


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
SCREAMING_SNAKE_CASE							    =							pytest.mark.integration


SCREAMING_SNAKE_CASE							    =							{'comet'}
SCREAMING_SNAKE_CASE							    =							importlib.util.find_spec('fairseq') is not None

SCREAMING_SNAKE_CASE							    =							{'code_eval'}
SCREAMING_SNAKE_CASE							    =							os.name == 'nt'

SCREAMING_SNAKE_CASE							    =							{'bertscore', 'frugalscore', 'perplexity'}
SCREAMING_SNAKE_CASE							    =							importlib.util.find_spec('transformers') is not None




def  a   (lowerCAmelCase__   ):
   @wraps(lowerCAmelCase__   )
   def wrapper(self		,				lowerCAmelCase__   ):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest("""\"test requires Fairseq\""""   )
      else:
         test_case(self		,				lowerCAmelCase__   )

   return wrapper




def  a   (lowerCAmelCase__   ):
   @wraps(lowerCAmelCase__   )
   def wrapper(self		,				lowerCAmelCase__   ):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest("""\"test requires transformers\""""   )
      else:
         test_case(self		,				lowerCAmelCase__   )

   return wrapper




def  a   (lowerCAmelCase__   ):
   @wraps(lowerCAmelCase__   )
   def wrapper(self		,				lowerCAmelCase__   ):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest("""\"test not supported on Windows\""""   )
      else:
         test_case(self		,				lowerCAmelCase__   )

   return wrapper




def  a   ():
   __a					=  [metric_dir.split(os.sep   )[-2] for metric_dir in glob.glob("""./metrics/*/"""   )]
   return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names()							)
@for_all_test_methods(
    __A ,					__A ,					__A							)
@local
class     __UpperCAmelCase							(       parameterized.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      {}
     _lowerCamelCase   =      None


     @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
     @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""    )
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """[...]"""
        __a					=  importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("""metrics"""  ,       __A    )    ).module_path    )
        __a					=  datasets.load.import_main_class(metric_module.__name__  ,       dataset=__A    )
        # check parameters
        __a					=  inspect.signature(metric._compute    ).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()    )    )  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(__A  ,       metric_module.__name__    ):
           with self.use_local_metrics():
              try:
                 __a					=  doctest.testmod(__A  ,       verbose=__A  ,       raise_on_error=__A    )
              except doctest.UnexpectedException as e:
                 raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed  ,       0    )
        self.assertGreater(results.attempted  ,       1    )


     @slow
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """[...]"""
        __a					=  importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("""metrics"""  ,       __A    )    ).module_path    )
        # run doctest
        with self.use_local_metrics():
           __a					=  doctest.testmod(__A  ,       verbose=__A  ,       raise_on_error=__A    )
        self.assertEqual(results.failed  ,       0    )
        self.assertGreater(results.attempted  ,       1    )


     @contextmanager
     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        if metric_name in self.INTENSIVE_CALLS_PATCHER:
           with self.INTENSIVE_CALLS_PATCHER[metric_name](__A    ):
              yield
        else:
           yield


     @contextmanager
     def 	snake_case_  (	self    ):
        def load_local_metric(__A  ,       *__A  ,       **__A    ):
           return load_metric(os.path.join("""metrics"""  ,       __A    )  ,       *__A  ,       **__A    )

        with patch("""datasets.load_metric"""    ) as mock_load_metric:
           __a					=  load_local_metric
           yield


     @classmethod
     def 	snake_case_  (	cls  ,       __A    ):
        def wrapper(__A    ):
           __a					=  contextmanager(__A    )
           __a					=  patcher
           return patcher

        return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""   )
def  a   (lowerCAmelCase__   ):
   import tensorflow.compat.va as tf
   from bleurt.score import Predictor

   tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""   )  # handle pytest cli flags


   class     __UpperCAmelCase							(       __A							):


        """simple docstring"""


        def 	snake_case_  (	self  ,       __A    ):
           assert len(input_dict["""input_ids"""]    ) == 2
           return np.array([1.03, 1.04]    )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
   with patch("""bleurt.score._create_predictor"""   ) as mock_create_predictor:
      __a					=  MockedPredictor()
      yield




