

"""simple docstring"""



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 					a						:



    """simple docstring"""



    def __init__(     self: int    ,						UpperCamelCase: str    ,						UpperCamelCase: List[str]=3    ,						UpperCamelCase: Union[str, Any]=32    ,						UpperCamelCase: int=3    ,						UpperCamelCase: str=10    ,						UpperCamelCase: str=[10, 20, 30, 40]    ,						UpperCamelCase: str=[1, 1, 2, 1]    ,						UpperCamelCase: Any=True    ,						UpperCamelCase: str=True    ,						UpperCamelCase: Any="relu"    ,						UpperCamelCase: Tuple=3    ,						UpperCamelCase: Any=None    ,						):






         """simple docstring"""


         A__										=  parent
         A__										=  batch_size
         A__										=  image_size
         A__										=  num_channels
         A__										=  embeddings_size
         A__										=  hidden_sizes
         A__										=  depths
         A__										=  is_training
         A__										=  use_labels
         A__										=  hidden_act
         A__										=  num_labels
         A__										=  scope
         A__										=  len(UpperCamelCase       )

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

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

         A__										=  self.get_config()
         return config, pixel_values, labels

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return RegNetConfig(
             num_channels=self.num_channels    ,						embeddings_size=self.embeddings_size    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						hidden_act=self.hidden_act    ,						num_labels=self.num_labels    ,						)

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






         """simple docstring"""


         A__										=  TFRegNetModel(config=UpperCamelCase       )
         A__										=  model(UpperCamelCase    ,						training=UpperCamelCase       )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,						)

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: str    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.num_labels
         A__										=  TFRegNetForImageClassification(UpperCamelCase       )
         A__										=  model(UpperCamelCase    ,						labels=UpperCamelCase    ,						training=UpperCamelCase       )
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)       )





    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  self.prepare_config_and_inputs()
         A__			,				A__			,				A__										=  config_and_inputs
         A__										=  {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_tf
class 					a						(       _lowerCamelCase,	_lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
    UpperCAmelCase						 =					(
        {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
        if is_tf_available()
        else {}
    )

    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  TFRegNetModelTester(self       )
         A__										=  ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return

    @unittest.skip(reason="""RegNet does not use inputs_embeds"""       )
    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         pass

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0    ,						reason="""TF does not support backprop for grouped convolutions on CPU."""    ,						)
    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         super().test_keras_fit()

    @unittest.skip(reason="""RegNet does not support input and output embeddings"""       )
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

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

              A__										=  ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         def check_hidden_states_output(UpperCamelCase: List[str]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[int]       ):
              A__										=  model_class(UpperCamelCase       )
              A__										=  model(**self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )    ,						training=UpperCamelCase       )

              A__										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              A__										=  self.model_tester.num_stages
              self.assertEqual(len(UpperCamelCase       )    ,						expected_num_stages + 1       )

              # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]       )    ,						[self.model_tester.image_size // 2, self.model_tester.image_size // 2]    ,						)

         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  ["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
              for layer_type in layers_type:
                   A__										=  layer_type
                   A__										=  True
                   check_hidden_states_output(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

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

                   check_hidden_states_output(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         def check_equivalence(UpperCamelCase: int    ,						UpperCamelCase: int    ,						UpperCamelCase: Any    ,						UpperCamelCase: Union[str, Any]={}       ):
              A__										=  model(UpperCamelCase    ,						return_dict=UpperCamelCase    ,						**UpperCamelCase       )
              A__										=  model(UpperCamelCase    ,						return_dict=UpperCamelCase    ,						**UpperCamelCase       ).to_tuple()

              def recursive_check(UpperCamelCase: Any    ,						UpperCamelCase: List[str]       ):
                   if isinstance(UpperCamelCase    ,						(List, Tuple)       ):
                        for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase    ,						UpperCamelCase       ):
                             recursive_check(UpperCamelCase    ,						UpperCamelCase       )
                   elif tuple_object is None:
                        return
                   else:
                        self.assertTrue(
                            all(tf.equal(UpperCamelCase    ,						UpperCamelCase       )       )    ,						msg=(
                                """Tuple and dict output are not equal. Difference:"""
                                f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"""
                            )    ,						)

                   recursive_check(UpperCamelCase    ,						UpperCamelCase       )

         for model_class in self.all_model_classes:
              A__										=  model_class(UpperCamelCase       )

