




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							     =					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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

class     __a(	pl.LightningModule  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="base"  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         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    )
         UpperCAmelCase_     :				List[Any]  =     0
         UpperCAmelCase_     :				Any  =     Path(self.hparams.output_dir    )
         UpperCAmelCase_     :				Optional[Any]  =     self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
              UpperCAmelCase_     :				Optional[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=__lowerCAmelCase  ,**__lowerCAmelCase  ,)
         else:
              UpperCAmelCase_     :				List[Any]  =     config

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





    def 				a__ ( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				Any  =     self.model_type.from_pretrained(*__lowerCAmelCase  ,**__lowerCAmelCase    )





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





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				str  =     self.model
         UpperCAmelCase_     :				Union[str, Any]  =     ['''bias''', '''LayerNorm.weight''']
         UpperCAmelCase_     :				int  =     [
             {
                 '''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_     :				Any  =     Adafactor(
                  __lowerCAmelCase  ,lr=self.hparams.learning_rate  ,scale_parameter=__lowerCAmelCase  ,relative_step=__lowerCAmelCase    )

         else:
              UpperCAmelCase_     :				Dict  =     AdamW(
                  __lowerCAmelCase  ,lr=self.hparams.learning_rate  ,eps=self.hparams.adam_epsilon    )
         UpperCAmelCase_     :				Dict  =     optimizer

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

         return [optimizer], [scheduler]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         return self.validation_step(__lowerCAmelCase  ,__lowerCAmelCase    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         return self.validation_end(__lowerCAmelCase    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Optional[Any]  =     max(1  ,self.hparams.gpus    )  # TODO: consider num_tpu_cores
         UpperCAmelCase_     :				Optional[Any]  =     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:



         if stage == "test":
              UpperCAmelCase_     :				Tuple  =     len(self.test_dataloader().dataset    )
         else:
              UpperCAmelCase_     :				int  =     self.get_dataloader('''train'''  ,self.hparams.train_batch_size  ,shuffle=__lowerCAmelCase    )
              UpperCAmelCase_     :				Optional[int]  =     len(self.train_dataloader().dataset    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = False    )					->						Union[str, Any]:
         raise NotImplementedError('''You must implement this for your task'''    )





    def 				a__ ( self    )					->						int:
         return self.train_loader





    def 				a__ ( self    )					->						str:
         return self.get_dataloader('''dev'''  ,self.hparams.eval_batch_size  ,shuffle=__lowerCAmelCase    )





    def 				a__ ( self    )					->						List[Any]:
         return self.get_dataloader('''test'''  ,self.hparams.eval_batch_size  ,shuffle=__lowerCAmelCase    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						int:
         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 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				Any  =     self.output_dir.joinpath('''best_tfmr'''    )
         UpperCAmelCase_     :				List[str]  =     self.step_count
         self.model.save_pretrained(__lowerCAmelCase    )
         self.tokenizer.save_pretrained(__lowerCAmelCase    )





    @staticmethod
    def 				a__ ( _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         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=str(Path(__lowerCAmelCase    ).parent / '''test_run''' / '''cache'''    )  ,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     __a(	pl.Callback  ):






    """simple docstring"""





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:



         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  ):






    """simple docstring"""





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						str:



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


class     __a(	pl.Callback  ):






    """simple docstring"""





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						int:
         UpperCAmelCase_     :				Any  =     trainer.lr_schedulers[0]['''scheduler''']
         UpperCAmelCase_     :				Dict  =     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
         pl_module.logger.log_metrics(__lowerCAmelCase    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         rank_zero_info('''***** Validation results *****'''    )
         UpperCAmelCase_     :				List[str]  =     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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						int:
         rank_zero_info('''***** Test results *****'''    )
         UpperCAmelCase_     :				Tuple  =     trainer.callback_metrics
         # Log and save results to file
         UpperCAmelCase_     :				Tuple  =     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 lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''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 lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=True					,		_lowercase=[]					,		_lowercase=None					,		_lowercase=None					,		**_lowercase					,		):

     '''simple docstring'''


     pl.seed_everything(args.seed )

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

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

     UpperCAmelCase_     :				Any  =     {}

     if args.fpaa:
          UpperCAmelCase_     :				Optional[int]  =     16

     if args.gpus > 1:
          UpperCAmelCase_     :				str  =     '''auto'''
          UpperCAmelCase_     :				str  =     '''ddp'''

     UpperCAmelCase_     :				Dict  =     args.accumulate_grad_batches
     UpperCAmelCase_     :				Dict  =     None
     UpperCAmelCase_     :				Optional[int]  =     '''auto'''

     UpperCAmelCase_     :				Dict  =     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"""






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
       import sqlitea

       import sqlalchemy





class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(features=__lowerCAmelCase						,			cache_dir=__lowerCAmelCase						,			keep_in_memory=__lowerCAmelCase						,			**__lowerCAmelCase		)
            lowercase								=      Sql(
                cache_dir=__lowerCAmelCase						,			features=__lowerCAmelCase						,			sql=__lowerCAmelCase						,			con=__lowerCAmelCase						,			**__lowerCAmelCase						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      None
            lowercase								=      None
            lowercase								=      None

            self.builder.download_and_prepare(
                download_config=__lowerCAmelCase						,			download_mode=__lowerCAmelCase						,			verification_mode=__lowerCAmelCase						,			base_path=__lowerCAmelCase						,			)

            # Build dataset for splits
            lowercase								=      self.builder.as_dataset(
                split="""train"""						,			verification_mode=__lowerCAmelCase						,			in_memory=self.keep_in_memory		)
            return dataset






class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'num_proc {num_proc} must be an integer > 0.'		)

            lowercase								=      dataset
            lowercase								=      name
            lowercase								=      con
            lowercase								=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            lowercase								=      num_proc
            lowercase								=      to_sql_kwargs

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.to_sql_kwargs.pop("""sql"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""con"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""index"""						,			__lowerCAmelCase		)

            lowercase								=      self._write(index=__lowerCAmelCase						,			**self.to_sql_kwargs		)
            return written

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase				,				lowercase				,				lowercase								=      args
            lowercase								=      {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
            lowercase								=      query_table(
                table=self.dataset.data						,			key=slice(__lowerCAmelCase						,			offset + self.batch_size		)						,			indices=self.dataset._indices						,			)
            lowercase								=      batch.to_pandas()
            lowercase								=      df.to_sql(self.name						,			self.con						,			index=__lowerCAmelCase						,			**__lowerCAmelCase		)
            return num_rows or len(__lowerCAmelCase		)







      def 							A__			(		self						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      0

            if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0						,			len(self.dataset		)						,			self.batch_size		)						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                        written += self._batch_sql((offset, index, to_sql_kwargs)		)
            else:
                  lowercase				,				lowercase								=      len(self.dataset		), self.batch_size
                  with multiprocessing.Pool(self.num_proc		) as pool:
                        for num_rows in logging.tqdm(
                            pool.imap(
                                self._batch_sql						,			[(offset, index, to_sql_kwargs) for offset in range(0						,			__lowerCAmelCase						,			__lowerCAmelCase		)]						,			)						,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                              written += num_rows

            return written