@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""   )
def  a   (lowerCAmelCase__   ):
   import torch

   def bert_cos_score_idf(lowerCAmelCase__		,				lowerCAmelCase__		,				*lowerCAmelCase__		,				**lowerCAmelCase__   ):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCAmelCase__   )   )

   # mock get_model which is supposed to do download a bert model
   # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
   with patch("""bert_score.scorer.get_model"""   ), patch(
       """bert_score.scorer.bert_cos_score_idf"""   ) as mock_bert_cos_score_idf:
      __a					=  bert_cos_score_idf
      yield




@LocalMetricTest.register_intensive_calls_patcher("""comet"""   )
def  a   (lowerCAmelCase__   ):
   def load_from_checkpoint(lowerCAmelCase__   ):


      class     __UpperCAmelCase							:


           """simple docstring"""


           def 	snake_case_  (	self  ,       __A  ,       *__A  ,       **__A    ):
              assert len(__A    ) == 2
              __a					=  [0.19, 0.92]
              return scores, sum(__A    ) / len(__A    )







      return Model()

   # mock load_from_checkpoint which is supposed to do download a bert model
   # mock load_from_checkpoint which is supposed to do download a bert model
   with patch("""comet.download_model"""   ) as mock_download_model:
      __a					=  None
      with patch("""comet.load_from_checkpoint"""   ) as mock_load_from_checkpoint:
         __a					=  load_from_checkpoint
         yield




def  a   ():
   __a					=  load_metric(os.path.join("""metrics"""		,				"""seqeval"""   )   )
   __a					=  """ERROR"""
   __a					=  f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
   with pytest.raises(lowerCAmelCase__		,				match=re.escape(lowerCAmelCase__   )   ):
      metric.compute(predictions=[]		,				references=[]		,				scheme=lowerCAmelCase__   )







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  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""--model_ckpt"""		,				type=lowerCAmelCase__		,				default="""microsoft/unixcoder-base-nine"""   )
   parser.add_argument("""--num_epochs"""		,				type=lowerCAmelCase__		,				default=5   )
   parser.add_argument("""--batch_size"""		,				type=lowerCAmelCase__		,				default=6   )
   parser.add_argument("""--gradient_accumulation_steps"""		,				type=lowerCAmelCase__		,				default=1   )
   parser.add_argument("""--freeze"""		,				type=lowerCAmelCase__		,				default=lowerCAmelCase__   )
   parser.add_argument("""--learning_rate"""		,				type=lowerCAmelCase__		,				default=5E-4   )
   parser.add_argument("""--seed"""		,				type=lowerCAmelCase__		,				default=0   )
   parser.add_argument("""--lr_scheduler_type"""		,				type=lowerCAmelCase__		,				default="""cosine"""   )
   parser.add_argument("""--num_warmup_steps"""		,				type=lowerCAmelCase__		,				default=10   )
   parser.add_argument("""--weight_decay"""		,				type=lowerCAmelCase__		,				default=0.0_1   )
   parser.add_argument("""--output_dir"""		,				type=lowerCAmelCase__		,				default="""./results"""   )
   return parser.parse_args()


SCREAMING_SNAKE_CASE							    =							load('accuracy')




def  a   (lowerCAmelCase__   ):
   __a	,    __a					=  eval_pred
   __a					=  np.argmax(lowerCAmelCase__		,				axis=1   )
   return metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  trainer


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if control.should_evaluate:
           __a					=  deepcopy(__A    )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,       metric_key_prefix="""train"""    )
           return control_copy







def  a   ():
   __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(lowerCAmelCase__   ):
      __a					=  tokenizer(example["""src"""]		,				truncation=lowerCAmelCase__		,				max_length=1_024   )
      __a					=  labels.straint(example["""complexity"""]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

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

   __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=0.0_1		,				metric_for_best_model="""accuracy"""		,				run_name="""complexity-java"""		,				report_to="""wandb"""		,				)

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

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


if __name__ == "__main__":
  main()



