






"""simple docstring"""






import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


UpperCAmelCase_   : List[str]   			= get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCAmelCase_   : Optional[Any]   			= get_tests_dir("""fixtures/vocab.json""")
UpperCAmelCase_   : List[Any]   			= get_tests_dir("""fixtures""")




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h''')
          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaConfig()
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h''')

              # save in new folder
              model_config.save_pretrained(lowercase_)
              processor.save_pretrained(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(lowercase_						,	os.path.join(lowercase_						,	lowercase_))
              copyfile(lowercase_						,	os.path.join(lowercase_						,	'''vocab.json'''))

              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaFeatureExtractor()
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h''')

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaProcessor(lowercase_						,	lowercase_)

              # save in new folder
              processor.save_pretrained(lowercase_)

              # drop `processor_class` in tokenizer
              with open(os.path.join(lowercase_						,	lowercase_)						,	'''r''') as f:
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		json.load(lowercase_)
                  config_dict.pop('''processor_class''')

              with open(os.path.join(lowercase_						,	lowercase_)						,	'''w''') as f:
                  f.write(json.dumps(lowercase_))

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaFeatureExtractor()
              SCREAMING_SNAKE_CASE_      :  int    						=		AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h''')

              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaProcessor(lowercase_						,	lowercase_)

              # save in new folder
              processor.save_pretrained(lowercase_)

              # drop `processor_class` in feature extractor
              with open(os.path.join(lowercase_						,	lowercase_)						,	'''r''') as f:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		json.load(lowercase_)
                  config_dict.pop('''processor_class''')

              with open(os.path.join(lowercase_						,	lowercase_)						,	'''w''') as f:
                  f.write(json.dumps(lowercase_))

              SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaConfig(processor_class='''Wav2Vec2Processor''')
              model_config.save_pretrained(lowercase_)
              # copy relevant files
              copyfile(lowercase_						,	os.path.join(lowercase_						,	'''vocab.json'''))
              # create emtpy sample processor
              with open(os.path.join(lowercase_						,	lowercase_)						,	'''w''') as f:
                  f.write('''{}''')

              SCREAMING_SNAKE_CASE_      :  int    						=		AutoProcessor.from_pretrained(lowercase_)

          self.assertIsInstance(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  str    						=		AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
          self.assertTrue(processor.special_attribute_present)
          self.assertEqual(processor.__class__.__name__						,	'''NewProcessor''')

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present)
          self.assertEqual(feature_extractor.__class__.__name__						,	'''NewFeatureExtractor''')

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		processor.tokenizer
          self.assertTrue(tokenizer.special_attribute_present)
          if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__						,	'''NewTokenizerFast''')

              # Test we can also load the slow version
              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_						,	use_fast=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		new_processor.tokenizer
              self.assertTrue(new_tokenizer.special_attribute_present)
              self.assertEqual(new_tokenizer.__class__.__name__						,	'''NewTokenizer''')
          else:
              self.assertEqual(tokenizer.__class__.__name__						,	'''NewTokenizer''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          try:
              AutoConfig.register('''custom'''						,	lowercase_)
              AutoFeatureExtractor.register(lowercase_						,	lowercase_)
              AutoTokenizer.register(lowercase_						,	slow_tokenizer_class=lowercase_)
              AutoProcessor.register(lowercase_						,	lowercase_)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(lowercase_):
                  AutoProcessor.register(lowercase_						,	lowercase_)

              # Now that the config is registered, it can be used as any other config with the auto-API
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		CustomFeatureExtractor.from_pretrained(lowercase_)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		os.path.join(lowercase_						,	'''vocab.txt''')
                  with open(lowercase_						,	'''w'''						,	encoding='''utf-8''') as vocab_writer:
                      vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]))
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		CustomTokenizer(lowercase_)

              SCREAMING_SNAKE_CASE_      :  Dict    						=		CustomProcessor(lowercase_						,	lowercase_)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  str    						=		AutoProcessor.from_pretrained(lowercase_)
                  self.assertIsInstance(lowercase_						,	lowercase_)

          finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                  del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  False




          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  False




          class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






                '''simple docstring'''

                __UpperCamelCase	 =  "AutoFeatureExtractor"
                __UpperCamelCase	 =  "AutoTokenizer"
                __UpperCamelCase	 =  False

          try:
              AutoConfig.register('''custom'''						,	lowercase_)
              AutoFeatureExtractor.register(lowercase_						,	lowercase_)
              AutoTokenizer.register(lowercase_						,	slow_tokenizer_class=lowercase_)
              AutoProcessor.register(lowercase_						,	lowercase_)
              # If remote code is not set, the default is to use local classes.
              SCREAMING_SNAKE_CASE_      :  int    						=		AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''')
              self.assertEqual(processor.__class__.__name__						,	'''NewProcessor''')
              self.assertFalse(processor.special_attribute_present)
              self.assertFalse(processor.feature_extractor.special_attribute_present)
              self.assertFalse(processor.tokenizer.special_attribute_present)

