





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    : int    	=  logging.getLogger(__name__)

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

lowercase    : 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
lowercase    : Optional[int]    	=  {
    """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    : Optional[Any]    	=  sorted(arg_to_scheduler.keys())
lowercase    : int    	=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class  __A(						pl.LightningModule ):

     def __init__(      self,       A,       A=None,       A="base",       A=None,       A=None,       A=None,       **A,       ):





            """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(A      )
            _UpperCamelCase              =						0
            _UpperCamelCase              =						Path(self.hparams.output_dir      )
            _UpperCamelCase              =						self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _UpperCamelCase              =						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=A,       **A,       )
            else:
                   _UpperCamelCase              =						config

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

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

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_type.from_pretrained(*A,       **A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model
            _UpperCamelCase              =						['''bias''', '''LayerNorm.weight''']
            _UpperCamelCase              =						[
                {
                    '''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:
                   _UpperCamelCase              =						Adafactor(
                       A,       lr=self.hparams.learning_rate,       scale_parameter=A,       relative_step=A      )

            else:
                   _UpperCamelCase              =						AdamW(
                       A,       lr=self.hparams.learning_rate,       eps=self.hparams.adam_epsilon      )
            _UpperCamelCase              =						optimizer

            _UpperCamelCase              =						self.get_lr_scheduler()

            return [optimizer], [scheduler]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            return self.validation_step(A,       A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.validation_end(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						max(1,       self.hparams.gpus      )  # TODO: consider num_tpu_cores
            _UpperCamelCase              =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if stage == "test":
                   _UpperCamelCase              =						len(self.test_dataloader().dataset      )
            else:
                   _UpperCamelCase              =						self.get_dataloader('''train''',       self.hparams.train_batch_size,       shuffle=A      )
                   _UpperCamelCase              =						len(self.train_dataloader().dataset      )

     def 	_UpperCamelCase     (      self,       A,       A,       A = False      ):





            """simple docstring"""

            raise NotImplementedError('''You must implement this for your task'''      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.train_loader

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.get_dataloader('''dev''',       self.hparams.eval_batch_size,       shuffle=A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.get_dataloader('''test''',       self.hparams.eval_batch_size,       shuffle=A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

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

     @pl.utilities.rank_zero_only
     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.output_dir.joinpath('''best_tfmr'''      )
            _UpperCamelCase              =						self.step_count
            self.model.save_pretrained(A      )
            self.tokenizer.save_pretrained(A      )



     @staticmethod
     def 	_UpperCamelCase     (      A,       A      ):





            """simple docstring"""

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






class  __A(						pl.Callback ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            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  __A(						pl.Callback ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            for name, param in pl_module.model.rag.named_parameters():
                   if param.grad is None:
                          print(A      )






class  __A(						pl.Callback ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						trainer.lr_schedulers[0]['''scheduler''']
            _UpperCamelCase              =						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
            pl_module.logger.log_metrics(A      )

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

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



     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

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






def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=None    ,       lowerCAmelCase=True    ,       lowerCAmelCase=[]    ,       lowerCAmelCase=None    ,       lowerCAmelCase=None    ,       **lowerCAmelCase    ,       ):
       pl.seed_everything(args.seed					)

       # init model
       _UpperCamelCase              =						Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=lowerCAmelCase					)

       # add custom checkpoints
       if checkpoint_callback is None:
              _UpperCamelCase              =						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:
              _UpperCamelCase              =						LoggingCallback()

       _UpperCamelCase              =						{}

       if args.fpaa:
              _UpperCamelCase              =						16

       if args.gpus > 1:
              _UpperCamelCase              =						'''auto'''
              _UpperCamelCase              =						'''ddp'''

       _UpperCamelCase              =						args.accumulate_grad_batches
       _UpperCamelCase              =						None
       _UpperCamelCase              =						'''auto'''

       _UpperCamelCase              =						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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






