






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase__  :					List[Any]  						=    logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase__  :					List[str]  						=    {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase__  :					Union[str, Any]  						=    {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase__  :					Any  						=    sorted(arg_to_scheduler.keys())
UpperCamelCase__  :					Union[str, Any]  						=    "{" + ", ".join(arg_to_scheduler_choices) + "}"







class  lowerCamelCase_   (		pl.LightningModule   ):
							def __init__(  self						:    int							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    Dict=None							,__lowerCamelCase						:    Optional[int]="base"							,__lowerCamelCase						:    Dict=None							,__lowerCamelCase						:    Optional[int]=None							,__lowerCamelCase						:    Optional[int]=None							,**__lowerCamelCase						:    Optional[int]							,):





								'''simple docstring'''



								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(__lowerCamelCase    )
								a				=	0
								a				=	Path(self.hparams.output_dir    )
								a				=	self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
									a				=	AutoConfig.from_pretrained(
									    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,**({'''num_labels''': num_labels} if num_labels is not None else {})							,cache_dir=__lowerCamelCase							,**__lowerCamelCase							,)
								else:
									a				=	config

								a				=	('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
								for p in extra_model_params:
									if getattr(self.hparams							,__lowerCamelCase							,__lowerCamelCase    ):
										assert hasattr(self.config							,__lowerCamelCase    ), F"""model config doesn\'t have a `{p}` attribute"""
										setattr(self.config							,__lowerCamelCase							,getattr(self.hparams							,__lowerCamelCase    )    )

								if tokenizer is None:
									a				=	AutoTokenizer.from_pretrained(
									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,cache_dir=__lowerCamelCase							,)
								else:
									a				=	tokenizer
								a				=	MODEL_MODES[mode]
								if model is None:
									a				=	self.model_type.from_pretrained(
									    self.hparams.model_name_or_path							,from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path    )							,config=self.config							,cache_dir=__lowerCamelCase							,)
								else:
									a				=	model
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]							,*__lowerCamelCase						:    Dict							,**__lowerCamelCase						:    Tuple    ):





								'''simple docstring'''



								a				=	self.model_type.from_pretrained(*__lowerCamelCase							,**__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]    ):





								'''simple docstring'''



								a				=	arg_to_scheduler[self.hparams.lr_scheduler]
								a				=	get_schedule_func(
								    self.opt							,num_warmup_steps=self.hparams.warmup_steps							,num_training_steps=self.total_steps()    )
								a				=	{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
								return scheduler
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[str]    ):





								'''simple docstring'''



								a				=	self.model
								a				=	['''bias''', '''LayerNorm.weight''']
								a				=	[
								    {
								        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
								        '''weight_decay''': self.hparams.weight_decay,
								    },
								    {
								        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
								        '''weight_decay''': 0.0,
								    },
								]
								if self.hparams.adafactor:
									a				=	Adafactor(
									    __lowerCamelCase							,lr=self.hparams.learning_rate							,scale_parameter=__lowerCamelCase							,relative_step=__lowerCamelCase    )

								else:
									a				=	AdamW(
									    __lowerCamelCase							,lr=self.hparams.learning_rate							,eps=self.hparams.adam_epsilon    )
								a				=	optimizer

								a				=	self.get_lr_scheduler()

								return [optimizer], [scheduler]
							def        SCREAMING_SNAKE_CASE_       (  self						:    int							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    str    ):





								'''simple docstring'''



								return self.validation_step(__lowerCamelCase							,__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[str]							,__lowerCamelCase						:    Union[str, Any]    ):





								'''simple docstring'''



								return self.validation_end(__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]    ):





								'''simple docstring'''



								a				=	max(1							,self.hparams.gpus    )  # TODO: consider num_tpu_cores
								a				=	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]							,__lowerCamelCase						:    str    ):





								'''simple docstring'''



								if mode == "test":
									a				=	len(self.test_dataloader().dataset    )
								else:
									a				=	self.get_dataloader('''train'''							,self.hparams.train_batch_size							,shuffle=__lowerCamelCase    )
									a				=	len(self.train_dataloader().dataset    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    Optional[int]							,__lowerCamelCase						:    str = False    ):





								'''simple docstring'''



								raise NotImplementedError('''You must implement this for your task'''    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    int    ):





								'''simple docstring'''



								return self.train_loader
							def        SCREAMING_SNAKE_CASE_       (  self						:    Union[str, Any]    ):