              # If remote code is disabled, we load the local ones.
              SCREAMING_SNAKE_CASE_      :  Any    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
              self.assertEqual(processor.__class__.__name__						,	'''NewProcessor''')
              self.assertFalse(processor.special_attribute_present)
              self.assertFalse(processor.feature_extractor.special_attribute_present)
              self.assertFalse(processor.tokenizer.special_attribute_present)

              # If remote is enabled, we load from the Hub.
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor'''						,	trust_remote_code=lowercase_)
              self.assertEqual(processor.__class__.__name__						,	'''NewProcessor''')
              self.assertTrue(processor.special_attribute_present)
              self.assertTrue(processor.feature_extractor.special_attribute_present)
              self.assertTrue(processor.tokenizer.special_attribute_present)

          finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                  del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert''')
          self.assertEqual(processor.__class__.__name__						,	'''BertTokenizerFast''')



      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext''')
          self.assertEqual(processor.__class__.__name__						,	'''ConvNextImageProcessor''')




@is_staging_test
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		TOKEN
          HfFolder.save_token(lowercase_)
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  str):




          '''simple docstring'''


          try:
              delete_repo(token=cls._token						,	repo_id='''test-processor''')
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token						,	repo_id='''valid_org/test-processor-org''')
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token						,	repo_id='''test-dynamic-processor''')
          except HTTPError:
              pass
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaProcessor.from_pretrained(lowercase_)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(lowercase_						,	'''test-processor''')						,	push_to_hub=lowercase_						,	use_auth_token=self._token)

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		WavaVecaProcessor.from_pretrained(F'{USER}/test-processor')
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(lowercase_						,	getattr(new_processor.feature_extractor						,	lowercase_))
              self.assertDictEqual(new_processor.tokenizer.get_vocab()						,	processor.tokenizer.get_vocab())
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		WavaVecaProcessor.from_pretrained(lowercase_)

          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(lowercase_						,	'''test-processor-org''')						,	push_to_hub=lowercase_						,	use_auth_token=self._token						,	organization='''valid_org'''						,	)

              SCREAMING_SNAKE_CASE_      :  str    						=		WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org''')
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(lowercase_						,	getattr(new_processor.feature_extractor						,	lowercase_))
              self.assertDictEqual(new_processor.tokenizer.get_vocab()						,	processor.tokenizer.get_vocab())



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		CustomFeatureExtractor.from_pretrained(lowercase_)

          with tempfile.TemporaryDirectory() as tmp_dir:
              SCREAMING_SNAKE_CASE_      :  Any    						=		os.path.join(lowercase_						,	'''vocab.txt''')
              with open(lowercase_						,	'''w'''						,	encoding='''utf-8''') as vocab_writer:
                  vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]))
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		CustomTokenizer(lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		CustomProcessor(lowercase_						,	lowercase_)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F'{USER}/test-dynamic-processor'						,	token=self._token)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Repository(lowercase_						,	clone_from=F'{USER}/test-dynamic-processor'						,	token=self._token)
              processor.save_pretrained(lowercase_)

              # This has added the proper auto_map field to the feature extractor config
              self.assertDictEqual(
                  processor.feature_extractor.auto_map						,	{
                      '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
                      '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                  }						,	)

              # This has added the proper auto_map field to the tokenizer config
              with open(os.path.join(lowercase_						,	'''tokenizer_config.json''')) as f:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		json.load(lowercase_)
              self.assertDictEqual(
                  tokenizer_config['''auto_map''']						,	{
                      '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                      '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                  }						,	)

              # The code has been copied from fixtures
              self.assertTrue(os.path.isfile(os.path.join(lowercase_						,	'''custom_feature_extraction.py''')))
              self.assertTrue(os.path.isfile(os.path.join(lowercase_						,	'''custom_tokenization.py''')))
              self.assertTrue(os.path.isfile(os.path.join(lowercase_						,	'''custom_processing.py''')))

              repo.push_to_hub()

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoProcessor.from_pretrained(F'{USER}/test-dynamic-processor'						,	trust_remote_code=lowercase_)
          # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
          self.assertEqual(new_processor.__class__.__name__						,	'''CustomProcessor''')








import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


SCREAMING_SNAKE_CASE__       :      Dict  =   logging.getLogger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """sequence-classification"""







    def __init__(    self     ,  UpperCamelCase__     )     ->      List[Any]:
           if type(UpperCamelCase__     ) == dict:
                  lowerCamelCase   :      int			 =					Namespace(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					glue_output_modes[hparams.task]
           lowerCamelCase   :      int			 =					glue_tasks_num_labels[hparams.task]

           super().__init__(UpperCamelCase__     ,  UpperCamelCase__     ,  self.mode     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Tuple:
           return self.model(**UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Union[str, Any]			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

           if self.config.model_type not in ["distilbert", "bart"]:
                  lowerCamelCase   :      List[str]			 =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

           lowerCamelCase   :      Optional[int]			 =					self(**UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					outputs[0]

           lowerCamelCase   :      str			 =					self.trainer.lr_schedulers[0]["scheduler"]
           lowerCamelCase   :      Optional[int]			 =					{"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
           return {"loss": loss, "log": tensorboard_logs}







