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			):



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

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



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A=	self.model_type.from_pretrained(*lowerCAmelCase__				,      **lowerCAmelCase__       )



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



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

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

            _A=	self.get_lr_scheduler()

            return [optimizer], [scheduler]



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            return self.validation_step(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return self.validation_end(lowerCAmelCase__       )



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



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



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



     def 			a__  (					self       ):
            return self.train_loader



     def 			a__  (					self       ):
            return self.get_dataloader('dev'				,      self.hparams.eval_batch_size				,      shuffle=lowerCAmelCase__       )



     def 			a__  (					self       ):
            return self.get_dataloader('test'				,      self.hparams.eval_batch_size				,      shuffle=lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            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 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	self.output_dir.joinpath('best_tfmr'       )
            _A=	self.step_count
            self.model.save_pretrained(lowerCAmelCase__       )
            self.tokenizer.save_pretrained(lowerCAmelCase__       )





     @staticmethod
     def 			a__  (					lowerCAmelCase__				,      lowerCAmelCase__       ):
            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      lowerCAmelCase						(					pl.Callback			):



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            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			):



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





class      lowerCAmelCase						(					pl.Callback			):



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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            rank_zero_info('***** Validation results *****'       )
            _A=	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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            rank_zero_info('***** Test results *****'       )
            _A=	trainer.callback_metrics
            # Log and save results to file
            _A=	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				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						None:




       '''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=42       ,					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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=True       ,					lowerCAmelCase_=[]       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=None       ,					**lowerCAmelCase_       ,					)		->						Any:




       '''simple docstring'''






       pl.seed_everything(args.seed   )

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

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

       _A=	{}

       if args.fpaa:
              _A=	16

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

       _A=	args.accumulate_grad_batches
       _A=	None
       _A=	'auto'

       _A=	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
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()