


"""simple docstring"""

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor





class    lowerCamelCase__						:
      def __init__(		self	:	str      ,       A_	:	Union[str, Any]      ,       A_	:	Tuple=1_3      ,       A_	:	List[str]=3      ,       A_	:	str=True      ,       A_	:	Any=True      ,       A_	:	List[Any]=0.1      ,       A_	:	Dict=0.1      ,       A_	:	List[Any]=2_2_4      ,       A_	:	str=1_0_0_0      ,       A_	:	Tuple=[3, 3, 6, 4]      ,       A_	:	int=[4_8, 5_6, 1_1_2, 2_2_0]      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	is_training
       __lowercase									=	use_labels
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	num_labels
       __lowercase									=	image_size
       __lowercase									=	layer_depths
       __lowercase									=	embed_dims
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       __lowercase									=	None
       if self.use_labels:
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.num_labels							)

       __lowercase									=	self.get_config()

       return config, pixel_values, labels
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return SwiftFormerConfig(
           depths=self.layer_depths      ,       embed_dims=self.embed_dims      ,       mlp_ratio=4      ,       downsamples=[True, True, True, True]      ,       hidden_act="""gelu"""      ,       num_labels=self.num_labels      ,       down_patch_size=3      ,       down_stride=2      ,       down_pad=1      ,       drop_rate=0.0      ,       drop_path_rate=0.0      ,       use_layer_scale=lowerCamelCase__      ,       layer_scale_init_value=1e-5      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	str      ,       A_	:	int      ,       A_	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	SwiftFormerModel(config=lowerCamelCase__							)
       model.to(lowerCamelCase__							)
       model.eval()
       __lowercase									=	model(lowerCamelCase__							)
       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.embed_dims[-1], 7, 7)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Tuple      ,       A_	:	List[str]      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	SwiftFormerForImageClassification(lowerCamelCase__							)
       model.to(lowerCamelCase__							)
       model.eval()
       __lowercase									=	model(lowerCamelCase__      ,       labels=lowerCamelCase__							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)

       __lowercase									=	SwiftFormerForImageClassification(lowerCamelCase__							)
       model.to(lowerCamelCase__							)
       model.eval()

       __lowercase									=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
       __lowercase									=	model(lowerCamelCase__							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       (__lowercase)									=	self.prepare_config_and_inputs()
       __lowercase									=	{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class    lowerCamelCase__						(		__lowerCAmelCase			,   __lowerCAmelCase			,   unittest.TestCase    ):
      a :     Tuple											=		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      a :     Optional[int]											=		(
          {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      a :     Optional[Any]											=		False
      a :     Tuple											=		False
      a :     List[str]											=		False
      a :     Union[str, Any]											=		False
      a :     List[str]											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	SwiftFormerModelTester(self							)
       __lowercase									=	ConfigTester(
           self      ,       config_class=lowerCamelCase__      ,       has_text_modality=lowerCamelCase__      ,       hidden_size=3_7      ,       num_attention_heads=1_2      ,       num_hidden_layers=1_2      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(lowerCamelCase__							)
        __lowercase									=	model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase__      ,       nn.Linear							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(lowerCamelCase__							)
        __lowercase									=	inspect.signature(model.forward							)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowercase									=	[*signature.parameters.keys()]

        __lowercase									=	['''pixel_values''']
        self.assertListEqual(arg_names[:1]      ,       lowerCamelCase__							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__							)
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase									=	SwiftFormerModel.from_pretrained(lowerCamelCase__							)
        self.assertIsNotNone(lowerCamelCase__							)
      @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       def check_hidden_states_output(A_	:	Union[str, Any]      ,       A_	:	List[str]      ,       A_	:	Optional[int]							):
        __lowercase									=	model_class(lowerCamelCase__							)
        model.to(lowerCamelCase__							)
        model.eval()

        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(lowerCamelCase__      ,       lowerCamelCase__							)							)

        __lowercase									=	outputs.hidden_states

        __lowercase									=	8
        self.assertEqual(len(lowerCamelCase__							)      ,       lowerCamelCase__							)  # TODO

        # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
        # with the width and height being successively divided by 2, after every 2 blocks
        for i in range(len(lowerCamelCase__							)							):
         self.assertEqual(
             hidden_states[i].shape      ,       torch.Size(
                 [
                     self.model_tester.batch_size,
                     self.model_tester.embed_dims[i // 2],
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                 ]							)      ,       )

       __lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	True
        check_hidden_states_output(lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__							)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowercase									=	True

        check_hidden_states_output(lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       def _config_zero_init(A_	:	Optional[int]							):
        __lowercase									=	copy.deepcopy(lowerCamelCase__							)
        for key in configs_no_init.__dict__.keys():
         if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
          setattr(lowerCamelCase__      ,       lowerCamelCase__      ,       1e-1_0							)
         if isinstance(getattr(lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__							)      ,       lowerCamelCase__							):
          __lowercase									=	_config_zero_init(getattr(lowerCamelCase__      ,       lowerCamelCase__							)							)
          setattr(lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__							)
        return configs_no_init

       __lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       __lowercase									=	_config_zero_init(lowerCamelCase__							)
       for model_class in self.all_model_classes:
        __lowercase									=	model_class(config=lowerCamelCase__							)
        for name, param in model.named_parameters():
         if param.requires_grad:
          self.assertIn(
              ((param.data.mean() * 1e9) / 1e9).round().item()      ,       [0.0, 1.0]      ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,       )






      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       pass






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
 return image





@require_torch
@require_vision
class    lowerCamelCase__						(		unittest.TestCase    ):
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowerCamelCase__							)

       __lowercase									=	self.default_image_processor
       __lowercase									=	prepare_img()
       __lowercase									=	image_processor(images=lowerCamelCase__      ,       return_tensors="""pt"""							).to(lowerCamelCase__							)

       # forward pass
       with torch.no_grad():
        __lowercase									=	model(**lowerCamelCase__							)

       # verify the logits
       __lowercase									=	torch.Size((1, 1_0_0_0)							)
       self.assertEqual(outputs.logits.shape      ,       lowerCamelCase__							)

       __lowercase									=	torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(lowerCamelCase__							)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       lowerCamelCase__      ,       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_      (				):






 """simple docstring"""







 __lowercase									=	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()


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	eval_pred
 __lowercase									=	np.argmax(UpperCamelCase__				,   axis=1 )
 return metric.compute(predictions=UpperCamelCase__				,   references=UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


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






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

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

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

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

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

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

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

 __lowercase									=	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									=	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()
