





'''simple docstring'''

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


lowercase_         =			logging.getLogger(__name__)

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

lowercase_         =			{
    """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
lowercase_         =			{
    """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
}
lowercase_         =			sorted(arg_to_scheduler.keys())
lowercase_         =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class  a_					(    pl.LightningModule	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A=None			,		A="base"			,		A=None			,		A=None			,		A=None			,		**A			,		)		->       Tuple:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(_UpperCAmelCase	)
        _SCREAMING_SNAKE_CASE     =      0
        _SCREAMING_SNAKE_CASE     =      Path(self.hparams.output_dir	)
        _SCREAMING_SNAKE_CASE     =      self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
            _SCREAMING_SNAKE_CASE     =      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=_UpperCAmelCase			,		**_UpperCAmelCase			,		)
        else:
            _SCREAMING_SNAKE_CASE     =      config

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

        if tokenizer is None:
            _SCREAMING_SNAKE_CASE     =      AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,		cache_dir=_UpperCAmelCase			,		)
        else:
            _SCREAMING_SNAKE_CASE     =      tokenizer
        _SCREAMING_SNAKE_CASE     =      MODEL_MODES[mode]
        if model is None:
            _SCREAMING_SNAKE_CASE     =      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=_UpperCAmelCase			,		)
        else:
            _SCREAMING_SNAKE_CASE     =      model


    def       snake_case_(		self			,		*A			,		**A	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      self.model_type.from_pretrained(*_UpperCAmelCase			,		**_UpperCAmelCase	)


    def       snake_case_(		self	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      arg_to_scheduler[self.hparams.lr_scheduler]
        _SCREAMING_SNAKE_CASE     =      get_schedule_func(
            self.opt			,		num_warmup_steps=self.hparams.warmup_steps			,		num_training_steps=self.total_steps()	)
        _SCREAMING_SNAKE_CASE     =      {"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler


    def       snake_case_(		self	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      self.model
        _SCREAMING_SNAKE_CASE     =      ["bias", "LayerNorm.weight"]
        _SCREAMING_SNAKE_CASE     =      [
            {
                "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:
            _SCREAMING_SNAKE_CASE     =      Adafactor(
                _UpperCAmelCase			,		lr=self.hparams.learning_rate			,		scale_parameter=_UpperCAmelCase			,		relative_step=_UpperCAmelCase	)

        else:
            _SCREAMING_SNAKE_CASE     =      AdamW(
                _UpperCAmelCase			,		lr=self.hparams.learning_rate			,		eps=self.hparams.adam_epsilon	)
        _SCREAMING_SNAKE_CASE     =      optimizer

        _SCREAMING_SNAKE_CASE     =      self.get_lr_scheduler()

        return [optimizer], [scheduler]


    def       snake_case_(		self			,		A			,		A	)		->       Optional[Any]:
        return self.validation_step(_UpperCAmelCase			,		_UpperCAmelCase	)


    def       snake_case_(		self			,		A	)		->       Optional[int]:
        return self.validation_end(_UpperCAmelCase	)


    def       snake_case_(		self	)		->       int:
        _SCREAMING_SNAKE_CASE     =      max(1			,		self.hparams.gpus	)  # TODO: consider num_tpu_cores
        _SCREAMING_SNAKE_CASE     =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


    def       snake_case_(		self			,		A	)		->       int:
        if mode == "test":
            _SCREAMING_SNAKE_CASE     =      len(self.test_dataloader().dataset	)
        else:
            _SCREAMING_SNAKE_CASE     =      self.get_dataloader("""train"""			,		self.hparams.train_batch_size			,		shuffle=_UpperCAmelCase	)
            _SCREAMING_SNAKE_CASE     =      len(self.train_dataloader().dataset	)


    def       snake_case_(		self			,		A			,		A			,		A = False	)		->       List[Any]:
        raise NotImplementedError("""You must implement this for your task"""	)


    def       snake_case_(		self	)		->       Any:
        return self.train_loader


    def       snake_case_(		self	)		->       Any:
        return self.get_dataloader("""dev"""			,		self.hparams.eval_batch_size			,		shuffle=_UpperCAmelCase	)


