code: 
  
  
  
  
  
  import copy
  import inspect
  
  from transformers.testing_utils import require_torch, torch_device
  from transformers.utils.backbone_utils import BackboneType
  
  @require_torch
  class 				A    :
        _SCREAMING_SNAKE_CASE					=							()
        _SCREAMING_SNAKE_CASE					=							True
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.config_class
  
               # test default config
               _snake_case      :							Dict     				=						config_class()
               self.assertIsNotNone(lowercase_					)
               _snake_case      :							List[str]     				=						['stem'] + [F'''stage{idx}''' for idx in range(1     ,  len(config.depths					) + 1					)]
               self.assertEqual(config.stage_names     ,  lowercase_					)
               self.assertTrue(set(config.out_features					).issubset(set(config.stage_names					)					)					)
  
               # Test out_features and out_indices are correctly set
               # out_features and out_indices both None
               _snake_case      :							Dict     				=						config_class(out_features=lowercase_     ,  out_indices=lowercase_					)
               self.assertEqual(config.out_features     ,  [config.stage_names[-1]]					)
               self.assertEqual(config.out_indices     ,  [len(config.stage_names					) - 1]					)
  
               # out_features and out_indices both set
               _snake_case      :							str     				=						config_class(out_features=['''stem''', '''stage1''']     ,  out_indices=[0, 1]					)
               self.assertEqual(config.out_features     ,  ['''stem''', '''stage1''']					)
               self.assertEqual(config.out_indices     ,  [0, 1]					)
  
               # Only out_features set
               _snake_case      :							Dict     				=						config_class(out_features=['''stage1''', '''stage3''']					)
               self.assertEqual(config.out_features     ,  ['''stage1''', '''stage3''']					)
               self.assertEqual(config.out_indices     ,  [1, 3]					)
  
               # Only out_indices set
               _snake_case      :							Dict     				=						config_class(out_indices=[0, 2]					)
               self.assertEqual(config.out_features     ,  [config.stage_names[0], config.stage_names[2]]					)
               self.assertEqual(config.out_indices     ,  [0, 2]					)
  
               # Error raised when out_indices do not correspond to out_features
               with self.assertRaises(lowercase_					):
                      _snake_case      :							Tuple     				=						config_class(out_features=['''stage1''', '''stage2''']     ,  out_indices=[0, 2]					)
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      _snake_case      :							Tuple     				=						model_class(lowercase_					)
                      _snake_case      :							str     				=						inspect.signature(model.forward					)
                      # signature.parameters is an OrderedDict => so arg_names order is deterministic
                      _snake_case      :							Dict     				=						[*signature.parameters.keys()]
                      _snake_case      :							Union[str, Any]     				=						['pixel_values']
                      self.assertListEqual(arg_names[:1]     ,  lowercase_					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      _snake_case      :							Dict     				=						model_class(lowercase_					)
                      self.assertEqual(len(model.channels					)     ,  len(config.out_features					)					)
                      _snake_case      :							int     				=						model.num_features
                      _snake_case      :							Tuple     				=						[config.stage_names.index(lowercase_					) for feat in config.out_features]
                      _snake_case      :							Dict     				=						[num_features[idx] for idx in out_indices]
                      self.assertListEqual(model.channels     ,  lowercase_					)
  
                      _snake_case      :							Any     				=						copy.deepcopy(lowercase_					)
                      _snake_case      :							Optional[Any]     				=						None
                      _snake_case      :							Optional[int]     				=						model_class(lowercase_					)
                      self.assertEqual(len(model.channels					)     ,  1					)
                      self.assertListEqual(model.channels     ,  [num_features[-1]]					)
  
