"""simple docstring"""


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""       )
      _UpperCAmelCase    			=   AutoTokenizer.from_pretrained("""google/mt5-small"""       )

      _UpperCAmelCase    			=   tokenizer("""Hello there""" ,					return_tensors="""np"""       ).input_ids
      _UpperCAmelCase    			=   tokenizer("""Hi I am""" ,					return_tensors="""np"""       ).input_ids

      _UpperCAmelCase    			=   shift_tokens_right(lowerCamelCase_ ,					model.config.pad_token_id ,					model.config.decoder_start_token_id       )

      _UpperCAmelCase    			=   model(lowerCamelCase_ ,					decoder_input_ids=lowerCamelCase_       ).logits
      _UpperCAmelCase    			=   optax.softmax_cross_entropy(lowerCamelCase_ ,					onehot(lowerCamelCase_ ,					logits.shape[-1]       )       ).mean()

      _UpperCAmelCase    			=   -(labels.shape[-1] * loss.item())

      _UpperCAmelCase    			=   -84.9_127
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1e-4       )

'''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


UpperCamelCase__  :						Union[str, Any]					=      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__  :						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
UpperCamelCase__  :						Optional[Any]					=      {
    '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__  :						List[str]					=      sorted(arg_to_scheduler.keys())
UpperCamelCase__  :						int					=      '{' + ', '.join(arg_to_scheduler_choices) + '}'


class 					_lowercase						(   pl.LightningModule ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_="base"       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      Union[str, 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_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	0
         UpperCAmelCase__				:					Optional[Any] 					=	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=lowerCamelCase_       ,**lowerCamelCase_       ,)
         else:
            UpperCAmelCase__				:					PretrainedConfig 					=	config

         UpperCAmelCase__				:					Union[str, Any] 					=	('''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__				:					Tuple 					=	AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,cache_dir=lowerCamelCase_       ,)
         else:
            UpperCAmelCase__				:					PreTrainedTokenizer 					=	tokenizer
         UpperCAmelCase__				:					Tuple 					=	MODEL_MODES[mode]
         if model is None:
            UpperCAmelCase__				:					List[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__				:					Optional[int] 					=	model




      def 		lowerCAmelCase__       (					self       ,*lowerCamelCase_       ,**lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.model_type.from_pretrained(*lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.model
         UpperCAmelCase__				:					Optional[Any] 					=	['''bias''', '''LayerNorm.weight''']
         UpperCAmelCase__				:					List[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__				:					Tuple 					=	Adafactor(
                lowerCamelCase_       ,lr=self.hparams.learning_rate       ,scale_parameter=lowerCamelCase_       ,relative_step=lowerCamelCase_				)

         else:
            UpperCAmelCase__				:					Optional[int] 					=	AdamW(
                lowerCamelCase_       ,lr=self.hparams.learning_rate       ,eps=self.hparams.adam_epsilon				)
         UpperCAmelCase__				:					Optional[int] 					=	optimizer

         UpperCAmelCase__				:					int 					=	self.get_lr_scheduler()

         return [optimizer], [scheduler]




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         return self.validation_step(lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         return self.validation_end(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	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       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         if stage == "test":
            UpperCAmelCase__				:					List[str] 					=	len(self.test_dataloader().dataset				)
         else:
            UpperCAmelCase__				:					Optional[int] 					=	self.get_dataloader('''train'''       ,self.hparams.train_batch_size       ,shuffle=lowerCamelCase_				)
            UpperCAmelCase__				:					Optional[Any] 					=	len(self.train_dataloader().dataset				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = False				)      ->      str:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return self.train_loader




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.output_dir.joinpath('''best_tfmr'''				)
         UpperCAmelCase__				:					str 					=	self.step_count
         self.model.save_pretrained(lowerCamelCase_				)
         self.tokenizer.save_pretrained(lowerCamelCase_				)




      @staticmethod
      def 		lowerCAmelCase__       (					lowerCamelCase_       ,lowerCamelCase_				)      ->      List[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=32       ,type=lowerCamelCase_				)
         parser.add_argument('''--eval_batch_size'''       ,default=32       ,type=lowerCamelCase_				)
         parser.add_argument('''--adafactor'''       ,action='''store_true'''				)




class 					_lowercase						(   pl.Callback ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[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 ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



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




class 					_lowercase						(   pl.Callback ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	trainer.lr_schedulers[0]['''scheduler''']
         UpperCAmelCase__				:					Tuple 					=	{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
         pl_module.logger.log_metrics(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         rank_zero_info('''***** Validation results *****'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         rank_zero_info('''***** Test results *****'''				)
         UpperCAmelCase__				:					List[Any] 					=	trainer.callback_metrics
         # Log and save results to file
         UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Tuple	,      _A       :					str     ):
   '''simple docstring'''
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       '''--output_dir'''	,      default=str(Path(_A     ).parent / '''test_run''' / '''model_checkpoints'''     )	,      type=_A	,      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=_A	,      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=_A     )
   parser.add_argument('''--max_grad_norm'''	,      dest='''gradient_clip_val'''	,      default=1.0	,      type=_A	,      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=_A	,      default=1	,      help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,      )
   parser.add_argument('''--seed'''	,      type=_A	,      default=42	,      help='''random seed for initialization'''     )
   parser.add_argument(
       '''--data_dir'''	,      default=str(Path(_A     ).parent / '''test_run''' / '''dummy-train-data'''     )	,      type=_A	,      help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,      )







def __UpperCamelCase( _A       :					BaseTransformer	,      _A       :					argparse.Namespace	,      _A       :					Optional[Any]=None	,      _A       :					int=True	,      _A       :					Any=[]	,      _A       :					Optional[Any]=None	,      _A       :					Tuple=None	,      **_A       :					Dict	,      ):
   '''simple docstring'''
   pl.seed_everything(args.seed     )

   # init model
   UpperCAmelCase__				:					Tuple 					=	Path(model.hparams.output_dir     )
   odir.mkdir(exist_ok=_A     )

   # add custom checkpoints
   if checkpoint_callback is None:
      UpperCAmelCase__				:					List[str] 					=	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(_A     )
   if logging_callback is None:
      UpperCAmelCase__				:					Dict 					=	LoggingCallback()

   UpperCAmelCase__				:					Any 					=	{}

   if args.fpaa:
      UpperCAmelCase__				:					List[Any] 					=	16

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

   UpperCAmelCase__				:					Any 					=	args.accumulate_grad_batches
   UpperCAmelCase__				:					Dict 					=	None
   UpperCAmelCase__				:					List[str] 					=	'''auto'''

   UpperCAmelCase__				:					Dict 					=	pl.Trainer.from_argparse_args(
       _A	,      weights_summary=_A	,      callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]	,      logger=_A	,      val_check_interval=1	,      num_sanity_val_steps=2	,      **_A	,      )

   if args.do_train:
      trainer.fit(_A     )

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