    def       snake_case_(		self	)		->       Union[str, Any]:
        return self.get_dataloader("""test"""			,		self.hparams.eval_batch_size			,		shuffle=_UpperCAmelCase	)


    def       snake_case_(		self			,		A	)		->       Optional[int]:
        return os.path.join(
            self.hparams.data_dir			,		"""cached_{}_{}_{}""".format(
                _UpperCAmelCase			,		list(filter(_UpperCAmelCase			,		self.hparams.model_name_or_path.split("""/"""	)	)	).pop()			,		str(self.hparams.max_seq_length	)			,		)			,		)


    @pl.utilities.rank_zero_only
    def       snake_case_(		self			,		A	)		->       None:
        _SCREAMING_SNAKE_CASE     =      self.output_dir.joinpath("""best_tfmr"""	)
        _SCREAMING_SNAKE_CASE     =      self.step_count
        self.model.save_pretrained(_UpperCAmelCase	)
        self.tokenizer.save_pretrained(_UpperCAmelCase	)





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




class  a_					(    pl.Callback	):

    '''simple docstring'''


    def       snake_case_(		self			,		A			,		A	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      trainer.lr_schedulers[0]["scheduler"]
        _SCREAMING_SNAKE_CASE     =      {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
        pl_module.logger.log_metrics(_UpperCAmelCase	)


    def       snake_case_(		self			,		A			,		A	)		->       Dict:
        rank_zero_info("""***** Validation results *****"""	)
        _SCREAMING_SNAKE_CASE     =      trainer.callback_metrics
        # Log results
        for key in sorted(_UpperCAmelCase	):
            if key not in ["log", "progress_bar"]:
                rank_zero_info("""{} = {}\n""".format(_UpperCAmelCase			,		str(metrics[key]	)	)	)





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





def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase   )						->int:
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        """--output_dir""" ,	default=snake_case__ ,	type=snake_case__ ,	required=snake_case__ ,	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=snake_case__ ,	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=snake_case__   )
    parser.add_argument("""--max_grad_norm""" ,	dest="""gradient_clip_val""" ,	default=1.0 ,	type=snake_case__ ,	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=snake_case__ ,	default=1 ,	help="""Number of updates steps to accumulate before performing a backward/update pass.""" ,	)
    parser.add_argument("""--seed""" ,	type=snake_case__ ,	default=42 ,	help="""random seed for initialization"""   )
    parser.add_argument(
        """--data_dir""" ,	default=snake_case__ ,	type=snake_case__ ,	required=snake_case__ ,	help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" ,	)






def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase=None ,	__lowerCamelCase=True ,	__lowerCamelCase=[] ,	__lowerCamelCase=None ,	__lowerCamelCase=None ,	**__lowerCamelCase ,	)						->Any:
    pl.seed_everything(args.seed   )

    # init model
    _SCREAMING_SNAKE_CASE     =      Path(model.hparams.output_dir   )
    odir.mkdir(exist_ok=snake_case__   )

    # add custom checkpoints
    if checkpoint_callback is None:
        _SCREAMING_SNAKE_CASE     =      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(snake_case__   )
    if logging_callback is None:
        _SCREAMING_SNAKE_CASE     =      LoggingCallback()

    _SCREAMING_SNAKE_CASE     =      {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        _SCREAMING_SNAKE_CASE     =      16
        _SCREAMING_SNAKE_CASE     =      args.fpaa_opt_level

    if args.gpus > 1:
        _SCREAMING_SNAKE_CASE     =      "ddp"

    _SCREAMING_SNAKE_CASE     =      args.accumulate_grad_batches
    _SCREAMING_SNAKE_CASE     =      extra_train_kwargs.get("""accelerator""" ,	snake_case__   )
    _SCREAMING_SNAKE_CASE     =      extra_train_kwargs.get("""profiler""" ,	snake_case__   )

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

    if args.do_train:
        trainer.fit(snake_case__   )

    return trainer










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



