'''simple docstring'''

import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase_     		= sys.version_info >= (3, 10)



def 							UpperCamelCase__			(			a__=None			,      a__=None      ):


						'''simple docstring'''






						return field(default_factory=lambda: default			,      metadata=lowerCamelCase__      )
@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      float
		lowercase   :      str
		lowercase   :      bool

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      int       =							42
		lowercase   :      str       =							field(default='toto'    ,			metadata={'help': 'help message'})

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      bool       =							False
		lowercase   :      bool       =							True
		lowercase   :      Optional[bool]       =							None

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							"""titi"""
		lowercase   :      Optional[int]       =							"""toto"""

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							"""titi"""
		lowercase   :      Dict       =							"""toto"""
		lowercase   :      Dict       =							42

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      BasicEnum       =							"toto"






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=BasicEnum(self.foo				)

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      MixedTypeEnum       =							"toto"






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=MixedTypeEnum(self.foo				)

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      Optional[int]       =							None
		lowercase   :      Optional[float]       =							field(default=__lowercase    ,			metadata={'help': 'help message'})
		lowercase   :      Optional[str]       =							None
		lowercase   :      Optional[List[str]]       =							list_field(default=[])
		lowercase   :      Optional[List[int]]       =							list_field(default=[])

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      List[int]       =							list_field(default=[])
		lowercase   :      List[int]       =							list_field(default=[1, 2, 3])
		lowercase   :      List[str]       =							list_field(default=['Hallo', 'Bonjour', 'Hello'])
		lowercase   :      List[float]       =							list_field(default=[0.1, 0.2, 0.3])

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      List[int]       =							field()
		lowercase   :      str       =							field()
		lowercase   :      BasicEnum       =							field()






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=BasicEnum(self.required_enum				)

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      "BasicEnum"       =							field()
		lowercase   :      "Optional[bool]"       =							None
		lowercase   :      "str"       =							field(default='toto'    ,			metadata={'help': 'help message'})
		lowercase   :      "List[str]"       =							list_field(default=['Hallo', 'Bonjour', 'Hello'])


if is_python_no_less_than_3_10:
							@dataclass
							class   SCREAMING_SNAKE_CASE			:







									"""simple docstring"""
									lowercase   :      bool       =							False
									lowercase   :      bool       =							True
									lowercase   :      bool | None       =							None

							@dataclass
							class   SCREAMING_SNAKE_CASE			:







									"""simple docstring"""
									lowercase   :      int | None       =							None
									lowercase   :      float | None       =							field(default=__lowercase    ,			metadata={'help': 'help message'})
									lowercase   :      str | None       =							None
									lowercase   :      list[str] | None       =							list_field(default=[])
									lowercase   :      list[int] | None       =							list_field(default=[])


class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     str:
								self.assertEqual(len(a._actions				)		,						len(b._actions				)				)
								for x, y in zip(a._actions		,						b._actions				):
														_lowerCAmelCase										={k: v for k, v in vars(__A				).items() if k != 'container'}
														_lowerCAmelCase										={k: v for k, v in vars(__A				).items() if k != 'container'}

														# Choices with mixed type have custom function as "type"
														# So we need to compare results directly for equality
														if xx.get('choices'		,						__A				) and yy.get('choices'		,						__A				):
																				for expected_choice in yy["choices"] + xx["choices"]:
																										self.assertEqual(xx['type'](__A				)		,						yy['type'](__A				)				)
																				del xx["type"], yy["type"]

														self.assertEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo'		,						type=__A		,						required=__A				)
								expected.add_argument('--bar'		,						type=__A		,						required=__A				)
								expected.add_argument('--baz'		,						type=__A		,						required=__A				)
								expected.add_argument('--flag'		,						type=__A		,						default=__A		,						const=__A		,						nargs='?'				)
								self.argparsersEqual(__A		,						__A				)

								_lowerCAmelCase										=['--foo', '1', '--baz', 'quux', '--bar', '0.5']
								((_lowerCAmelCase  )						,				  )										=parser.parse_args_into_dataclasses(__A		,						look_for_args_file=__A				)
								self.assertFalse(example.flag				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo'		,						default=42		,						type=__A				)
								expected.add_argument('--baz'		,						default='toto'		,						type=__A		,						help='help message'				)
								self.argparsersEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo'		,						type=__A		,						default=__A		,						const=__A		,						nargs='?'				)
								expected.add_argument('--baz'		,						type=__A		,						default=__A		,						const=__A		,						nargs='?'				)
								# A boolean no_* argument always has to come after its "default: True" regular counter-part
								# and its default must be set to False
								expected.add_argument('--no_baz'		,						action='store_false'		,						default=__A		,						dest='baz'				)
								expected.add_argument('--opt'		,						type=__A		,						default=__A				)

