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


lowerCAmelCase__     						=			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase__     						=			{
    "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
lowerCAmelCase__     						=			{
    "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
}
lowerCAmelCase__     						=			sorted(arg_to_scheduler.keys())
lowerCAmelCase__     						=			"{" + ", ".join(arg_to_scheduler_choices) + "}"
class     a_			(						pl.LightningModule						):






 '''simple docstring'''






 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  List[Any]      ,   lowercase__ :  int=None      ,   lowercase__ :  Union[str, Any]="base"      ,   lowercase__ :  Any=None      ,   lowercase__ :  Dict=None      ,   lowercase__ :  List[Any]=None      ,   **lowercase__ :  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(A__)
        lowerCAmelCase__					     = 0
        lowerCAmelCase__					     = Path(self.hparams.output_dir)
        lowerCAmelCase__					     = self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
               lowerCAmelCase__					     = 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=A__      ,   **A__      ,   )
        else:
               lowerCAmelCase__					     = config

        lowerCAmelCase__					     = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
        for p in extra_model_params:
               if getattr(self.hparams      ,   A__      ,   A__):
                      assert hasattr(self.config      ,   A__), F"""model config doesn\'t have a `{p}` attribute"""
                      setattr(self.config      ,   A__      ,   getattr(self.hparams      ,   A__))

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




 def 		__snake_case						(      self :  Optional[Any]      ,   *lowercase__ :  List[str]      ,   **lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_type.from_pretrained(*A__      ,   **A__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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

        else:
               lowerCAmelCase__					     = AdamW(
                   A__      ,   lr=self.hparams.learning_rate      ,   eps=self.hparams.adam_epsilon)
        lowerCAmelCase__					     = optimizer

        lowerCAmelCase__					     = self.get_lr_scheduler()

        return [optimizer], [scheduler]




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  int      ,   lowercase__ :  int):







        '''simple docstring'''



        return self.validation_step(A__      ,   A__)




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        return self.validation_end(A__)




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = max(1      ,   self.hparams.gpus)  # TODO: consider num_tpu_cores
        lowerCAmelCase__					     = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  int):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  int      ,   lowercase__ :  int      ,   lowercase__ :  str      ,   lowercase__ :  Any = False):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.train_loader




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  str):







        '''simple docstring'''



        return os.path.join(
            self.hparams.data_dir      ,   'cached_{}_{}_{}'.format(
                A__      ,   list(filter(A__      ,   self.hparams.model_name_or_path.split('/'))).pop()      ,   str(self.hparams.max_seq_length)      ,   )      ,   )




 @pl.utilities.rank_zero_only
 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.output_dir.joinpath('best_tfmr')
        lowerCAmelCase__					     = self.step_count
        self.model.save_pretrained(A__)
        self.tokenizer.save_pretrained(A__)




 @staticmethod
 def 		__snake_case						(      lowercase__ :  str      ,   lowercase__ :  List[str]):







        '''simple docstring'''