								'''simple docstring'''



								return self.get_dataloader('''dev'''							,self.hparams.eval_batch_size							,shuffle=__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    str    ):





								'''simple docstring'''



								return self.get_dataloader('''test'''							,self.hparams.eval_batch_size							,shuffle=__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]							,__lowerCamelCase						:    Optional[Any]    ):





								'''simple docstring'''



								return os.path.join(
								    self.hparams.data_dir							,'''cached_{}_{}_{}'''.format(
								        __lowerCamelCase							,list(filter(__lowerCamelCase							,self.hparams.model_name_or_path.split('''/'''    )    )    ).pop()							,str(self.hparams.max_seq_length    )							,)							,)
							@pl.utilities.rank_zero_only
							def        SCREAMING_SNAKE_CASE_       (  self						:    Any							,__lowerCamelCase						:    List[Any]    ):





								'''simple docstring'''



								a				=	self.output_dir.joinpath('''best_tfmr'''    )
								a				=	self.step_count
								self.model.save_pretrained(__lowerCamelCase    )
								self.tokenizer.save_pretrained(__lowerCamelCase    )




							@staticmethod
							def        SCREAMING_SNAKE_CASE_       (  __lowerCamelCase						:    List[Any]							,__lowerCamelCase						:    int    ):





								'''simple docstring'''



								parser.add_argument(
								    '''--model_name_or_path'''							,default=__lowerCamelCase							,type=__lowerCamelCase							,required=__lowerCamelCase							,help='''Path to pretrained model or model identifier from huggingface.co/models'''							,)
								parser.add_argument(
								    '''--config_name'''							,default=''''''							,type=__lowerCamelCase							,help='''Pretrained config name or path if not the same as model_name'''    )
								parser.add_argument(
								    '''--tokenizer_name'''							,default=__lowerCamelCase							,type=__lowerCamelCase							,help='''Pretrained tokenizer name or path if not the same as model_name'''							,)
								parser.add_argument(
								    '''--cache_dir'''							,default=''''''							,type=__lowerCamelCase							,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''							,)
								parser.add_argument(
								    '''--encoder_layerdrop'''							,type=__lowerCamelCase							,help='''Encoder layer dropout probability (Optional). Goes into model.config'''							,)
								parser.add_argument(
								    '''--decoder_layerdrop'''							,type=__lowerCamelCase							,help='''Decoder layer dropout probability (Optional). Goes into model.config'''							,)
								parser.add_argument(
								    '''--dropout'''							,type=__lowerCamelCase							,help='''Dropout probability (Optional). Goes into model.config'''							,)
								parser.add_argument(
								    '''--attention_dropout'''							,type=__lowerCamelCase							,help='''Attention dropout probability (Optional). Goes into model.config'''							,)
								parser.add_argument('''--learning_rate'''							,default=5e-5							,type=__lowerCamelCase							,help='''The initial learning rate for Adam.'''    )
								parser.add_argument(
								    '''--lr_scheduler'''							,default='''linear'''							,choices=__lowerCamelCase							,metavar=__lowerCamelCase							,type=__lowerCamelCase							,help='''Learning rate scheduler'''							,)
								parser.add_argument('''--weight_decay'''							,default=0.0							,type=__lowerCamelCase							,help='''Weight decay if we apply some.'''    )
								parser.add_argument('''--adam_epsilon'''							,default=1e-8							,type=__lowerCamelCase							,help='''Epsilon for Adam optimizer.'''    )
								parser.add_argument('''--warmup_steps'''							,default=0							,type=__lowerCamelCase							,help='''Linear warmup over warmup_steps.'''    )
								parser.add_argument('''--num_workers'''							,default=4							,type=__lowerCamelCase							,help='''kwarg passed to DataLoader'''    )
								parser.add_argument('''--num_train_epochs'''							,dest='''max_epochs'''							,default=3							,type=__lowerCamelCase    )
								parser.add_argument('''--train_batch_size'''							,default=32							,type=__lowerCamelCase    )
								parser.add_argument('''--eval_batch_size'''							,default=32							,type=__lowerCamelCase    )
								parser.add_argument('''--adafactor'''							,action='''store_true'''    )







class  lowerCamelCase_   (		pl.Callback   ):
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]							,__lowerCamelCase						:    str							,__lowerCamelCase						:    Tuple    ):





								'''simple docstring'''



								a				=	trainer.lr_schedulers[0]['''scheduler''']
								a				=	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
								pl_module.logger.log_metrics(__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]							,__lowerCamelCase						:    Optional[Any]							,__lowerCamelCase						:    List[str]    ):





