


import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		CanineTokenizer
  _lowercase									=		False

  def    _UpperCamelCase( self			:  Dict       ):
        super().setUp()
        a__	:  str     =   CanineTokenizer()
        tokenizer.save_pretrained(self.tmpdirname       )

  @cached_property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        return CanineTokenizer.from_pretrained("google/canine-s"       )

  def    _UpperCamelCase( self			:  Optional[Any]				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  Dict     =   self.tokenizer_class.from_pretrained(self.tmpdirname				,				**lowerCamelCase__       )
        a__	:  Optional[int]     =   1_024
        return tokenizer

  @require_torch
  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Dict     =   self.canine_tokenizer
        a__	:  Union[str, Any]     =   ["Life is like a box of chocolates.", "You never know what you're gonna get."]
        # fmt: off
        a__	:  Dict     =   [57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
        # fmt: on
        a__	:  str     =   tokenizer(lowerCamelCase__				,				padding=lowerCamelCase__				,				return_tensors="pt"       )
        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  str     =   list(batch.input_ids.numpy()[0]       )

        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        self.assertEqual((2, 39)				,				batch.input_ids.shape       )
        self.assertEqual((2, 39)				,				batch.attention_mask.shape       )

  @require_torch
  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Optional[Any]     =   self.canine_tokenizer
        a__	:  List[Any]     =   ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
        a__	:  Any     =   tokenizer(lowerCamelCase__				,				padding=lowerCamelCase__				,				return_tensors="pt"       )
        # check if input_ids, attention_mask and token_type_ids are returned
        self.assertIn("input_ids"				,				lowerCamelCase__       )
        self.assertIn("attention_mask"				,				lowerCamelCase__       )
        self.assertIn("token_type_ids"				,				lowerCamelCase__       )

  @require_torch
  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  int     =   self.canine_tokenizer
        a__	:  List[Any]     =   [
            "What's the weater?",
            "It's about 25 degrees.",
        ]
        a__	:  Dict     =   tokenizer(
            text_target=lowerCamelCase__				,				max_length=32				,				padding="max_length"				,				truncation=lowerCamelCase__				,				return_tensors="pt"       )
        self.assertEqual(32				,				targets["input_ids"].shape[1]       )

  def    _UpperCamelCase( self			:  int       ):
        # safety check on max_len default value so we are sure the test works
        a__	:  Union[str, Any]     =   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    self.assertNotEqual(tokenizer.model_max_length				,				42       )

        # Now let's start the test
        a__	:  List[str]     =   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    # Isolate this from the other tests because we save additional tokens/etc
                    a__	:  Optional[Any]     =   tempfile.mkdtemp()

                    a__	:  int     =   " He is very happy, UNwant\u00E9d,running"
                    a__	:  Optional[int]     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    tokenizer.save_pretrained(lowerCamelCase__       )

                    a__	:  int     =   tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                    a__	:  Optional[int]     =   after_tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

                    shutil.rmtree(lowerCamelCase__       )

        a__	:  Union[str, Any]     =   self.get_tokenizers(model_max_length=42       )
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    # Isolate this from the other tests because we save additional tokens/etc
                    a__	:  Tuple     =   tempfile.mkdtemp()

                    a__	:  int     =   " He is very happy, UNwant\u00E9d,running"

                    a__	:  Any     =   tokenizer.additional_special_tokens

                    # We can add a new special token for Canine as follows:
                    a__	:  List[Any]     =   chr(0XE007       )
                    additional_special_tokens.append(lowerCamelCase__       )
                    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}       )
                    a__	:  str     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    tokenizer.save_pretrained(lowerCamelCase__       )

                    a__	:  Tuple     =   tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                    a__	:  List[Any]     =   after_tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )
                    self.assertIn(lowerCamelCase__				,				after_tokenizer.additional_special_tokens       )
                    self.assertEqual(after_tokenizer.model_max_length				,				42       )

                    a__	:  List[str]     =   tokenizer.__class__.from_pretrained(lowerCamelCase__				,				model_max_length=43       )
                    self.assertEqual(tokenizer.model_max_length				,				43       )

                    shutil.rmtree(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int       ):
        a__	:  Optional[int]     =   self.get_tokenizers(do_lower_case=lowerCamelCase__       )
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    a__,	a__	:  Tuple     =   self.get_clean_sequence(lowerCamelCase__       )

                    # a special token for Canine can be defined as follows:
                    a__	:  List[Any]     =   0XE005
                    a__	:  Optional[Any]     =   chr(lowerCamelCase__       )

                    tokenizer.add_special_tokens({"cls_token": special_token}       )
                    a__	:  str     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    self.assertEqual(len(lowerCamelCase__       )				,				1       )

                    a__	:  Optional[Any]     =   tokenizer.decode(ids + encoded_special_token				,				clean_up_tokenization_spaces=lowerCamelCase__       )
                    a__	:  Any     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )

                    a__	:  Optional[int]     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    a__	:  Any     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    self.assertEqual(lowerCamelCase__				,				input_encoded + special_token_id       )

                    a__	:  List[Any]     =   tokenizer.decode(lowerCamelCase__				,				skip_special_tokens=lowerCamelCase__       )
                    self.assertTrue(special_token not in decoded       )