                      _snake_case      :							Dict     				=						copy.deepcopy(lowercase_					)
                      _snake_case      :							Optional[int]     				=						None
                      _snake_case      :							int     				=						model_class(lowercase_					)
                      self.assertEqual(len(model.channels					)     ,  1					)
                      self.assertListEqual(model.channels     ,  [num_features[-1]]					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      _snake_case      :							int     				=						model_class(lowercase_					)
                      model.to(lowercase_					)
                      model.eval()
                      _snake_case      :							int     				=						model(**lowercase_					)
  
                      self.assertEqual(len(result.feature_maps					)     ,  len(config.out_features					)					)
                      self.assertEqual(len(model.channels					)     ,  len(config.out_features					)					)
                      self.assertEqual(len(result.feature_maps					)     ,  len(config.out_indices					)					)
                      self.assertEqual(len(model.channels					)     ,  len(config.out_indices					)					)
  
                      # Check output of last stage is taken if out_features=None, out_indices=None
                      _snake_case      :							List[str]     				=						copy.deepcopy(lowercase_					)
                      _snake_case      :							Any     				=						None
                      _snake_case      :							Optional[int]     				=						model_class(lowercase_					)
                      model.to(lowercase_					)
                      model.eval()
                      _snake_case      :							Any     				=						model(**lowercase_					)
  
                      self.assertEqual(len(result.feature_maps					)     ,  1					)
                      self.assertEqual(len(model.channels					)     ,  1					)
  
                      _snake_case      :							Any     				=						copy.deepcopy(lowercase_					)
                      _snake_case      :							Dict     				=						None
                      _snake_case      :							int     				=						model_class(lowercase_					)
                      model.to(lowercase_					)
                      model.eval()
                      _snake_case      :							int     				=						model(**lowercase_					)
  
                      self.assertEqual(len(result.feature_maps					)     ,  1					)
                      self.assertEqual(len(model.channels					)     ,  1					)
  
                      # Check backbone can be initialized with fresh weights
                      _snake_case      :							Union[str, Any]     				=						copy.deepcopy(lowercase_					)
                      _snake_case      :							Tuple     				=						False
                      _snake_case      :							Any     				=						model_class(lowercase_					)
                      model.to(lowercase_					)
                      model.eval()
                      _snake_case      :							Any     				=						model(**lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.model_tester.prepare_config_and_inputs_for_common()
  
               for backbone_class in self.all_model_classes:
                      _snake_case      :							int     				=						backbone_class(lowercase_					)
  
                      self.assertTrue(hasattr(lowercase_     ,  '''backbone_type'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''stage_names'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''num_features'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''out_indices'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''out_features'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''out_feature_channels'''					)					)
                      self.assertTrue(hasattr(lowercase_     ,  '''channels'''					)					)
  
                      self.assertIsInstance(backbone.backbone_type     ,  lowercase_					)
                      # Verify num_features has been initialized in the backbone init
                      self.assertIsNotNone(backbone.num_features					)
                      self.assertTrue(len(backbone.channels					) == len(backbone.out_indices					)					)
                      self.assertTrue(len(backbone.stage_names					) == len(backbone.num_features					)					)
                      self.assertTrue(len(backbone.channels					) <= len(backbone.num_features					)					)
                      self.assertTrue(len(backbone.out_feature_channels					) == len(backbone.stage_names					)					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs_for_common()
               _snake_case      :							Any     				=						inputs_dict['pixel_values'].shape[0]
  
               for backbone_class in self.all_model_classes:
                      _snake_case      :							List[Any]     				=						backbone_class(lowercase_					)
                      backbone.to(lowercase_					)
                      backbone.eval()
  
                      _snake_case      :							Union[str, Any]     				=						backbone(**lowercase_					)
  
                      # Test default outputs and verify feature maps
                      self.assertIsInstance(outputs.feature_maps     ,  lowercase_					)
                      self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
                      for feature_map, n_channels in zip(outputs.feature_maps     ,  backbone.channels					):
                             self.assertTrue(feature_map.shape[:2]     ,  (batch_size, n_channels)					)
                      self.assertIsNone(outputs.hidden_states					)
                      self.assertIsNone(outputs.attentions					)
  