								'''simple docstring'''



								rank_zero_info('''***** Validation results *****'''    )
								a				=	trainer.callback_metrics
								# Log results
								for key in sorted(__lowerCamelCase    ):
									if key not in ["log", "progress_bar"]:
										rank_zero_info('''{} = {}\n'''.format(__lowerCamelCase							,str(metrics[key]    )    )    )




							def        SCREAMING_SNAKE_CASE_       (  self						:    str							,__lowerCamelCase						:    List[Any]							,__lowerCamelCase						:    str    ):





								'''simple docstring'''



								rank_zero_info('''***** Test results *****'''    )
								a				=	trainer.callback_metrics
								# Log and save results to file
								a				=	os.path.join(pl_module.hparams.output_dir							,'''test_results.txt'''    )
								with open(__lowerCamelCase							,'''w'''    ) as writer:
									for key in sorted(__lowerCamelCase    ):
										if key not in ["log", "progress_bar"]:
											rank_zero_info('''{} = {}\n'''.format(__lowerCamelCase							,str(metrics[key]    )    )    )
											writer.write('''{} = {}\n'''.format(__lowerCamelCase							,str(metrics[key]    )    )    )





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_     )      ->     None:


	"""simple docstring"""



	parser.add_argument(
	    '''--output_dir''',     default=__UpperCamelCase,     type=__UpperCamelCase,     required=__UpperCamelCase,     help='''The output directory where the model predictions and checkpoints will be written.''',     )
	parser.add_argument(
	    '''--fp16''',     action='''store_true''',     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',     )

	parser.add_argument(
	    '''--fp16_opt_level''',     type=__UpperCamelCase,     default='''O2''',     help=(
	        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
	        '''See details at https://nvidia.github.io/apex/amp.html'''
	    ),     )
	parser.add_argument('''--n_tpu_cores''',     dest='''tpu_cores''',     type=__UpperCamelCase     )
	parser.add_argument('''--max_grad_norm''',     dest='''gradient_clip_val''',     default=1.0,     type=__UpperCamelCase,     help='''Max gradient norm'''     )
	parser.add_argument('''--do_train''',     action='''store_true''',     help='''Whether to run training.'''     )
	parser.add_argument('''--do_predict''',     action='''store_true''',     help='''Whether to run predictions on the test set.'''     )
	parser.add_argument(
	    '''--gradient_accumulation_steps''',     dest='''accumulate_grad_batches''',     type=__UpperCamelCase,     default=1,     help='''Number of updates steps to accumulate before performing a backward/update pass.''',     )
	parser.add_argument('''--seed''',     type=__UpperCamelCase,     default=4_2,     help='''random seed for initialization'''     )
	parser.add_argument(
	    '''--data_dir''',     default=__UpperCamelCase,     type=__UpperCamelCase,     required=__UpperCamelCase,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''',     )





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_=None,     snake_case_=True,     snake_case_=[],     snake_case_=None,     snake_case_=None,     **snake_case_,     )      ->     Optional[Any]:


	"""simple docstring"""



	pl.seed_everything(args.seed     )

	# init model
	a				=	Path(model.hparams.output_dir     )
	odir.mkdir(exist_ok=__UpperCamelCase     )

	# add custom checkpoints
	if checkpoint_callback is None:
		a				=	pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir,     prefix='''checkpoint''',     monitor='''val_loss''',     mode='''min''',     save_top_k=1     )
	if early_stopping_callback:
		extra_callbacks.append(__UpperCamelCase     )
	if logging_callback is None:
		a				=	LoggingCallback()

	a				=	{}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		a				=	1_6
		a				=	args.fpaa_opt_level

	if args.gpus > 1:
		a				=	'''ddp'''

	a				=	args.accumulate_grad_batches
	a				=	extra_train_kwargs.get('''accelerator''',     __UpperCamelCase     )
	a				=	extra_train_kwargs.get('''profiler''',     __UpperCamelCase     )

	a				=	pl.Trainer.from_argparse_args(
	    __UpperCamelCase,     weights_summary=__UpperCamelCase,     callbacks=[logging_callback] + extra_callbacks,     logger=__UpperCamelCase,     checkpoint_callback=__UpperCamelCase,     **__UpperCamelCase,     )

	if args.do_train:
		trainer.fit(__UpperCamelCase     )

	return trainer











import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







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



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