              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )
              check_equivalence(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase       )
              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase       )
              check_equivalence(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )
              check_equivalence(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						{"""output_hidden_states""": True}       )

              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase       )
              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase       )
              check_equivalence(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						{"""output_hidden_states""": True}       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase       )





    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  TFRegNetModel.from_pretrained(UpperCamelCase       )
              self.assertIsNotNone(UpperCamelCase       )





def  _snake_case     (    ):
     A__										=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
     return image



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



    """simple docstring"""



    @cached_property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return (
             AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
             if is_vision_available()
             else None
         )





    @slow
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

         A__										=  self.default_image_processor
         A__										=  prepare_img()
         A__										=  image_processor(images=UpperCamelCase    ,						return_tensors="""tf"""       )

         # forward pass
         A__										=  model(**UpperCamelCase    ,						training=UpperCamelCase       )

         # verify the logits
         A__										=  tf.TensorShape((1, 10_00)       )
         self.assertEqual(outputs.logits.shape    ,						UpperCamelCase       )

         A__										=  tf.constant([-0.4_180, -1.5_051, -3.4_836]       )

         tf.debugging.assert_near(outputs.logits[0, :3]    ,						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  _snake_case     (    ):
     A__										=  argparse.ArgumentParser()
     parser.add_argument("""--model_ckpt"""					,   type=UpperCAmelCase_					,   default="""microsoft/unixcoder-base-nine"""			)
     parser.add_argument("""--num_epochs"""					,   type=UpperCAmelCase_					,   default=5			)
     parser.add_argument("""--batch_size"""					,   type=UpperCAmelCase_					,   default=6			)
     parser.add_argument("""--gradient_accumulation_steps"""					,   type=UpperCAmelCase_					,   default=1			)
     parser.add_argument("""--freeze"""					,   type=UpperCAmelCase_					,   default=UpperCAmelCase_			)
     parser.add_argument("""--learning_rate"""					,   type=UpperCAmelCase_					,   default=5e-4			)
     parser.add_argument("""--seed"""					,   type=UpperCAmelCase_					,   default=0			)
     parser.add_argument("""--lr_scheduler_type"""					,   type=UpperCAmelCase_					,   default="""cosine"""			)
     parser.add_argument("""--num_warmup_steps"""					,   type=UpperCAmelCase_					,   default=10			)
     parser.add_argument("""--weight_decay"""					,   type=UpperCAmelCase_					,   default=0.01			)
     parser.add_argument("""--output_dir"""					,   type=UpperCAmelCase_					,   default="""./results"""			)
     return parser.parse_args()


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						load('accuracy')





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



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



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






         """simple docstring"""


         super().__init__()
         A__										=  trainer





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






         """simple docstring"""


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





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

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

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

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

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

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

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

     A__										=  TrainingArguments(
         output_dir=args.output_dir					,   learning_rate=args.learning_rate					,   lr_scheduler_type=args.lr_scheduler_type					,   evaluation_strategy="""epoch"""					,   save_strategy="""epoch"""					,   logging_strategy="""epoch"""					,   per_device_train_batch_size=args.batch_size					,   per_device_eval_batch_size=args.batch_size					,   num_train_epochs=args.num_epochs					,   gradient_accumulation_steps=args.gradient_accumulation_steps					,   weight_decay=0.01					,   metric_for_best_model="""accuracy"""					,   run_name="""complexity-java"""					,   report_to="""wandb"""					,   )

     A__										=  Trainer(
         model=UpperCAmelCase_					,   args=UpperCAmelCase_					,   train_dataset=tokenized_datasets["""train"""]					,   eval_dataset=tokenized_datasets["""valid"""]					,   tokenizer=UpperCAmelCase_					,   data_collator=UpperCAmelCase_					,   compute_metrics=UpperCAmelCase_					,   )

     print("""Training..."""			)
     trainer.add_callback(CustomCallback(UpperCAmelCase_			)			)
     trainer.train()


if __name__ == "__main__":
 main()