								_lowerCAmelCase										=[WithDefaultBoolExample]
								if is_python_no_less_than_3_10:
														dataclass_types.append(__A				)

								for dataclass_type in dataclass_types:
														_lowerCAmelCase										=HfArgumentParser(__A				)
														self.argparsersEqual(__A		,						__A				)

														_lowerCAmelCase										=parser.parse_args([]				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						baz=__A		,						opt=__A				)				)

														_lowerCAmelCase										=parser.parse_args(['--foo', '--no_baz']				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						baz=__A		,						opt=__A				)				)

														_lowerCAmelCase										=parser.parse_args(['--foo', '--baz']				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						baz=__A		,						opt=__A				)				)

														_lowerCAmelCase										=parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True']				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						baz=__A		,						opt=__A				)				)

														_lowerCAmelCase										=parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False']				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						baz=__A		,						opt=__A				)				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument(
								    '--foo'		,						default='toto'		,						choices=['titi', 'toto', 42]		,						type=make_choice_type_function(['titi', 'toto', 42]				)		,						)
								self.argparsersEqual(__A		,						__A				)

								_lowerCAmelCase										=parser.parse_args([]				)
								self.assertEqual(args.foo		,						'toto'				)
								_lowerCAmelCase										=parser.parse_args_into_dataclasses([]				)[0]
								self.assertEqual(enum_ex.foo		,						MixedTypeEnum.toto				)

								_lowerCAmelCase										=parser.parse_args(['--foo', 'titi']				)
								self.assertEqual(args.foo		,						'titi'				)
								_lowerCAmelCase										=parser.parse_args_into_dataclasses(['--foo', 'titi']				)[0]
								self.assertEqual(enum_ex.foo		,						MixedTypeEnum.titi				)

								_lowerCAmelCase										=parser.parse_args(['--foo', '42']				)
								self.assertEqual(args.foo		,						42				)
								_lowerCAmelCase										=parser.parse_args_into_dataclasses(['--foo', '42']				)[0]
								self.assertEqual(enum_ex.foo		,						MixedTypeEnum.fourtytwo				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								@dataclass
								class   SCREAMING_SNAKE_CASE			:







										"""simple docstring"""
										lowercase   :      Literal["titi", "toto", 42]       =							"toto"

								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument(
								    '--foo'		,						default='toto'		,						choices=('titi', 'toto', 42)		,						type=make_choice_type_function(['titi', 'toto', 42]				)		,						)
								self.argparsersEqual(__A		,						__A				)

								_lowerCAmelCase										=parser.parse_args([]				)
								self.assertEqual(args.foo		,						'toto'				)

								_lowerCAmelCase										=parser.parse_args(['--foo', 'titi']				)
								self.assertEqual(args.foo		,						'titi'				)

								_lowerCAmelCase										=parser.parse_args(['--foo', '42']				)
								self.assertEqual(args.foo		,						42				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo_int'		,						nargs='+'		,						default=[]		,						type=__A				)
								expected.add_argument('--bar_int'		,						nargs='+'		,						default=[1, 2, 3]		,						type=__A				)
								expected.add_argument('--foo_str'		,						nargs='+'		,						default=['Hallo', 'Bonjour', 'Hello']		,						type=__A				)
								expected.add_argument('--foo_float'		,						nargs='+'		,						default=[0.1, 0.2, 0.3]		,						type=__A				)

								self.argparsersEqual(__A		,						__A				)

								_lowerCAmelCase										=parser.parse_args([]				)
								self.assertEqual(
								    __A		,						Namespace(foo_int=[]		,						bar_int=[1, 2, 3]		,						foo_str=['Hallo', 'Bonjour', 'Hello']		,						foo_float=[0.1, 0.2, 0.3]				)		,						)

								_lowerCAmelCase										=parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split()				)
								self.assertEqual(__A		,						Namespace(foo_int=[1]		,						bar_int=[2, 3]		,						foo_str=['a', 'b', 'c']		,						foo_float=[0.1, 0.7]				)				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo'		,						default=__A		,						type=__A				)
								expected.add_argument('--bar'		,						default=__A		,						type=__A		,						help='help message'				)
								expected.add_argument('--baz'		,						default=__A		,						type=__A				)
								expected.add_argument('--ces'		,						nargs='+'		,						default=[]		,						type=__A				)
								expected.add_argument('--des'		,						nargs='+'		,						default=[]		,						type=__A				)

