import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase	:    Tuple          =      get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
			from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_lowerCamelCase	:    int          =      256_047
_lowerCamelCase	:    Dict          =      256_145




@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     _SCREAMING_SNAKE_CASE							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = NllbTokenizer
	_UpperCAmelCase	:		Union[str, Any]          = NllbTokenizerFast
	_UpperCAmelCase	:		Tuple          = True
	_UpperCAmelCase	:		Union[str, Any]          = True
	_UpperCAmelCase	:		Union[str, Any]          = {}



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				super().setUp()

				# We have a SentencePiece fixture for testing
				_snake_case          =      NllbTokenizer(lowercase      ,  keep_accents=lowercase     )
				tokenizer.save_pretrained(self.tmpdirname     )



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


				'''simple docstring'''


				_snake_case          =      NllbTokenizer(lowercase      ,  keep_accents=lowercase     )

				_snake_case          =      tokenizer.tokenize('This is a test'     )
				self.assertListEqual(lowercase      ,  ['▁This', '▁is', '▁a', '▁t', 'est']     )

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowercase     )      ,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,  )

				_snake_case          =      tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
				self.assertListEqual(
				    lowercase      ,  [
				        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',
				        '.',
				    ]      ,  )
				_snake_case          =      tokenizer.convert_tokens_to_ids(lowercase     )
				self.assertListEqual(
				    lowercase      ,  [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]      ,  )

				_snake_case          =      tokenizer.convert_ids_to_tokens(lowercase     )
				self.assertListEqual(
				    lowercase      ,  [
				        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 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      self.rust_tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      self.tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )

										_snake_case          =      tempfile.mkdtemp()

										_snake_case          =      tokenizer_r.save_pretrained(lowercase     )
										_snake_case          =      tokenizer_p.save_pretrained(lowercase     )

										# 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     )     )
										_snake_case          =      tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f     )
										self.assertSequenceEqual(lowercase      ,  lowercase     )

										# Checks everything loads correctly in the same way
										_snake_case          =      tokenizer_r.from_pretrained(lowercase     )
										_snake_case          =      tokenizer_p.from_pretrained(lowercase     )

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

										shutil.rmtree(lowercase     )

										# Save tokenizer rust, legacy_format=True
										_snake_case          =      tempfile.mkdtemp()

										_snake_case          =      tokenizer_r.save_pretrained(lowercase      ,  legacy_format=lowercase     )
										_snake_case          =      tokenizer_p.save_pretrained(lowercase     )

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

										# Checks everything loads correctly in the same way
										_snake_case          =      tokenizer_r.from_pretrained(lowercase     )
										_snake_case          =      tokenizer_p.from_pretrained(lowercase     )

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

										shutil.rmtree(lowercase     )

										# Save tokenizer rust, legacy_format=False
										_snake_case          =      tempfile.mkdtemp()

										_snake_case          =      tokenizer_r.save_pretrained(lowercase      ,  legacy_format=lowercase     )
										_snake_case          =      tokenizer_p.save_pretrained(lowercase     )

										# 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
										_snake_case          =      tokenizer_r.from_pretrained(lowercase     )
										_snake_case          =      tokenizer_p.from_pretrained(lowercase     )

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

										shutil.rmtree(lowercase     )



	@require_torch
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				if not self.test_seqaseq:
							return

				_snake_case          =      self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
										# Longer text that will definitely require truncation.
										_snake_case          =      [
										    """ UN Chief Says There Is No Military Solution in Syria""",
										    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
										    """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
										    """ will only worsen the violence and misery for millions of people.""",
										]
										_snake_case          =      [
										    """Şeful ONU declară că nu există o soluţie militară în Siria""",
										    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
										    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
										    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
										]
										try:
													_snake_case          =      tokenizer.prepare_seqaseq_batch(
													    src_texts=lowercase      ,  tgt_texts=lowercase      ,  max_length=3      ,  max_target_length=10      ,  return_tensors='pt'      ,  src_lang='eng_Latn'      ,  tgt_lang='ron_Latn'      ,  )
										except NotImplementedError:
													return
										self.assertEqual(batch.input_ids.shape[1]      ,  3     )
										self.assertEqual(batch.labels.shape[1]      ,  10     )
										# max_target_length will default to max_length if not specified
										_snake_case          =      tokenizer.prepare_seqaseq_batch(
										    lowercase      ,  tgt_texts=lowercase      ,  max_length=3      ,  return_tensors='pt'     )
										self.assertEqual(batch.input_ids.shape[1]      ,  3     )
										self.assertEqual(batch.labels.shape[1]      ,  3     )

										_snake_case          =      tokenizer.prepare_seqaseq_batch(
										    src_texts=lowercase      ,  max_length=3      ,  max_target_length=10      ,  return_tensors='pt'     )
										self.assertEqual(batch_encoder_only.input_ids.shape[1]      ,  3     )
										self.assertEqual(batch_encoder_only.attention_mask.shape[1]      ,  3     )
										self.assertNotIn('decoder_input_ids'      ,  lowercase     )



