




'''simple docstring'''


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase       			= get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 		lowerCAmelCase_     (    lowerCAmelCase__    , unittest.TestCase				):







  '''simple docstring'''

  UpperCamelCase_       :	str				       =							XLMRobertaTokenizer
  UpperCamelCase_       :	str				       =							XLMRobertaTokenizerFast
  UpperCamelCase_       :	int				       =							True
  UpperCamelCase_       :	List[Any]				       =							True



  def  _snake_case			(      self							:				Optional[int]     )       ->						Optional[int]:







       '''simple docstring'''



       super().setUp()

       # We have a SentencePiece fixture for testing
       A:						Any						=						XLMRobertaTokenizer(UpperCamelCase__ ,   keep_accents=UpperCamelCase__     )
       tokenizer.save_pretrained(self.tmpdirname     )



  def  _snake_case			(      self							:				Optional[int]     )       ->						List[str]:







       '''simple docstring'''



       A:						Optional[int]						=						"<pad>"
       A:						Union[str, Any]						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__     ) ,   UpperCamelCase__     )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__     ) ,   UpperCamelCase__     )



  def  _snake_case			(      self							:				List[str]     )       ->						Union[str, Any]:







       '''simple docstring'''



       A:						str						=						list(self.get_tokenizer().get_vocab().keys()     )

       self.assertEqual(vocab_keys[0] ,   '''<s>'''     )
       self.assertEqual(vocab_keys[1] ,   '''<pad>'''     )
       self.assertEqual(vocab_keys[-1] ,   '''<mask>'''     )
       self.assertEqual(len(UpperCamelCase__     ) ,   10_02     )



  def  _snake_case			(      self							:				int     )       ->						Dict:







       '''simple docstring'''



       self.assertEqual(self.get_tokenizer().vocab_size ,   10_02     )



  def  _snake_case			(      self							:				Union[str, Any]     )       ->						Any:







       '''simple docstring'''



       A:						List[str]						=						XLMRobertaTokenizer(UpperCamelCase__ ,   keep_accents=UpperCamelCase__     )

       A:						str						=						tokenizer.tokenize('''This is a test'''     )
       self.assertListEqual(UpperCamelCase__ ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCamelCase__     ) ,   [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,   )

       A:						int						=						tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
       self.assertListEqual(
           UpperCamelCase__ ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''é''',
               '''.''',
           ] ,   )
       A:						Optional[int]						=						tokenizer.convert_tokens_to_ids(UpperCamelCase__     )
       self.assertListEqual(
           UpperCamelCase__ ,   [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ] ,   )

       A:						Any						=						tokenizer.convert_ids_to_tokens(UpperCamelCase__     )
       self.assertListEqual(
           UpperCamelCase__ ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''<unk>''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''<unk>''',
               '''.''',
           ] ,   )



  def  _snake_case			(      self							:				List[Any]     )       ->						Any:







       '''simple docstring'''



       if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

       A:						Dict						=						(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                 A:						str						=						self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ ,   **UpperCamelCase__     )
                 A:						Any						=						self.tokenizer_class.from_pretrained(UpperCamelCase__ ,   **UpperCamelCase__     )

                 A:						Optional[Any]						=						tempfile.mkdtemp()

                 A:						Dict						=						tokenizer_r.save_pretrained(UpperCamelCase__     )
                 A:						Tuple						=						tokenizer_p.save_pretrained(UpperCamelCase__     )

                 # Checks it save with the same files + the tokenizer.json file for the fast one
                 self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )
                 A:						Any						=						tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f     )
                 self.assertSequenceEqual(UpperCamelCase__ ,   UpperCamelCase__     )

                 # Checks everything loads correctly in the same way
                 A:						int						=						tokenizer_r.from_pretrained(UpperCamelCase__     )
                 A:						Any						=						tokenizer_p.from_pretrained(UpperCamelCase__     )

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase__ ,   UpperCamelCase__     )     )
                      # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                      # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                 shutil.rmtree(UpperCamelCase__     )

                 # Save tokenizer rust, legacy_format=True
                 A:						str						=						tempfile.mkdtemp()

                 A:						int						=						tokenizer_r.save_pretrained(UpperCamelCase__ ,   legacy_format=UpperCamelCase__     )
                 A:						List[str]						=						tokenizer_p.save_pretrained(UpperCamelCase__     )

                 # Checks it save with the same files
                 self.assertSequenceEqual(UpperCamelCase__ ,   UpperCamelCase__     )

                 # Checks everything loads correctly in the same way
                 A:						Optional[int]						=						tokenizer_r.from_pretrained(UpperCamelCase__     )
                 A:						Any						=						tokenizer_p.from_pretrained(UpperCamelCase__     )

