmelCase__	:      List[str]     							=					(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size     ,	collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          UpperCAmelCase__	:      Optional[int]     							=					(
              config.patch_size
              if isinstance(config.patch_size     ,	collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          UpperCAmelCase__	:      Any     							=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          UpperCAmelCase__	:      int     							=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
              UpperCAmelCase__	:      Tuple     							=					True
              self.check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	(padded_height, padded_width)      )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase__	:      Tuple     							=					True
              self.check_hidden_states_output(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	(padded_height, padded_width)      )



      @slow
      def  __snake_case							(   self      ):
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__	:      List[str]     							=					FocalNetModel.from_pretrained(UpperCamelCase_      )
              self.assertIsNotNone(UpperCamelCase_      )



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

          UpperCAmelCase__	:      List[Any]     							=					_config_zero_init(UpperCamelCase_      )
          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      Dict     							=					model_class(config=UpperCamelCase_      )
              for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()     ,	[0.0, 1.0]     ,	msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,	)





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



      @cached_property
      def  __snake_case							(   self      ):
          # TODO update organization
          return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'      ) if is_vision_available() else None



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					self.default_image_processor

          UpperCAmelCase__	:      Union[str, Any]     							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
          UpperCAmelCase__	:      int     							=					image_processor(images=UpperCamelCase_     ,	return_tensors='pt'      ).to(UpperCamelCase_      )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__	:      List[Any]     							=					model(**UpperCamelCase_      )

          # verify the logits
          UpperCAmelCase__	:      Union[str, Any]     							=					torch.Size((1, 1_000)      )
          self.assertEqual(outputs.logits.shape     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					torch.tensor([0.2166, -0.4368, 0.2191]      ).to(UpperCamelCase_      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	UpperCamelCase_     ,	atol=1E-4      )      )
          self.assertTrue(outputs.logits.argmax(dim=-1      ).item()     ,	281      )





@require_torch
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Dict           =     (FocalNetBackbone,) if is_torch_available() else ()
      UpperCamelCase							:   Any           =     FocalNetConfig

      UpperCamelCase							:   str           =     False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					FocalNetModelTester(self      )



"""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()

