






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


__snake_case							      =						logging.getLogger(__name__)

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

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







class     UpperCAmelCase_		(      pl.LightningModule				):



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_="base"					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   List[str]:
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :str										=     0
          UpperCamelCase :int										=     Path(self.hparams.output_dir				)
          UpperCamelCase :List[str]										=     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=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )
          else:
              UpperCamelCase :PretrainedConfig										=     config

          UpperCamelCase :List[str]										=     ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
          for p in extra_model_params:
              if getattr(self.hparams					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                  assert hasattr(self.config					,       SCREAMING_SNAKE_CASE_				), F'''model config doesn\'t have a `{p}` attribute'''
                  setattr(self.config					,       SCREAMING_SNAKE_CASE_					,       getattr(self.hparams					,       SCREAMING_SNAKE_CASE_				)				)

          if tokenizer is None:
              UpperCamelCase :str										=     AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,       cache_dir=SCREAMING_SNAKE_CASE_					,       )
          else:
              UpperCamelCase :PreTrainedTokenizer										=     tokenizer
          UpperCamelCase :Tuple										=     MODEL_MODES[mode]
          if model is None:
              UpperCamelCase :Tuple										=     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=SCREAMING_SNAKE_CASE_					,       )
          else:
              UpperCamelCase :List[Any]										=     model
      def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Dict:
          UpperCamelCase :Tuple										=     self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   str:
          UpperCamelCase :Union[str, Any]										=     arg_to_scheduler[self.hparams.lr_scheduler]
          UpperCamelCase :Optional[int]										=     get_schedule_func(
              self.opt					,       num_warmup_steps=self.hparams.warmup_steps					,       num_training_steps=self.total_steps()				)
          UpperCamelCase :Any										=     {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
          return scheduler
      def 					UpperCAmelCase		(       self				)       ->   Tuple:
          UpperCamelCase :Optional[Any]										=     self.model
          UpperCamelCase :Dict										=     ['''bias''', '''LayerNorm.weight''']
          UpperCamelCase :str										=     [
              {
                  '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
                  '''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 :int										=     Adafactor(
                  SCREAMING_SNAKE_CASE_					,       lr=self.hparams.learning_rate					,       scale_parameter=SCREAMING_SNAKE_CASE_					,       relative_step=SCREAMING_SNAKE_CASE_				)

          else:
              UpperCamelCase :Optional[int]										=     AdamW(
                  SCREAMING_SNAKE_CASE_					,       lr=self.hparams.learning_rate					,       eps=self.hparams.adam_epsilon				)
          UpperCamelCase :Union[str, Any]										=     optimizer

          UpperCamelCase :str										=     self.get_lr_scheduler()

          return [optimizer], [scheduler]
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
          return self.validation_step(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
          return self.validation_end(SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :Any										=     max(1					,       self.hparams.gpus				)  # TODO: consider num_tpu_cores
          UpperCamelCase :Optional[int]										=     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					,       SCREAMING_SNAKE_CASE_				)       ->   str:
          if stage == "test":
              UpperCamelCase :Tuple										=     len(self.test_dataloader().dataset				)
          else:
              UpperCamelCase :Dict										=     self.get_dataloader('''train'''					,       self.hparams.train_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Tuple										=     len(self.train_dataloader().dataset				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = False				)       ->   Any:
          raise NotImplementedError('''You must implement this for your task'''				)
      def 					UpperCAmelCase		(       self				)       ->   Dict:
          return self.train_loader
      def 					UpperCAmelCase		(       self				)       ->   Any:
          return self.get_dataloader('''dev'''					,       self.hparams.eval_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Tuple:
          return self.get_dataloader('''test'''					,       self.hparams.eval_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[int]:
          return os.path.join(
              self.hparams.data_dir					,       '''cached_{}_{}_{}'''.format(
                  SCREAMING_SNAKE_CASE_					,       list(filter(SCREAMING_SNAKE_CASE_					,       self.hparams.model_name_or_path.split('''/'''				)				)				).pop()					,       str(self.hparams.max_seq_length				)					,       )					,       )
      @pl.utilities.rank_zero_only
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
          UpperCamelCase :Dict										=     self.output_dir.joinpath('''best_tfmr'''				)
          UpperCamelCase :str										=     self.step_count
          self.model.save_pretrained(SCREAMING_SNAKE_CASE_				)
          self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_				)




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







class     UpperCAmelCase_		(      pl.Callback				):



      """simple docstring"""
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   int:
          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     UpperCAmelCase_		(      pl.Callback				):



      """simple docstring"""
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
          UpperCamelCase :Dict										=     trainer.lr_schedulers[0]['''scheduler''']
          UpperCamelCase :List[Any]										=     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
          pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
          rank_zero_info('''***** Validation results *****'''				)
          UpperCamelCase :Any										=     trainer.callback_metrics
          # Log results
          for key in sorted(SCREAMING_SNAKE_CASE_				):
              if key not in ["log", "progress_bar"]:
                  rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_					,       str(metrics[key]				)				)				)




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







def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Dict ):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''  ,						default=SCREAMING_SNAKE_CASE__  ,						type=SCREAMING_SNAKE_CASE__  ,						required=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=SCREAMING_SNAKE_CASE__  ,						type=SCREAMING_SNAKE_CASE__  ,						required=SCREAMING_SNAKE_CASE__  ,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,						)



def 						_A     (       SCREAMING_SNAKE_CASE__						:			BaseTransformer  ,						SCREAMING_SNAKE_CASE__						:			argparse.Namespace  ,						SCREAMING_SNAKE_CASE__						:			Tuple=None  ,						SCREAMING_SNAKE_CASE__						:			str=True  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]=None  ,						SCREAMING_SNAKE_CASE__						:			Tuple=[]  ,						SCREAMING_SNAKE_CASE__						:			List[str]=None  ,						SCREAMING_SNAKE_CASE__						:			str=None  ,						**SCREAMING_SNAKE_CASE__						:			int  ,						):
    pl.seed_everything(args.seed )

    # init model
    UpperCamelCase :Optional[Any]										=     Path(model.hparams.output_dir )
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )

    # add custom checkpoints
    if checkpoint_callback is None:
        UpperCamelCase :Tuple										=     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:
        UpperCamelCase :Any										=     LoggingCallback()

    UpperCamelCase :Any										=     {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        UpperCamelCase :Optional[int]										=     16
        # train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        UpperCamelCase :Optional[int]										=     '''auto'''  # "ddp"
        UpperCamelCase :Optional[int]										=     '''ddp'''

    UpperCamelCase :int										=     args.accumulate_grad_batches
    UpperCamelCase :Tuple										=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
    UpperCamelCase :List[str]										=     '''auto'''

    UpperCamelCase :Optional[int]										=     pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE__  ,						weights_summary=SCREAMING_SNAKE_CASE__  ,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,						logger=SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__  ,						)

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE__ )

    return trainer













# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      @classmethod
      def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
          return cls




      @slow
      @require_torch_gpu
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :int										=     self.get_auto_remove_tmp_dir()
          UpperCamelCase :List[str]										=     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

          # we want this test to run even if there is only one GPU, but if there are more we use them all
          UpperCamelCase :List[str]										=     get_gpu_count()
          UpperCamelCase :Optional[Any]										=     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
          UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
          execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)

          UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
          UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
          # print(metrics)
          self.assertGreaterEqual(metrics['''bleu''']					,       25				)