	@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'     )
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				pass





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      [AddedToken('<special>'      ,  lstrip=lowercase     )]

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  additional_special_tokens=lowercase      ,  **lowercase     )
										_snake_case          =      tokenizer_r.encode('Hey this is a <special> token'     )

										_snake_case          =      tokenizer_r.encode('<special>'      ,  add_special_tokens=lowercase     )[0]

										self.assertTrue(special_token_id in r_output     )

										if self.test_slow_tokenizer:
													_snake_case          =      self.rust_tokenizer_class.from_pretrained(
													    lowercase      ,  additional_special_tokens=lowercase      ,  **lowercase      ,  )
													_snake_case          =      self.tokenizer_class.from_pretrained(
													    lowercase      ,  additional_special_tokens=lowercase      ,  **lowercase     )

													_snake_case          =      tokenizer_p.encode('Hey this is a <special> token'     )

													_snake_case          =      tokenizer_cr.encode('Hey this is a <special> token'     )

													self.assertEqual(lowercase      ,  lowercase     )
													self.assertEqual(lowercase      ,  lowercase     )
													self.assertTrue(special_token_id in p_output     )
													self.assertTrue(special_token_id in cr_output     )




@require_torch
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = "facebook/nllb-200-distilled-600M"
	_UpperCAmelCase	:		Any          = [
	    " UN Chief Says There Is No Military Solution in Syria",
	    " Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
	]
	_UpperCAmelCase	:		List[str]          = [
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
	    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
	    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
	]
	_UpperCAmelCase	:		List[Any]          = [
	    2_5_6_0_4_7,
	    1_6_2_9_7,
	    1_3_4_4_0_8,
	    8_1_6_5,
	    2_4_8_0_6_6,
	    1_4_7_3_4,
	    9_5_0,
	    1_1_3_5,
	    1_0_5_7_2_1,
	    3_5_7_3,
	    8_3,
	    2_7_3_5_2,
	    1_0_8,
	    4_9_4_8_6,
	    2,
	]



	@classmethod
	def 			A							(  cls     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      NllbTokenizer.from_pretrained(
				    cls.checkpoint_name      ,  src_lang='eng_Latn'      ,  tgt_lang='ron_Latn'     )
				_snake_case          =      1
				return cls



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']      ,  256_001     )
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']      ,  256_002     )
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']      ,  256_057     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
				self.assertListEqual(self.expected_src_tokens      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				self.assertIn(lowercase      ,  self.tokenizer.all_special_ids     )
				# fmt: off
				_snake_case          =      [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
				# fmt: on

				_snake_case          =      self.tokenizer.decode(lowercase      ,  skip_special_tokens=lowercase     )
				_snake_case          =      self.tokenizer.decode(generated_ids[1:]      ,  skip_special_tokens=lowercase     )
				self.assertEqual(lowercase      ,  lowercase     )
				self.assertNotIn(self.tokenizer.eos_token      ,  lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      ["""this is gunna be a long sentence """ * 20]
				assert isinstance(src_text[0]      ,  lowercase     )
				_snake_case          =      10
				_snake_case          =      self.tokenizer(lowercase      ,  max_length=lowercase      ,  truncation=lowercase     ).input_ids[0]
				self.assertEqual(ids[-1]      ,  2     )
				self.assertEqual(ids[0]      ,  lowercase     )
				self.assertEqual(len(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']     )      ,  [256_203, 3]     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      tempfile.mkdtemp()
				_snake_case          =      self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(lowercase     )
				_snake_case          =      NllbTokenizer.from_pretrained(lowercase     )
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,  lowercase     )



	@require_torch
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer(
				    self.src_text      ,  text_target=self.tgt_text      ,  padding=lowercase      ,  truncation=lowercase      ,  max_length=len(self.expected_src_tokens     )      ,  return_tensors='pt'      ,  )
				_snake_case          =      shift_tokens_right(
				    batch['labels']      ,  self.tokenizer.pad_token_id      ,  self.tokenizer.lang_code_to_id['ron_Latn']     )

				self.assertIsInstance(lowercase      ,  lowercase     )

				self.assertEqual((2, 15)      ,  batch.input_ids.shape     )
				self.assertEqual((2, 15)      ,  batch.attention_mask.shape     )
				_snake_case          =      batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens      ,  lowercase     )
				self.assertEqual(lowercase      ,  batch.decoder_input_ids[0, 0]     )  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens      ,  [EN_CODE]     )
				self.assertEqual(self.tokenizer.suffix_tokens      ,  [self.tokenizer.eos_token_id]     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer(self.src_text      ,  padding=lowercase      ,  truncation=lowercase      ,  max_length=3      ,  return_tensors='pt'     )
				_snake_case          =      self.tokenizer(
				    text_target=self.tgt_text      ,  padding=lowercase      ,  truncation=lowercase      ,  max_length=10      ,  return_tensors='pt'     )
				_snake_case          =      targets["""input_ids"""]
				_snake_case          =      shift_tokens_right(
				    lowercase      ,  self.tokenizer.pad_token_id      ,  decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]      ,  )

				self.assertEqual(batch.input_ids.shape[1]      ,  3     )
				self.assertEqual(batch.decoder_input_ids.shape[1]      ,  10     )