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase__ ,   UpperCamelCase__     )     )

                 shutil.rmtree(UpperCamelCase__     )

                 # Save tokenizer rust, legacy_format=False
                 A:						List[str]						=						tempfile.mkdtemp()

                 A:						Any						=						tokenizer_r.save_pretrained(UpperCamelCase__ ,   legacy_format=UpperCamelCase__     )
                 A:						int						=						tokenizer_p.save_pretrained(UpperCamelCase__     )

                 # Checks it saved the tokenizer.json file
                 self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )

                 # Checks everything loads correctly in the same way
                 A:						List[str]						=						tokenizer_r.from_pretrained(UpperCamelCase__     )
                 A:						Optional[Any]						=						tokenizer_p.from_pretrained(UpperCamelCase__     )

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase__ ,   UpperCamelCase__     )     )

                 shutil.rmtree(UpperCamelCase__     )



  @cached_property
  def  _snake_case			(      self							:				int     )       ->						List[str]:







       '''simple docstring'''



       return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''     )



  def  _snake_case			(      self							:				List[str]     )       ->						List[Any]:







       '''simple docstring'''



       with tempfile.NamedTemporaryFile() as f:
            shutil.copyfile(UpperCamelCase__ ,   f.name     )
            A:						Optional[Any]						=						XLMRobertaTokenizer(f.name ,   keep_accents=UpperCamelCase__     )
            A:						Optional[Any]						=						pickle.dumps(UpperCamelCase__     )
       pickle.loads(UpperCamelCase__     )



  def  _snake_case			(      self							:				Tuple     )       ->						int:







       '''simple docstring'''



       if not self.test_rust_tokenizer:
            return

       A:						Dict						=						self.get_tokenizer()
       A:						Optional[Any]						=						self.get_rust_tokenizer()

       A:						int						=						"I was born in 92000, and this is falsé."

       A:						List[str]						=						tokenizer.tokenize(UpperCamelCase__     )
       A:						List[Any]						=						rust_tokenizer.tokenize(UpperCamelCase__     )
       self.assertListEqual(UpperCamelCase__ ,   UpperCamelCase__     )

       A:						str						=						tokenizer.encode(UpperCamelCase__ ,   add_special_tokens=UpperCamelCase__     )
       A:						Union[str, Any]						=						rust_tokenizer.encode(UpperCamelCase__ ,   add_special_tokens=UpperCamelCase__     )
       self.assertListEqual(UpperCamelCase__ ,   UpperCamelCase__     )

       A:						Optional[Any]						=						self.get_rust_tokenizer()
       A:						List[str]						=						tokenizer.encode(UpperCamelCase__     )
       A:						int						=						rust_tokenizer.encode(UpperCamelCase__     )
       self.assertListEqual(UpperCamelCase__ ,   UpperCamelCase__     )



  @slow
  def  _snake_case			(      self							:				Optional[Any]     )       ->						Dict:







       '''simple docstring'''



       A:						str						=						"Hello World!"
       A:						Optional[Any]						=						[0, 3_53_78, 66_61, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(UpperCamelCase__ ,   self.big_tokenizer.encode(UpperCamelCase__     )     )



  @slow
  def  _snake_case			(      self							:				str     )       ->						int:







       '''simple docstring'''



       A:						List[str]						=						(
           "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
           " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
       )
       A:						Union[str, Any]						=						[
           0,
           32_93,
           83,
           10,
           45_52,
           49_89,
           79_86,
           6_78,
           10,
           59_15,
           1_11,
           17_94_59,
           12_48_50,
           4,
           60_44,
           2_37,
           12,
           6,
           5,
           6,
           4,
           67_80,
           7_05,
           15,
           13_88,
           44,
           3_78,
           1_01_14,
           7_11,
           1_52,
           20,
           6,
           5,
           2_23_76,
           6_42,
           12_21,
           1_51_90,
           3_41_53,
           4_50,
           56_08,
           9_59,
           11_19,
           5_77_02,
           1_36,
           1_86,
           47,
           10_98,
           2_93_67,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           60_44,
           2_37,
           62_84,
           5_09_01,
           5_28,
           31,
           90,
           34,
           9_27,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(UpperCamelCase__ ,   self.big_tokenizer.encode(UpperCamelCase__     )     )




  @slow
  def  _snake_case			(      self							:				Optional[int]     )       ->						Any:







       '''simple docstring'''



       A:						int						=						{"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=UpperCamelCase__ ,   model_name='''xlm-roberta-base''' ,   revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' ,   )












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()






