


'''simple docstring'''






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  __magic_name__							(		pl.LightningModule    ):






       def __init__(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="base"  ,							snake_case=None  ,							snake_case=None  ,							snake_case=None  ,							**snake_case  ,							)						->  Optional[int]:




          '''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(snake_case)
          _UpperCAmelCase       :       Any							   =0
          _UpperCAmelCase       :       Dict							   =Path(self.hparams.output_dir)
          _UpperCAmelCase       :       Union[str, Any]							   =self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
             _UpperCAmelCase       :       List[str]							   =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:
             _UpperCAmelCase       :       PretrainedConfig							   =config

          _UpperCAmelCase       :       List[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:
             _UpperCAmelCase       :       Optional[int]							   =AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,							cache_dir=snake_case  ,							)
          else:
             _UpperCAmelCase       :       PreTrainedTokenizer							   =tokenizer
          _UpperCAmelCase       :       List[Any]							   =MODEL_MODES[mode]
          if model is None:
             _UpperCAmelCase       :       Dict							   =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:
             _UpperCAmelCase       :       int							   =model






       def       lowerCAmelCase		(      self  ,							*snake_case  ,							**snake_case)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.model_type.from_pretrained(*snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =arg_to_scheduler[self.hparams.lr_scheduler]
          _UpperCAmelCase       :       int							   =get_schedule_func(
              self.opt  ,							num_warmup_steps=self.hparams.warmup_steps  ,							num_training_steps=self.total_steps())
          _UpperCAmelCase       :       int							   ={'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
          return scheduler






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.model
          _UpperCAmelCase       :       Optional[Any]							   =['bias', 'LayerNorm.weight']
          _UpperCAmelCase       :       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:
             _UpperCAmelCase       :       Union[str, Any]							   =Adafactor(
                 snake_case  ,							lr=self.hparams.learning_rate  ,							scale_parameter=snake_case  ,							relative_step=snake_case)

          else:
             _UpperCAmelCase       :       Union[str, Any]							   =AdamW(
                 snake_case  ,							lr=self.hparams.learning_rate  ,							eps=self.hparams.adam_epsilon)
          _UpperCAmelCase       :       Any							   =optimizer

          _UpperCAmelCase       :       str							   =self.get_lr_scheduler()

          return [optimizer], [scheduler]






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          return self.validation_step(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.validation_end(snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =max(1  ,							self.hparams.gpus)  # TODO: consider num_tpu_cores
          _UpperCAmelCase       :       List[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       lowerCAmelCase		(      self  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          if stage == "test":
             _UpperCAmelCase       :       int							   =len(self.test_dataloader().dataset)
          else:
             _UpperCAmelCase       :       Dict							   =self.get_dataloader('train'  ,							self.hparams.train_batch_size  ,							shuffle=snake_case)
             _UpperCAmelCase       :       str							   =len(self.train_dataloader().dataset)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = False)						->  str:




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






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          return self.train_loader






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          return self.get_dataloader('dev'  ,							self.hparams.eval_batch_size  ,							shuffle=snake_case)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          return self.get_dataloader('test'  ,							self.hparams.eval_batch_size  ,							shuffle=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          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       lowerCAmelCase		(      self  ,							snake_case)						->  None:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.output_dir.joinpath('best_tfmr')
          _UpperCAmelCase       :       Dict							   =self.step_count
          self.model.save_pretrained(snake_case)
          self.tokenizer.save_pretrained(snake_case)






       @staticmethod
       def       lowerCAmelCase		(      snake_case  ,							snake_case)						->  Union[str, Any]:




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






class  __magic_name__							(		pl.Callback    ):






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  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  __magic_name__							(		pl.Callback    ):






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          # print(pl_module.model.rag)
          for name, param in pl_module.model.rag.named_parameters():
             if param.grad is None:
                print(snake_case)






class  __magic_name__							(		pl.Callback    ):






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =trainer.lr_schedulers[0]['scheduler']
          _UpperCAmelCase       :       Optional[int]							   ={f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
          pl_module.logger.log_metrics(snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          rank_zero_info('***** Validation results *****')
          _UpperCAmelCase       :       Union[str, 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       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          rank_zero_info('***** Test results *****')
          _UpperCAmelCase       :       Dict							   =trainer.callback_metrics
          # Log and save results to file
          _UpperCAmelCase       :       int							   =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 lowerCamelCase__      (						__lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Any							):







   '''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=4_2	,   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__      (						__lowerCamelCase	:							BaseTransformer	,   __lowerCamelCase	:							argparse.Namespace	,   __lowerCamelCase	:							Optional[int]=None	,   __lowerCamelCase	:							str=True	,   __lowerCamelCase	:							int=[]	,   __lowerCamelCase	:							Optional[Any]=None	,   __lowerCamelCase	:							Any=None	,   **__lowerCamelCase	:							Optional[int]	,   ):







   '''simple docstring'''




   pl.seed_everything(args.seed							)

   # init model
   _UpperCAmelCase       :       Tuple							   =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       :       Optional[int]							   =LoggingCallback()

   _UpperCAmelCase       :       int							   ={}

   if args.fpaa:
      _UpperCAmelCase       :       str							   =1_6

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

   _UpperCAmelCase       :       Optional[int]							   =args.accumulate_grad_batches
   _UpperCAmelCase       :       Union[str, Any]							   =None
   _UpperCAmelCase       :       Tuple							   ='auto'

   _UpperCAmelCase       :       int							   =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 jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