	@require_torch
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer._build_translation_inputs(
				    'A test'      ,  return_tensors='pt'      ,  src_lang='eng_Latn'      ,  tgt_lang='fra_Latn'     )

				self.assertEqual(
				    nested_simplify(lowercase     )      ,  {
				        # A, test, EOS, en_XX
				        'input_ids': [[256_047, 70, 7_356, 2]],
				        'attention_mask': [[1, 1, 1, 1]],
				        # ar_AR
				        'forced_bos_token_id': 256_057,
				    }      ,  )





	@require_torch
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      True
				_snake_case          =      self.tokenizer(
				    'UN Chief says there is no military solution in Syria'      ,  src_lang='eng_Latn'      ,  tgt_lang='fra_Latn'     )
				self.assertEqual(
				    inputs.input_ids      ,  [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]     )

				_snake_case          =      False
				_snake_case          =      self.tokenizer(
				    'UN Chief says there is no military solution in Syria'      ,  src_lang='eng_Latn'      ,  tgt_lang='fra_Latn'     )
				self.assertEqual(
				    inputs.input_ids      ,  [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]     )

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


_lowerCamelCase	:    str          =      logging.getLogger(__name__)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "sequence-classification"



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


				'''simple docstring'''


				if type(lowercase     ) == dict:
							_snake_case          =      Namespace(**lowercase     )
				_snake_case          =      glue_output_modes[hparams.task]
				_snake_case          =      glue_tasks_num_labels[hparams.task]

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



	def 			A							(  self     :   List[str]      ,  **lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				return self.model(**lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

				_snake_case          =      self(**lowercase     )
				_snake_case          =      outputs[0]

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



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.hparams
				_snake_case          =      processors[args.task]()
				_snake_case          =      processor.get_labels()

				for mode in ["train", "dev"]:
							_snake_case          =      self._feature_file(lowercase     )
							if os.path.exists(lowercase     ) and not args.overwrite_cache:
										logger.info('Loading features from cached file %s'      ,  lowercase     )
							else:
										logger.info('Creating features from dataset file at %s'      ,  args.data_dir     )
										_snake_case          =      (
										    processor.get_dev_examples(args.data_dir     )
										    if mode == 'dev'
										    else processor.get_train_examples(args.data_dir     )
										)
										_snake_case          =      convert_examples_to_features(
										    lowercase      ,  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'      ,  lowercase     )
										torch.save(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				_snake_case          =      'dev' if mode == 'test' else mode

				_snake_case          =      self._feature_file(lowercase     )
				logger.info('Loading features from cached file %s'      ,  lowercase     )
				_snake_case          =      torch.load(lowercase     )
				_snake_case          =      torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.token_type_ids for f in features]      ,  dtype=torch.long     )
				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.long     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.float     )

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



	def 			A							(  self     :   str      ,  lowercase     :   Dict      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

				_snake_case          =      self(**lowercase     )
				_snake_case     , _snake_case          =      outputs[:2]
				_snake_case          =      logits.detach().cpu().numpy()
				_snake_case          =      inputs['labels'].detach().cpu().numpy()

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



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
				_snake_case          =      np.concatenate([x['pred'] for x in outputs]      ,  axis=0     )

				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      np.argmax(lowercase      ,  axis=1     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      np.squeeze(lowercase     )

				_snake_case          =      np.concatenate([x['target'] for x in outputs]      ,  axis=0     )
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]

				_snake_case          =      {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task      ,  lowercase      ,  lowercase     )}

				_snake_case          =      dict(results.items()     )
				_snake_case          =      results
				return ret, preds_list, out_label_list



	def 			A							(  self     :   List[str]      ,  lowercase     :   list     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      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 			A							(  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				BaseTransformer.add_model_specific_args(lowercase      ,  lowercase     )
				parser.add_argument(
				    '--max_seq_length'      ,  default=128      ,  type=lowercase      ,  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=lowercase      ,  required=lowercase      ,  help='The GLUE task to run'      ,  )
				parser.add_argument(
				    '--gpus'      ,  default=0      ,  type=lowercase      ,  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_		(	)			->  List[str]:
			_snake_case          =      argparse.ArgumentParser()
			add_generic_args(__lowercase	, os.getcwd()			)
			_snake_case          =      GLUETransformer.add_model_specific_args(__lowercase	, os.getcwd()			)
			_snake_case          =      parser.parse_args()

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

			_snake_case          =      GLUETransformer(__lowercase			)
			_snake_case          =      generic_train(__lowercase	, __lowercase			)

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


if __name__ == "__main__":
			main()