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Union[str, Any]     =   self.get_tokenizers(do_lower_case=lowerCamelCase__       )
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    a__	:  List[Any]     =   chr(0XE005       )
                    a__	:  str     =   chr(0XE006       )

                    # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                    tokenizer.add_tokens([SPECIAL_TOKEN_1]				,				special_tokens=lowerCamelCase__       )
                    # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                    # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                    tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}       )

                    a__	:  Dict     =   tokenizer.tokenize(lowerCamelCase__       )
                    a__	:  int     =   tokenizer.tokenize(lowerCamelCase__       )

                    self.assertEqual(len(lowerCamelCase__       )				,				1       )
                    self.assertEqual(len(lowerCamelCase__       )				,				1       )
                    self.assertEqual(token_a[0]				,				lowerCamelCase__       )
                    self.assertEqual(token_a[0]				,				lowerCamelCase__       )

  @require_tokenizers
  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Any     =   self.get_tokenizers(do_lower_case=lowerCamelCase__       )
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    # a special token for Canine can be defined as follows:
                    a__	:  List[Any]     =   0XE006
                    a__	:  Union[str, Any]     =   chr(lowerCamelCase__       )

                    a__	:  Optional[Any]     =   AddedToken(lowerCamelCase__				,				lstrip=lowerCamelCase__       )
                    tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}       )

                    with tempfile.TemporaryDirectory() as tmp_dir_name:
                          tokenizer.save_pretrained(lowerCamelCase__       )
                          tokenizer.from_pretrained(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  str     =   []
        if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

        if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

        for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(lowerCamelCase__       )

                    with open(os.path.join(lowerCamelCase__				,				"special_tokens_map.json"       )				,				encoding="utf-8"       ) as json_file:
                          a__	:  Dict     =   json.load(lowerCamelCase__       )

                    with open(os.path.join(lowerCamelCase__				,				"tokenizer_config.json"       )				,				encoding="utf-8"       ) as json_file:
                          a__	:  Tuple     =   json.load(lowerCamelCase__       )

                    # a special token for Canine can be defined as follows:
                    a__	:  Optional[int]     =   0XE006
                    a__	:  Tuple     =   chr(lowerCamelCase__       )

                    a__	:  List[Any]     =   [new_token_a]
                    a__	:  Optional[Any]     =   [new_token_a]

                    with open(os.path.join(lowerCamelCase__				,				"special_tokens_map.json"       )				,				"w"				,				encoding="utf-8"       ) as outfile:
                          json.dump(lowerCamelCase__				,				lowerCamelCase__       )
                    with open(os.path.join(lowerCamelCase__				,				"tokenizer_config.json"       )				,				"w"				,				encoding="utf-8"       ) as outfile:
                          json.dump(lowerCamelCase__				,				lowerCamelCase__       )

                    # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                    # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                    # "special_tokens_map.json" files
                    a__	:  Any     =   tokenizer_class.from_pretrained(lowerCamelCase__				,				extra_ids=0       )
                    self.assertIn(lowerCamelCase__				,				tokenizer_without_change_in_init.additional_special_tokens       )
                    # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                    self.assertEqual(
                        [new_token_a]				,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                            tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]       )       )				,				)

                    a__	:  Optional[Any]     =   0XE007
                    a__	:  str     =   chr(lowerCamelCase__       )
                    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                    a__	:  List[str]     =   [AddedToken(lowerCamelCase__				,				lstrip=lowerCamelCase__       )]
                    a__	:  List[str]     =   tokenizer_class.from_pretrained(
                        lowerCamelCase__				,				additional_special_tokens=lowerCamelCase__				,				extra_ids=0       )

                    self.assertIn(lowerCamelCase__				,				tokenizer.additional_special_tokens       )
                    # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                    self.assertEqual(
                        [new_token_a]				,				tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]       )       )       )

  @require_tokenizers
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  int     =   self.get_tokenizers(do_lower_case=lowerCamelCase__       )
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    a__	:  Optional[Any]     =   "hello world"
                    if self.space_between_special_tokens:
                          a__	:  Any     =   "[CLS] hello world [SEP]"
                    else:
                          a__	:  str     =   input
                    a__	:  Any     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
                    a__	:  Dict     =   tokenizer.decode(lowerCamelCase__				,				spaces_between_special_tokens=self.space_between_special_tokens       )
                    self.assertIn(lowerCamelCase__				,				[output, output.lower()]       )

