

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    _lowercase				(					pl.LightningModule			):

   def __init__(				self	:    Dict			,						lowerCamelCase__	:    argparse.Namespace			,						lowerCamelCase__	:    int=None			,						lowerCamelCase__	:    Dict="base"			,						lowerCamelCase__	:    Union[str, Any]=None			,						lowerCamelCase__	:    str=None			,						lowerCamelCase__	:    Optional[Any]=None			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			Any:
         """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(lowerCamelCase__       )
         A_							       =		0
         A_							       =		Path(self.hparams.output_dir       )
         A_							       =		self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               A_							       =		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:
               A_							       =		config

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

   def     UpperCamelCase					(				self	:    Optional[int]			,						*lowerCamelCase__	:    List[Any]			,						**lowerCamelCase__	:    Union[str, Any]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_type.from_pretrained(*lowerCamelCase__			,						**lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Optional[int]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Tuple       ) ->			int:
         """simple docstring"""





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

         else:
               A_							       =		AdamW(
                   lowerCamelCase__			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon       )
         A_							       =		optimizer

         A_							       =		self.get_lr_scheduler()

         return [optimizer], [scheduler]

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Dict       ) ->			Dict:
         """simple docstring"""





         return self.validation_step(lowerCamelCase__			,						lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    List[str]       ) ->			List[Any]:
         """simple docstring"""





         return self.validation_end(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         A_							       =		max(1			,						self.hparams.gpus       )  # TODO: consider num_tpu_cores
         A_							       =		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	:    Optional[Any]			,						lowerCamelCase__	:    Tuple       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    bool = False       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Dict       ) ->			int:
         """simple docstring"""





         return self.train_loader

   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    Union[str, Any]       ) ->			List[str]:
         """simple docstring"""





         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     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    Dict[str, Any]       ) ->			None:
         """simple docstring"""





         A_							       =		self.output_dir.joinpath('''best_tfmr'''       )
         A_							       =		self.step_count
         self.model.save_pretrained(lowerCamelCase__       )
         self.tokenizer.save_pretrained(lowerCamelCase__       )





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    int			,						lowerCamelCase__	:    int       ) ->			str:
         """simple docstring"""





         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=3_2			,						type=lowerCamelCase__       )
         parser.add_argument('''--eval_batch_size'''			,						default=3_2			,						type=lowerCamelCase__       )
         parser.add_argument('''--adafactor'''			,						action='''store_true'''       )



class    _lowercase				(					pl.Callback			):

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any       ) ->			int:
         """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    _lowercase				(					pl.Callback			):

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Tuple       ) ->			Tuple:
         """simple docstring"""





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



class    _lowercase				(					pl.Callback			):

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Dict       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    pl.Trainer			,						lowerCamelCase__	:    pl.LightningModule       ) ->			str:
         """simple docstring"""





         rank_zero_info('''***** Validation results *****'''       )
         A_							       =		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     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    pl.Trainer			,						lowerCamelCase__	:    pl.LightningModule       ) ->			int:
         """simple docstring"""





         rank_zero_info('''***** Test results *****'''       )
         A_							       =		trainer.callback_metrics
         # Log and save results to file
         A_							       =		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   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=[]		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE		,				):
      '''simple docstring'''







      pl.seed_everything(args.seed   )

      # init model
      A_							       =		Path(model.hparams.output_dir   )
      odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE   )

      # add custom checkpoints
      if checkpoint_callback is None:
            A_							       =		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(SCREAMING_SNAKE_CASE   )
      if logging_callback is None:
            A_							       =		LoggingCallback()

      A_							       =		{}

      if args.fpaa:
            A_							       =		16

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

      A_							       =		args.accumulate_grad_batches
      A_							       =		None
      A_							       =		'''auto'''

      A_							       =		pl.Trainer.from_argparse_args(
          SCREAMING_SNAKE_CASE		,				weights_summary=SCREAMING_SNAKE_CASE		,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,				logger=SCREAMING_SNAKE_CASE		,				val_check_interval=1		,				num_sanity_val_steps=2		,				**SCREAMING_SNAKE_CASE		,				)

      if args.do_train:
            trainer.fit(SCREAMING_SNAKE_CASE   )

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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      A_							       =		[]
      for char_count in range(SCREAMING_SNAKE_CASE   ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]   )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]   )
      return "".join(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)





