






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 	a_(		pl.LightningModule	):



     """simple docstring"""



     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	argparse.Namespace  ,  lowerCAmelCase__					:	Optional[int]=None  ,  lowerCAmelCase__					:	List[Any]="base"  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[Any]=None  ,  lowerCAmelCase__					:	Optional[Any]=None  ,  **lowerCAmelCase__					:	Optional[int]  ,  )							->	Optional[Any]:

      """simple docstring"""
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(_A)
      SCREAMING_SNAKE_CASE				    =		0
      SCREAMING_SNAKE_CASE				    =		Path(self.hparams.output_dir)
      SCREAMING_SNAKE_CASE				    =		self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
       SCREAMING_SNAKE_CASE				    =		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:
       SCREAMING_SNAKE_CASE				    =		config

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





     def     __UpperCamelCase						(		self					:	str  ,  *lowerCAmelCase__					:	str  ,  **lowerCAmelCase__					:	Dict)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model_type.from_pretrained(*_A  ,  **_A)





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[Any]:

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





     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model
      SCREAMING_SNAKE_CASE				    =		['bias', 'LayerNorm.weight']
      SCREAMING_SNAKE_CASE				    =		[
          {
              '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:
       SCREAMING_SNAKE_CASE				    =		Adafactor(
           _A  ,  lr=self.hparams.learning_rate  ,  scale_parameter=_A  ,  relative_step=_A)

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

      SCREAMING_SNAKE_CASE				    =		self.get_lr_scheduler()

      return [optimizer], [scheduler]





     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	str)							->	Union[str, Any]:

      """simple docstring"""
      return self.validation_step(_A  ,  _A)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[int])							->	List[Any]:

      """simple docstring"""
      return self.validation_end(_A)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		max(1  ,  self.hparams.gpus)  # TODO: consider num_tpu_cores
      SCREAMING_SNAKE_CASE				    =		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					:	int  ,  lowerCAmelCase__					:	Optional[int])							->	Optional[int]:

      """simple docstring"""



      if mode == "test":
       SCREAMING_SNAKE_CASE				    =		len(self.test_dataloader().dataset)
      else:
       SCREAMING_SNAKE_CASE				    =		self.get_dataloader('train'  ,  self.hparams.train_batch_size  ,  shuffle=_A)
       SCREAMING_SNAKE_CASE				    =		len(self.train_dataloader().dataset)





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	bool = False)							->	Union[str, Any]:

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





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      return self.train_loader





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Union[str, Any]:

      """simple docstring"""
      return self.get_dataloader('dev'  ,  self.hparams.eval_batch_size  ,  shuffle=_A)





     def     __UpperCamelCase						(		self					:	List[str])							->	Union[str, Any]:

      """simple docstring"""
      return self.get_dataloader('test'  ,  self.hparams.eval_batch_size  ,  shuffle=_A)





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Union[str, Any])							->	List[Any]:

      """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     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	Dict[str, Any])							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.output_dir.joinpath('best_tfmr')
      SCREAMING_SNAKE_CASE				    =		self.step_count
      self.model.save_pretrained(_A)
      self.tokenizer.save_pretrained(_A)





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Union[str, Any])							->	Optional[int]:

      """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=''  ,  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=3_2  ,  type=_A)
      parser.add_argument('--eval_batch_size'  ,  default=3_2  ,  type=_A)
      parser.add_argument('--adafactor'  ,  action='store_true')

class 	a_(		pl.Callback	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Dict)							->	int:

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





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule)							->	str:

      """simple docstring"""
      rank_zero_info('***** Validation results *****')
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule)							->	str:

      """simple docstring"""
      rank_zero_info('***** Test results *****')
      SCREAMING_SNAKE_CASE				    =		trainer.callback_metrics
      # Log and save results to file
      SCREAMING_SNAKE_CASE				    =		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 						A_					(						lowercase_	,  lowercase_			)				->None:







 """simple docstring"""

 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=4_2	,  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_=[]	,  lowercase_=None	,  lowercase_=None	,  **lowercase_	,  )				->Optional[Any]:







 """simple docstring"""

 pl.seed_everything(args.seed			)

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

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

 SCREAMING_SNAKE_CASE				    =		{}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  SCREAMING_SNAKE_CASE				    =		1_6
  SCREAMING_SNAKE_CASE				    =		args.fpaa_opt_level

 if args.gpus > 1:
  SCREAMING_SNAKE_CASE				    =		'ddp'

 SCREAMING_SNAKE_CASE				    =		args.accumulate_grad_batches
 SCREAMING_SNAKE_CASE				    =		extra_train_kwargs.get('accelerator'	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		extra_train_kwargs.get('profiler'	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		pl.Trainer.from_argparse_args(
     lowercase_	,  weights_summary=lowercase_	,  callbacks=[logging_callback] + extra_callbacks	,  logger=lowercase_	,  checkpoint_callback=lowercase_	,  **lowercase_	,  )

 if args.do_train:
  trainer.fit(lowercase_			)

 return trainer






'''simple docstring'''





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     AudioLDMPipeline,
 )
else:
 from .pipeline_audioldm import AudioLDMPipeline