                      # Test output_hidden_states=True
                      _snake_case      :							List[str]     				=						backbone(**lowercase_     ,  output_hidden_states=lowercase_					)
                      self.assertIsNotNone(outputs.hidden_states					)
                      self.assertTrue(len(outputs.hidden_states					)     ,  len(backbone.stage_names					)					)
                      for hidden_state, n_channels in zip(outputs.hidden_states     ,  backbone.channels					):
                             self.assertTrue(hidden_state.shape[:2]     ,  (batch_size, n_channels)					)
  
                      # Test output_attentions=True
                      if self.has_attentions:
                             _snake_case      :							Union[str, Any]     				=						backbone(**lowercase_     ,  output_attentions=lowercase_					)
                             self.assertIsNotNone(outputs.attentions					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  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				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   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(lowercase_					)
               _snake_case      :							Union[str, Any]     				=						0
               _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
               _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
               if config is None:
                      _snake_case      :							List[Any]     				=						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=lowercase_     ,  **lowercase_     ,  )
               else:
                      _snake_case      :							PretrainedConfig     				=						config
  
               _snake_case      :							Optional[Any]     				=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
               for p in extra_model_params:
                      if getattr(self.hparams     ,  lowercase_     ,  lowercase_					):
                             assert hasattr(self.config     ,  lowercase_					), F'''model config doesn\'t have a `{p}` attribute'''
                             setattr(self.config     ,  lowercase_     ,  getattr(self.hparams     ,  lowercase_					)					)
  
               if tokenizer is None:
                      _snake_case      :							List[Any]     				=						AutoTokenizer.from_pretrained(
                          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=lowercase_     ,  )
               else:
                      _snake_case      :							PreTrainedTokenizer     				=						tokenizer
               _snake_case      :							str     				=						MODEL_MODES[mode]
               if model is None:
                      _snake_case      :							Optional[Any]     				=						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=lowercase_     ,  )
               else:
                      _snake_case      :							Optional[int]     				=						model
  
  
  
        def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						arg_to_scheduler[self.hparams.lr_scheduler]
               _snake_case      :							Optional[int]     				=						get_schedule_func(
                   self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()					)
               _snake_case      :							int     				=						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
               return scheduler
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model
               _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
               _snake_case      :							Optional[Any]     				=						[
                   {
                       '''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:
                      _snake_case      :							Optional[int]     				=						Adafactor(
                          lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)
  
               else:
                      _snake_case      :							Optional[Any]     				=						AdamW(
                          lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
               _snake_case      :							Optional[int]     				=						optimizer
  
               _snake_case      :							Tuple     				=						self.get_lr_scheduler()
  
               return [optimizer], [scheduler]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               return self.validation_step(lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               return self.validation_end(lowercase_					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
               _snake_case      :							Dict     				=						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     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               if stage == "test":
                      _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
               else:
                      _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                      _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               raise NotImplementedError('''You must implement this for your task'''					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               return self.train_loader
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return self.get_dataloader('''dev'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               return self.get_dataloader('''test'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return os.path.join(
                   self.hparams.data_dir     ,  '''cached_{}_{}_{}'''.format(
                       lowercase_     ,  list(filter(lowercase_     ,  self.hparams.model_name_or_path.split('''/'''					)					)					).pop()     ,  str(self.hparams.max_seq_length					)     ,  )     ,  )
  
  
  
        @pl.utilities.rank_zero_only
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
               _snake_case      :							str     				=						self.step_count
               self.model.save_pretrained(lowercase_					)
               self.tokenizer.save_pretrained(lowercase_					)
  
  
  
