




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

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

UpperCamelCase__ :   Tuple						=							{
    '''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__ :   Dict						=							{
    '''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__ :   List[Any]						=							sorted(arg_to_scheduler.keys())
UpperCamelCase__ :   str						=							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 				lowerCAmelCase_       (					pl.LightningModule							):






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

							self.save_hyperparameters(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      0
							SCREAMING_SNAKE_CASE_   : int					  =      Path(self.hparams.output_dir )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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=snake_case__      ,**snake_case__      ,)
							else:
										SCREAMING_SNAKE_CASE_   : PretrainedConfig					  =      config

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
										if getattr(self.hparams      ,snake_case__      ,snake_case__ ):
													assert hasattr(self.config      ,snake_case__ ), F'model config doesn\'t have a `{p}` attribute'
													setattr(self.config      ,snake_case__      ,getattr(self.hparams      ,snake_case__ ) )

							if tokenizer is None:
										SCREAMING_SNAKE_CASE_   : str					  =      AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,cache_dir=snake_case__      ,)
							else:
										SCREAMING_SNAKE_CASE_   : PreTrainedTokenizer					  =      tokenizer
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      MODEL_MODES[mode]
							if model is None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      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=snake_case__      ,)
							else:
										SCREAMING_SNAKE_CASE_   : Dict					  =      model






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_type.from_pretrained(*snake_case__      ,**snake_case__ )






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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model
							SCREAMING_SNAKE_CASE_   : Any					  =      ['bias', 'LayerNorm.weight']
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [
							    {
							        'params': [
							            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
							        ],  # check this named paramters
							        '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_   : List[Any]					  =      Adafactor(
										    snake_case__      ,lr=self.hparams.learning_rate      ,scale_parameter=snake_case__      ,relative_step=snake_case__ )

							else:
										SCREAMING_SNAKE_CASE_   : str					  =      AdamW(
										    snake_case__      ,lr=self.hparams.learning_rate      ,eps=self.hparams.adam_epsilon )
							SCREAMING_SNAKE_CASE_   : str					  =      optimizer

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.get_lr_scheduler()

							return [optimizer], [scheduler]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							return self.validation_step(snake_case__      ,snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							return self.validation_end(snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      max(1      ,self.hparams.gpus )  # TODO: consider num_tpu_cores
							SCREAMING_SNAKE_CASE_   : Dict					  =      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      ,snake_case__ ):
							if stage == "test":
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      len(self.test_dataloader().dataset )
							else:
										SCREAMING_SNAKE_CASE_   : Any					  =      self.get_dataloader('train'      ,self.hparams.train_batch_size      ,shuffle=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      len(self.train_dataloader().dataset )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = False ):
							raise NotImplementedError('You must implement this for your task' )






				def 		snake_case	(			self ):
							return self.train_loader






				def 		snake_case	(			self ):
							return self.get_dataloader('dev'      ,self.hparams.eval_batch_size      ,shuffle=snake_case__ )






				def 		snake_case	(			self ):
							return self.get_dataloader('test'      ,self.hparams.eval_batch_size      ,shuffle=snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							return os.path.join(
							    self.hparams.data_dir      ,'cached_{}_{}_{}'.format(
							        snake_case__      ,list(filter(snake_case__      ,self.hparams.model_name_or_path.split('/' ) ) ).pop()      ,str(self.hparams.max_seq_length )      ,)      ,)






				@pl.utilities.rank_zero_only
				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.output_dir.joinpath('best_tfmr' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.step_count
							self.model.save_pretrained(snake_case__ )
							self.tokenizer.save_pretrained(snake_case__ )






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







class 				lowerCAmelCase_       (					pl.Callback							):






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							if (
							    trainer.is_global_zero and trainer.global_rank == 0
							):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
										pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class 				lowerCAmelCase_       (					pl.Callback							):






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							# print(pl_module.model.rag)
							for name, param in pl_module.model.rag.named_parameters():
										if param.grad is None:
													print(snake_case__ )







class 				lowerCAmelCase_       (					pl.Callback							):






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      trainer.lr_schedulers[0]['scheduler']
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
							pl_module.logger.log_metrics(snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							rank_zero_info('***** Validation results *****' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      trainer.callback_metrics
							# Log results
							for key in sorted(snake_case__ ):
										if key not in ["log", "progress_bar"]:
													rank_zero_info('{} = {}\n'.format(snake_case__      ,str(metrics[key] ) ) )






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





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     List[str]  )       ->  None:







			"""simple docstring"""





			parser.add_argument(
			    '--output_dir'  ,							default=str(Path(lowerCamelCase_  ).parent / 'test_run' / 'model_checkpoints'  )  ,							type=lowerCamelCase_  ,							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=lowerCamelCase_  ,							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=lowerCamelCase_  )
			parser.add_argument('--max_grad_norm'  ,							dest='gradient_clip_val'  ,							default=1.0  ,							type=lowerCamelCase_  ,							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=lowerCamelCase_  ,							default=1  ,							help='Number of updates steps to accumulate before performing a backward/update pass.'  ,							)
			parser.add_argument('--seed'  ,							type=lowerCamelCase_  ,							default=42  ,							help='random seed for initialization'  )
			parser.add_argument(
			    '--data_dir'  ,							default=str(Path(lowerCamelCase_  ).parent / 'test_run' / 'dummy-train-data'  )  ,							type=lowerCamelCase_  ,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'  ,							)





def        __UpperCAmelCase						(							lowerCamelCase_ :     BaseTransformer  ,							lowerCamelCase_ :     argparse.Namespace  ,							lowerCamelCase_ :     Optional[Any]=None  ,							lowerCamelCase_ :     str=True  ,							lowerCamelCase_ :     Dict=[]  ,							lowerCamelCase_ :     Any=None  ,							lowerCamelCase_ :     Optional[Any]=None  ,							**lowerCamelCase_ :     List[Any]  ,							)       ->  List[str]:







			"""simple docstring"""





			pl.seed_everything(args.seed  )

			# init model
			SCREAMING_SNAKE_CASE_   : List[str]					  =      Path(model.hparams.output_dir  )
			odir.mkdir(exist_ok=lowerCamelCase_  )

			# add custom checkpoints
			if checkpoint_callback is None:
						SCREAMING_SNAKE_CASE_   : Dict					  =      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(lowerCamelCase_  )
			if logging_callback is None:
						SCREAMING_SNAKE_CASE_   : str					  =      LoggingCallback()

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {}

			if args.fpaa:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      16

			if args.gpus > 1:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'auto'
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'ddp'

			SCREAMING_SNAKE_CASE_   : Dict					  =      args.accumulate_grad_batches
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      None
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'auto'

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      pl.Trainer.from_argparse_args(
			    lowerCamelCase_  ,							weights_summary=lowerCamelCase_  ,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]  ,							logger=lowerCamelCase_  ,							val_check_interval=1  ,							num_sanity_val_steps=2  ,							**lowerCamelCase_  ,							)

			if args.do_train:
						trainer.fit(lowerCamelCase_  )

			else:
						print('RAG modeling tests with new set functions successfuly executed!'  )
			return trainer





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



