


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      =   logging.getLogger(__name__)

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

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







class lowerCAmelCase_						(			pl.LightningModule	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=None		,				_UpperCAmelCase="base"		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       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 )
       snake_case_	=				0
       snake_case_	=				Path(self.hparams.output_dir )
       snake_case_	=				self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             snake_case_	=				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:
             snake_case_	=				config

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



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       snake_case_	=				self.model_type.from_pretrained(*_UpperCAmelCase		,				**_UpperCAmelCase )



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model
       snake_case_	=				['''bias''', '''LayerNorm.weight''']
       snake_case_	=				[
           {
               '''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:
             snake_case_	=				Adafactor(
                 _UpperCAmelCase		,				lr=self.hparams.learning_rate		,				scale_parameter=_UpperCAmelCase		,				relative_step=_UpperCAmelCase )

       else:
             snake_case_	=				AdamW(
                 _UpperCAmelCase		,				lr=self.hparams.learning_rate		,				eps=self.hparams.adam_epsilon )
       snake_case_	=				optimizer

       snake_case_	=				self.get_lr_scheduler()

       return [optimizer], [scheduler]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return self.validation_step(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return self.validation_end(_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				max(1		,				self.hparams.gpus )  # TODO: consider num_tpu_cores
       snake_case_	=				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		,				_UpperCAmelCase ):
       if stage == "test":
             snake_case_	=				len(self.test_dataloader().dataset )
       else:
             snake_case_	=				self.get_dataloader('''train'''		,				self.hparams.train_batch_size		,				shuffle=_UpperCAmelCase )
             snake_case_	=				len(self.train_dataloader().dataset )



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



 def 						UpperCamelCase__					(     self ):
       return self.train_loader



 def 						UpperCamelCase__					(     self ):
       return self.get_dataloader('''dev'''		,				self.hparams.eval_batch_size		,				shuffle=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       return self.get_dataloader('''test'''		,				self.hparams.eval_batch_size		,				shuffle=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				self.output_dir.joinpath('''best_tfmr''' )
       snake_case_	=				self.step_count
       self.model.save_pretrained(_UpperCAmelCase )
       self.tokenizer.save_pretrained(_UpperCAmelCase )





 @staticmethod
 def 						UpperCamelCase__					(     _UpperCAmelCase		,				_UpperCAmelCase ):
       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=str(Path(_UpperCAmelCase ).parent / '''test_run''' / '''cache''' )		,				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 lowerCAmelCase_						(			pl.Callback	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       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	):

 '''simple docstring'''

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







class lowerCAmelCase_						(			pl.Callback	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				trainer.lr_schedulers[0]['''scheduler''']
       snake_case_	=				{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
       pl_module.logger.log_metrics(_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       rank_zero_info('''***** Validation results *****''' )
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       rank_zero_info('''***** Test results *****''' )
       snake_case_	=				trainer.callback_metrics
       # Log and save results to file
       snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  None:




      """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 , )->  str:




      """simple docstring"""

      pl.seed_everything(args.seed )

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

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

      snake_case_	=				{}

      if args.fpaa:
            snake_case_	=				16

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

      snake_case_	=				args.accumulate_grad_batches
      snake_case_	=				None
      snake_case_	=				'''auto'''

      snake_case_	=				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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      =   {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase      =   _LazyModule(__name__, globals()["""__file__"""], _import_structure)