								_lowerCAmelCase										=[OptionalExample]
								if is_python_no_less_than_3_10:
														dataclass_types.append(__A				)

								for dataclass_type in dataclass_types:
														_lowerCAmelCase										=HfArgumentParser(__A				)

														self.argparsersEqual(__A		,						__A				)

														_lowerCAmelCase										=parser.parse_args([]				)
														self.assertEqual(__A		,						Namespace(foo=__A		,						bar=__A		,						baz=__A		,						ces=[]		,						des=[]				)				)

														_lowerCAmelCase										=parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split()				)
														self.assertEqual(__A		,						Namespace(foo=12		,						bar=3.14		,						baz='42'		,						ces=['a', 'b', 'c']		,						des=[1, 2, 3]				)				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--required_list'		,						nargs='+'		,						type=__A		,						required=__A				)
								expected.add_argument('--required_str'		,						type=__A		,						required=__A				)
								expected.add_argument(
								    '--required_enum'		,						type=make_choice_type_function(['titi', 'toto']				)		,						choices=['titi', 'toto']		,						required=__A		,						)
								self.argparsersEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										=argparse.ArgumentParser()
								expected.add_argument('--foo'		,						type=__A		,						required=__A				)
								expected.add_argument(
								    '--required_enum'		,						type=make_choice_type_function(['titi', 'toto']				)		,						choices=['titi', 'toto']		,						required=__A		,						)
								expected.add_argument('--opt'		,						type=__A		,						default=__A				)
								expected.add_argument('--baz'		,						default='toto'		,						type=__A		,						help='help message'				)
								expected.add_argument('--foo_str'		,						nargs='+'		,						default=['Hallo', 'Bonjour', 'Hello']		,						type=__A				)
								self.argparsersEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										={
								    'foo': 12,
								    'bar': 3.14,
								    'baz': '42',
								    'flag': True,
								}

								_lowerCAmelCase										=parser.parse_dict(__A				)[0]
								_lowerCAmelCase										=BasicExample(**__A				)
								self.assertEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										={
								    'foo': 12,
								    'bar': 3.14,
								    'baz': '42',
								    'flag': True,
								    'extra': 42,
								}

								self.assertRaises(__A		,						parser.parse_dict		,						__A		,						allow_extra_keys=__A				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										={
								    'foo': 12,
								    'bar': 3.14,
								    'baz': '42',
								    'flag': True,
								}
								with tempfile.TemporaryDirectory() as tmp_dir:
														_lowerCAmelCase										=os.path.join(__A		,						'temp_json'				)
														os.mkdir(__A				)
														with open(temp_local_path + '.json'		,						'w+'				) as f:
																				json.dump(__A		,						__A				)
														_lowerCAmelCase										=parser.parse_yaml_file(Path(temp_local_path + '.json'				)				)[0]

								_lowerCAmelCase										=BasicExample(**__A				)
								self.assertEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=HfArgumentParser(__A				)

								_lowerCAmelCase										={
								    'foo': 12,
								    'bar': 3.14,
								    'baz': '42',
								    'flag': True,
								}
								with tempfile.TemporaryDirectory() as tmp_dir:
														_lowerCAmelCase										=os.path.join(__A		,						'temp_yaml'				)
														os.mkdir(__A				)
														with open(temp_local_path + '.yaml'		,						'w+'				) as f:
																				yaml.dump(__A		,						__A				)
														_lowerCAmelCase										=parser.parse_yaml_file(Path(temp_local_path + '.yaml'				)				)[0]
								_lowerCAmelCase										=BasicExample(**__A				)
								self.assertEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=HfArgumentParser(__A				)
								self.assertIsNotNone(__A				)
'''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


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






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

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






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

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






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

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

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

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

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

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

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=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 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								BaseTransformer.add_model_specific_args(__A		,						__A				)
								parser.add_argument(
								    '--max_seq_length'		,						default=128		,						type=__A		,						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=__A		,						required=__A		,						help='The GLUE task to run'		,						)
								parser.add_argument(
								    '--gpus'		,						default=0		,						type=__A		,						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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

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

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

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


if __name__ == "__main__":
							main()