        @staticmethod
        def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               parser.add_argument(
                   '''--model_name_or_path'''     ,  default=lowercase_     ,  type=lowercase_     ,  required=lowercase_     ,  help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,  )
               parser.add_argument(
                   '''--config_name'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Pretrained config name or path if not the same as model_name'''					)
               parser.add_argument(
                   '''--tokenizer_name'''     ,  default=lowercase_     ,  type=lowercase_     ,  help='''Pretrained tokenizer name or path if not the same as model_name'''     ,  )
               parser.add_argument(
                   '''--cache_dir'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,  )
               parser.add_argument(
                   '''--encoder_layerdrop'''     ,  type=lowercase_     ,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--decoder_layerdrop'''     ,  type=lowercase_     ,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--dropout'''     ,  type=lowercase_     ,  help='''Dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--attention_dropout'''     ,  type=lowercase_     ,  help='''Attention dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument('''--learning_rate'''     ,  default=5E-5     ,  type=lowercase_     ,  help='''The initial learning rate for Adam.'''					)
               parser.add_argument(
                   '''--lr_scheduler'''     ,  default='''linear'''     ,  choices=lowercase_     ,  metavar=lowercase_     ,  type=lowercase_     ,  help='''Learning rate scheduler'''     ,  )
               parser.add_argument('''--weight_decay'''     ,  default=0.0     ,  type=lowercase_     ,  help='''Weight decay if we apply some.'''					)
               parser.add_argument('''--adam_epsilon'''     ,  default=1E-8     ,  type=lowercase_     ,  help='''Epsilon for Adam optimizer.'''					)
               parser.add_argument('''--warmup_steps'''     ,  default=0     ,  type=lowercase_     ,  help='''Linear warmup over warmup_steps.'''					)
               parser.add_argument('''--num_workers'''     ,  default=4     ,  type=lowercase_     ,  help='''kwarg passed to DataLoader'''					)
               parser.add_argument('''--num_train_epochs'''     ,  dest='''max_epochs'''     ,  default=3     ,  type=lowercase_					)
               parser.add_argument('''--train_batch_size'''     ,  default=32     ,  type=lowercase_					)
               parser.add_argument('''--eval_batch_size'''     ,  default=32     ,  type=lowercase_					)
               parser.add_argument('''--adafactor'''     ,  action='''store_true'''					)
  
  
  class 				A    (pl.Callback				):
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[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				):
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
               _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
               pl_module.logger.log_metrics(lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               rank_zero_info('''***** Validation results *****'''					)
               _snake_case      :							Tuple     				=						trainer.callback_metrics
               # Log results
               for key in sorted(lowercase_					):
                      if key not in ["log", "progress_bar"]:
                             rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               rank_zero_info('''***** Test results *****'''					)
               _snake_case      :							int     				=						trainer.callback_metrics
               # Log and save results to file
               _snake_case      :							Dict     				=						os.path.join(pl_module.hparams.output_dir     ,  '''test_results.txt'''					)
               with open(lowercase_     ,  '''w'''					) as writer:
                      for key in sorted(lowercase_					):
                             if key not in ["log", "progress_bar"]:
                                    rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
                                    writer.write('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						None:
         #  To allow all pl args uncomment the following line
         #  parser = pl.Trainer.add_argparse_args(parser)
         parser.add_argument(
             '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		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=lowercase_      ,		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=lowercase_  )
         parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		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=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
         parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
         parser.add_argument(
             '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
         pl.seed_everything(args.seed  )
  
         # init model
         _snake_case      :							int     				=						Path(model.hparams.output_dir  )
         odir.mkdir(exist_ok=lowercase_  )
  
         # add custom checkpoints
         if checkpoint_callback is None:
                _snake_case      :							Union[str, Any]     				=						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(lowercase_  )
         if logging_callback is None:
                _snake_case      :							Optional[int]     				=						LoggingCallback()
  
         _snake_case      :							Union[str, Any]     				=						{}
  
         # TODO: remove with PyTorch 1.6 since pl uses native amp
         if args.fpaa:
                _snake_case      :							Any     				=						16
                # train_params["amp_level"] = args.fp16_opt_level
  
         if args.gpus > 1:
                _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
                _snake_case      :							Dict     				=						'''ddp'''
  
         _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
         _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
         _snake_case      :							Tuple     				=						'''auto'''
  
         _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
             lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)
  
         if args.do_train:
                trainer.fit(lowercase_  )
  
         return trainer
  
  
  
  
  
  
  