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Dict     =   self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                    a__	:  int     =   [
                        "bos_token",
                        "eos_token",
                        "unk_token",
                        "sep_token",
                        "pad_token",
                        "cls_token",
                        "mask_token",
                    ]

        a__	:  Optional[Any]     =   "a"
        a__	:  Optional[Any]     =   ord(lowerCamelCase__       )

        for attr in attributes_list:
              setattr(lowerCamelCase__				,				attr + "_id"				,				lowerCamelCase__       )
              self.assertEqual(getattr(lowerCamelCase__				,				lowerCamelCase__       )				,				lowerCamelCase__       )
              self.assertEqual(getattr(lowerCamelCase__				,				attr + "_id"       )				,				lowerCamelCase__       )

              setattr(lowerCamelCase__				,				attr + "_id"				,				lowerCamelCase__       )
              self.assertEqual(getattr(lowerCamelCase__				,				lowerCamelCase__       )				,				lowerCamelCase__       )
              self.assertEqual(getattr(lowerCamelCase__				,				attr + "_id"       )				,				lowerCamelCase__       )

        setattr(lowerCamelCase__				,				"additional_special_tokens_ids"				,				[]       )
        self.assertListEqual(getattr(lowerCamelCase__				,				"additional_special_tokens"       )				,				[]       )
        self.assertListEqual(getattr(lowerCamelCase__				,				"additional_special_tokens_ids"       )				,				[]       )

        a__	:  List[Any]     =   0XE006
        a__	:  Dict     =   chr(lowerCamelCase__       )
        setattr(lowerCamelCase__				,				"additional_special_tokens_ids"				,				[additional_special_token_id]       )
        self.assertListEqual(getattr(lowerCamelCase__				,				"additional_special_tokens"       )				,				[additional_special_token]       )
        self.assertListEqual(getattr(lowerCamelCase__				,				"additional_special_tokens_ids"       )				,				[additional_special_token_id]       )

  def    _UpperCamelCase( self			:  str       ):
        pass

  def    _UpperCamelCase( self			:  int       ):
        pass

  def    _UpperCamelCase( self			:  Any       ):
        pass

  def    _UpperCamelCase( self			:  Any       ):
        pass

  def    _UpperCamelCase( self			:  List[str]       ):
        pass

  def    _UpperCamelCase( self			:  List[str]       ):
        pass

  def    _UpperCamelCase( self			:  List[Any]       ):
        pass

  def    _UpperCamelCase( self			:  Optional[int]       ):
        pass






"""simple docstring"""







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


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     "sequence-classification"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				if type(SCREAMING_SNAKE_CASE__		) == dict:
					A__											=					Namespace(**SCREAMING_SNAKE_CASE__		)
				A__											=					glue_output_modes[hparams.task]
				A__											=					glue_tasks_num_labels[hparams.task]

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


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return self.model(**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				A__											=					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

				A__											=					self(**SCREAMING_SNAKE_CASE__		)
				A__											=					outputs[0]

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


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.hparams
				A__											=					processors[args.task]()
				A__											=					processor.get_labels()

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False		)      ->	DataLoader:
				A__											=					"dev" if mode == "test" else mode

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

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

				A__											=					self(**SCREAMING_SNAKE_CASE__		)
				A__							,							A__											=					outputs[:2]
				A__											=					logits.detach().cpu().numpy()
				A__											=					inputs["labels"].detach().cpu().numpy()

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	tuple:
				A__											=					torch.stack([x["val_loss"] for x in outputs]		).mean().detach().cpu().item()
				A__											=					np.concatenate([x["pred"] for x in outputs]						,	axis=0		)

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

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

				A__											=					{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)}

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				A__							,							A__							,							A__											=					self._eval_end(SCREAMING_SNAKE_CASE__		)
				A__											=					ret["log"]
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				A__							,							A__							,							A__											=					self._eval_end(SCREAMING_SNAKE_CASE__		)
				A__											=					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   snake_case__	(      SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				parser.add_argument(
				    "--max_seq_length"						,	default=128						,	type=SCREAMING_SNAKE_CASE__						,	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=SCREAMING_SNAKE_CASE__						,	required=SCREAMING_SNAKE_CASE__						,	help="The GLUE task to run"						,	)
				parser.add_argument(
				    "--gpus"						,	default=0						,	type=SCREAMING_SNAKE_CASE__						,	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       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					argparse.ArgumentParser()
	add_generic_args(UpperCAmelCase_,	os.getcwd()     )
	A__											=					GLUETransformer.add_model_specific_args(UpperCAmelCase_,	os.getcwd()     )
	A__											=					parser.parse_args()

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

	A__											=					GLUETransformer(UpperCAmelCase_     )
	A__											=					generic_train(UpperCAmelCase_,	UpperCAmelCase_     )

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


if __name__ == "__main__":
			main()