        parser.add_argument(
            '--model_name_or_path'      ,   default=A__      ,   type=A__      ,   required=A__      ,   help='Path to pretrained model or model identifier from huggingface.co/models'      ,   )
        parser.add_argument(
            '--config_name'      ,   default=''      ,   type=A__      ,   help='Pretrained config name or path if not the same as model_name')
        parser.add_argument(
            '--tokenizer_name'      ,   default=A__      ,   type=A__      ,   help='Pretrained tokenizer name or path if not the same as model_name'      ,   )
        parser.add_argument(
            '--cache_dir'      ,   default=str(Path(A__).parent / 'test_run' / 'cache')      ,   type=A__      ,   help='Where do you want to store the pre-trained models downloaded from huggingface.co'      ,   )
        parser.add_argument(
            '--encoder_layerdrop'      ,   type=A__      ,   help='Encoder layer dropout probability (Optional). Goes into model.config'      ,   )
        parser.add_argument(
            '--decoder_layerdrop'      ,   type=A__      ,   help='Decoder layer dropout probability (Optional). Goes into model.config'      ,   )
        parser.add_argument(
            '--dropout'      ,   type=A__      ,   help='Dropout probability (Optional). Goes into model.config'      ,   )
        parser.add_argument(
            '--attention_dropout'      ,   type=A__      ,   help='Attention dropout probability (Optional). Goes into model.config'      ,   )
        parser.add_argument('--learning_rate'      ,   default=5e-5      ,   type=A__      ,   help='The initial learning rate for Adam.')
        parser.add_argument(
            '--lr_scheduler'      ,   default='linear'      ,   choices=A__      ,   metavar=A__      ,   type=A__      ,   help='Learning rate scheduler'      ,   )
        parser.add_argument('--weight_decay'      ,   default=0.0      ,   type=A__      ,   help='Weight decay if we apply some.')
        parser.add_argument('--adam_epsilon'      ,   default=1e-8      ,   type=A__      ,   help='Epsilon for Adam optimizer.')
        parser.add_argument('--warmup_steps'      ,   default=0      ,   type=A__      ,   help='Linear warmup over warmup_steps.')
        parser.add_argument('--num_workers'      ,   default=4      ,   type=A__      ,   help='kwarg passed to DataLoader')
        parser.add_argument('--num_train_epochs'      ,   dest='max_epochs'      ,   default=3      ,   type=A__)
        parser.add_argument('--train_batch_size'      ,   default=32      ,   type=A__)
        parser.add_argument('--eval_batch_size'      ,   default=32      ,   type=A__)
        parser.add_argument('--adafactor'      ,   action='store_true')






class     a_			(						pl.Callback						):






 '''simple docstring'''






 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  int      ,   lowercase__ :  Optional[Any]):







        '''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     a_			(						pl.Callback						):






 '''simple docstring'''






 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Any      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



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






class     a_			(						pl.Callback						):






 '''simple docstring'''






 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



        rank_zero_info('***** Validation results *****')
        lowerCAmelCase__					     = trainer.callback_metrics
        # Log results
        for key in sorted(A__):
               if key not in ["log", "progress_bar"]:
                      rank_zero_info('{} = {}\n'.format(A__      ,   str(metrics[key])))




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        rank_zero_info('***** Test results *****')
        lowerCAmelCase__					     = trainer.callback_metrics
        # Log and save results to file
        lowerCAmelCase__					     = os.path.join(pl_module.hparams.output_dir      ,   'test_results.txt')
        with open(A__      ,   'w') as writer:
               for key in sorted(A__):
                      if key not in ["log", "progress_bar"]:
                             rank_zero_info('{} = {}\n'.format(A__      ,   str(metrics[key])))
                             writer.write('{} = {}\n'.format(A__      ,   str(metrics[key])))






def 				__lowerCamelCase		(						lowerCAmelCase__ :    int     ,  lowerCAmelCase__ :    int     ):
       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__ :    Dict     ,  lowerCAmelCase__ :    Optional[int]     ,  lowerCAmelCase__ :    str=None     ,  lowerCAmelCase__ :    Dict=True     ,  lowerCAmelCase__ :    Optional[int]=[]     ,  lowerCAmelCase__ :    Optional[int]=None     ,  lowerCAmelCase__ :    Tuple=None     ,  **lowerCAmelCase__ :    int     ,  ):
       pl.seed_everything(args.seed     )

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

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

       lowerCAmelCase__					     = {}

       if args.fpaa:
              lowerCAmelCase__					     = 1_6

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

       lowerCAmelCase__					     = args.accumulate_grad_batches
       lowerCAmelCase__					     = None
       lowerCAmelCase__					     = 'auto'

       lowerCAmelCase__					     = 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

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,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase__     						=			TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase__     						=			TaTokenizerFast

lowerCAmelCase__     						=			{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'MT5EncoderModel',
        'MT5ForConditionalGeneration',
        'MT5ForQuestionAnswering',
        'MT5Model',
        'MT5PreTrainedModel',
        'MT5Stack',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(
        __name__,
        globals()['__file__'],
        _import_structure,
        extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
        module_spec=__spec__,
    )
