

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


__A   :     Union[str, Any]      =			logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__A   :     Optional[Any]      =			{
    '''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
__A   :     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
}
__A   :     Any      =			sorted(arg_to_scheduler.keys())
__A   :     List[str]      =			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class    __A				(							pl.LightningModule ):






    def __init__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    argparse.Namespace    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    Tuple="base"    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    List[Any]=None    ,				UpperCAmelCase_			:    List[str]=None    ,				**UpperCAmelCase_			:    Optional[Any]    ,				):
        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_    )
        lowerCAmelCase				:			List[str]    			=      0
        lowerCAmelCase				:			Any    			=      Path(self.hparams.output_dir    )
        lowerCAmelCase				:			Optional[int]    			=      self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
            lowerCAmelCase				:			List[str]    			=      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:
            lowerCAmelCase				:			PretrainedConfig    			=      config

        lowerCAmelCase				:			int    			=      ('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:
            lowerCAmelCase				:			List[str]    			=      AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,				cache_dir=UpperCAmelCase_    ,				)
        else:
            lowerCAmelCase				:			PreTrainedTokenizer    			=      tokenizer
        lowerCAmelCase				:			Any    			=      MODEL_MODES[mode]
        if model is None:
            lowerCAmelCase				:			Optional[Any]    			=      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:
            lowerCAmelCase				:			Union[str, Any]    			=      model






    def 	lowercase__ (	self			:    Tuple    ,				*UpperCAmelCase_			:    Optional[Any]    ,				**UpperCAmelCase_			:    Any    ):
        lowerCAmelCase				:			Dict    			=      self.model_type.from_pretrained(*UpperCAmelCase_    ,				**UpperCAmelCase_    )






    def 	lowercase__ (	self			:    str    ):
        lowerCAmelCase				:			Dict    			=      arg_to_scheduler[self.hparams.lr_scheduler]
        lowerCAmelCase				:			Union[str, Any]    			=      get_schedule_func(
            self.opt    ,				num_warmup_steps=self.hparams.warmup_steps    ,				num_training_steps=self.total_steps()    )
        lowerCAmelCase				:			Tuple    			=      {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
        return scheduler






    def 	lowercase__ (	self			:    Union[str, Any]    ):
        lowerCAmelCase				:			Optional[Any]    			=      self.model
        lowerCAmelCase				:			Any    			=      ['bias', 'LayerNorm.weight']
        lowerCAmelCase				:			int    			=      [
            {
                '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:
            lowerCAmelCase				:			List[Any]    			=      Adafactor(
                UpperCAmelCase_    ,				lr=self.hparams.learning_rate    ,				scale_parameter=UpperCAmelCase_    ,				relative_step=UpperCAmelCase_    )

        else:
            lowerCAmelCase				:			str    			=      AdamW(
                UpperCAmelCase_    ,				lr=self.hparams.learning_rate    ,				eps=self.hparams.adam_epsilon    )
        lowerCAmelCase				:			Tuple    			=      optimizer

        lowerCAmelCase				:			Union[str, Any]    			=      self.get_lr_scheduler()

        return [optimizer], [scheduler]






    def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    str    ):
        return self.validation_step(UpperCAmelCase_    ,				UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Any    ,				UpperCAmelCase_			:    Optional[int]    ):
        return self.validation_end(UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[Any]    ):
        lowerCAmelCase				:			Union[str, Any]    			=      max(1    ,				self.hparams.gpus    )  # TODO: consider num_tpu_cores
        lowerCAmelCase				:			str    			=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ):
        if mode == "test":
            lowerCAmelCase				:			Optional[int]    			=      len(self.test_dataloader().dataset    )
        else:
            lowerCAmelCase				:			int    			=      self.get_dataloader('train'    ,				self.hparams.train_batch_size    ,				shuffle=UpperCAmelCase_    )
            lowerCAmelCase				:			int    			=      len(self.train_dataloader().dataset    )






    def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    bool = False    ):
        raise NotImplementedError('You must implement this for your task'    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        return self.train_loader






    def 	lowercase__ (	self			:    int    ):
        return self.get_dataloader('dev'    ,				self.hparams.eval_batch_size    ,				shuffle=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[Any]    ):
        return self.get_dataloader('test'    ,				self.hparams.eval_batch_size    ,				shuffle=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Dict    ):
        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 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Dict[str, Any]    ):
        lowerCAmelCase				:			List[Any]    			=      self.output_dir.joinpath('best_tfmr'    )
        lowerCAmelCase				:			Dict    			=      self.step_count
        self.model.save_pretrained(UpperCAmelCase_    )
        self.tokenizer.save_pretrained(UpperCAmelCase_    )






    @staticmethod
    def 	lowercase__ (	UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Any    ):
        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 ):






    def 	lowercase__ (	self			:    List[Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[Any]    ):
        lowerCAmelCase				:			Any    			=      trainer.lr_schedulers[0]['scheduler']
        lowerCAmelCase				:			Optional[int]    			=      {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
        pl_module.logger.log_metrics(UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    pl.Trainer    ,				UpperCAmelCase_			:    pl.LightningModule    ):
        rank_zero_info('***** Validation results *****'    )
        lowerCAmelCase				:			Optional[int]    			=      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 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    pl.Trainer    ,				UpperCAmelCase_			:    pl.LightningModule    ):
        rank_zero_info('***** Test results *****'    )
        lowerCAmelCase				:			Optional[int]    			=      trainer.callback_metrics
        # Log and save results to file
        lowerCAmelCase				:			List[str]    			=      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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> None:
    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=42,      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__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=None,      _UpperCAmelCase=True,      _UpperCAmelCase=[],      _UpperCAmelCase=None,      _UpperCAmelCase=None,      **_UpperCAmelCase,      )					-> Tuple:
    pl.seed_everything(args.seed				)

    # init model
    lowerCAmelCase				:			List[Any]    			=      Path(model.hparams.output_dir				)
    odir.mkdir(exist_ok=_UpperCAmelCase				)

    # add custom checkpoints
    if checkpoint_callback is None:
        lowerCAmelCase				:			Optional[Any]    			=      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:
        lowerCAmelCase				:			Any    			=      LoggingCallback()

    lowerCAmelCase				:			Tuple    			=      {}

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

    if args.gpus > 1:
        lowerCAmelCase				:			Tuple    			=      'ddp'

    lowerCAmelCase				:			Optional[int]    			=      args.accumulate_grad_batches
    lowerCAmelCase				:			str    			=      extra_train_kwargs.get('accelerator',      _UpperCAmelCase				)
    lowerCAmelCase				:			str    			=      extra_train_kwargs.get('profiler',      _UpperCAmelCase				)

    lowerCAmelCase				:			List[Any]    			=      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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Tuple      =			[
     '''UMT5EncoderModel''',
     '''UMT5ForConditionalGeneration''',
     '''UMT5ForQuestionAnswering''',
     '''UMT5Model''',
     '''UMT5PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_umta import UMTaConfig, UMTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_umta import (
      UMTaEncoderModel,
      UMTaForConditionalGeneration,
      UMTaForQuestionAnswering,
      UMTaModel,
      UMTaPreTrainedModel,
  )
else:
 import sys

 __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