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Any			 =					self.hparams
           lowerCamelCase   :      Union[str, Any]			 =					processors[args.task]()
           lowerCamelCase   :      Optional[int]			 =					processor.get_labels()

           for mode in ["train", "dev"]:
                  lowerCamelCase   :      Optional[Any]			 =					self._feature_file(UpperCamelCase__     )
                  if os.path.exists(UpperCamelCase__     ) and not args.overwrite_cache:
                         logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
                  else:
                         logger.info("Creating features from dataset file at %s"     ,  args.data_dir     )
                         lowerCamelCase   :      List[str]			 =					(
                             processor.get_dev_examples(args.data_dir     )
                             if mode == "dev"
                             else processor.get_train_examples(args.data_dir     )
                         )
                         lowerCamelCase   :      Dict			 =					convert_examples_to_features(
                             UpperCamelCase__     ,  self.tokenizer     ,  max_length=args.max_seq_length     ,  label_list=self.labels     ,  output_mode=args.glue_output_mode     ,  )
                         logger.info("Saving features into cached file %s"     ,  UpperCamelCase__     )
                         torch.save(UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     )     ->      DataLoader:
           lowerCamelCase   :      str			 =					"dev" if mode == "test" else mode

           lowerCamelCase   :      int			 =					self._feature_file(UpperCamelCase__     )
           logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					torch.load(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.input_ids for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      str			 =					torch.tensor([f.attention_mask for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.token_type_ids for f in features]     ,  dtype=torch.long     )
           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Any			 =					torch.tensor([f.label for f in features]     ,  dtype=torch.long     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      Union[str, Any]			 =					torch.tensor([f.label for f in features]     ,  dtype=torch.float     )

           return DataLoader(
               TensorDataset(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ,  batch_size=UpperCamelCase__     ,  shuffle=UpperCamelCase__     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

           if self.config.model_type not in ["distilbert", "bart"]:
                  lowerCamelCase   :      Tuple			 =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

           lowerCamelCase   :      Dict			 =					self(**UpperCamelCase__     )
           lowerCamelCase ,      lowerCamelCase   :      Any			 =					outputs[:2]
           lowerCamelCase   :      Union[str, Any]			 =					logits.detach().cpu().numpy()
           lowerCamelCase   :      Optional[Any]			 =					inputs["labels"].detach().cpu().numpy()

           return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      tuple:
           lowerCamelCase   :      Union[str, Any]			 =					torch.stack([x["val_loss"] for x in outputs]     ).mean().detach().cpu().item()
           lowerCamelCase   :      Optional[int]			 =					np.concatenate([x["pred"] for x in outputs]     ,  axis=0     )

           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Union[str, Any]			 =					np.argmax(UpperCamelCase__     ,  axis=1     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      str			 =					np.squeeze(UpperCamelCase__     )

           lowerCamelCase   :      List[Any]			 =					np.concatenate([x["target"] for x in outputs]     ,  axis=0     )
           lowerCamelCase   :      List[str]			 =					[[] for _ in range(out_label_ids.shape[0]     )]
           lowerCamelCase   :      Optional[int]			 =					[[] for _ in range(out_label_ids.shape[0]     )]

           lowerCamelCase   :      Dict			 =					{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task     ,  UpperCamelCase__     ,  UpperCamelCase__     )}

           lowerCamelCase   :      List[str]			 =					dict(results.items()     )
           lowerCamelCase   :      Optional[int]			 =					results
           return ret, preds_list, out_label_list







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
           return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    @staticmethod
    def     _lowercase     (    UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           BaseTransformer.add_model_specific_args(UpperCamelCase__     ,  UpperCamelCase__     )
           parser.add_argument(
               "--max_seq_length"     ,  default=128     ,  type=UpperCamelCase__     ,  help=(
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )     ,  )

           parser.add_argument(
               "--task"     ,  default=""     ,  type=UpperCamelCase__     ,  required=UpperCamelCase__     ,  help="The GLUE task to run"     ,  )
           parser.add_argument(
               "--gpus"     ,  default=0     ,  type=UpperCamelCase__     ,  help="The number of GPUs allocated for this, it is by default 0 meaning none"     ,  )

           parser.add_argument(
               "--overwrite_cache"     ,  action="store_true"     ,  help="Overwrite the cached training and evaluation sets"     )

           return parser




def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					argparse.ArgumentParser()
       add_generic_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					GLUETransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              lowerCamelCase   :      int			 =					os.path.join(
                  "./results" ,f'''{args.task}_{time.strftime("%Y%m%d_%H%M%S"    )}''' ,)
              os.makedirs(args.output_dir    )

       lowerCamelCase   :      int			 =					GLUETransformer(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					generic_train(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              lowerCamelCase   :      Optional[int]			 =					sorted(glob.glob(os.path.join(args.output_dir ,"checkpoint-epoch=*.ckpt"    ) ,recursive=_SCREAMING_SNAKE_CASE    )    )
              lowerCamelCase   :      Tuple			 =					model.load_from_checkpoint(checkpoints[-1]    )
              return trainer.test(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()






