




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__						:       Dict							=    logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase__						:       List[Any]							=    {
    '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__						:       Optional[int]							=    {
    '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__						:       str							=    sorted(arg_to_scheduler.keys())
UpperCAmelCase__						:       Union[str, Any]							=    '{' + ', '.join(arg_to_scheduler_choices) + '}'

class      UpperCAmelCase						(			pl.LightningModule   ):






   def __init__(	self   :       Any						,					UpperCamelCase_   :       argparse.Namespace						,					UpperCamelCase_   :       Dict=None						,					UpperCamelCase_   :       List[Any]="base"						,					UpperCamelCase_   :       List[Any]=None						,					UpperCamelCase_   :       Optional[int]=None						,					UpperCamelCase_   :       Union[str, Any]=None						,					**UpperCamelCase_   :       Optional[int]						,					)->			Any:
     super().__init__()
     # TODO: move to self.save_hyperparameters()
     # self.save_hyperparameters()
     # can also expand arguments into trainer signature for easier reading

     self.save_hyperparameters(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    0
     _lowercase: Any						     =    Path(self.hparams.output_dir     )
     _lowercase: Tuple						     =    self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
       _lowercase: 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=UpperCamelCase_						,					**UpperCamelCase_						,					)
     else:
       _lowercase: PretrainedConfig						     =    config

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

     if tokenizer is None:
       _lowercase: List[Any]						     =    AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,					cache_dir=UpperCamelCase_						,					)
     else:
       _lowercase: PreTrainedTokenizer						     =    tokenizer
     _lowercase: Dict						     =    MODEL_MODES[mode]
     if model is None:
       _lowercase: 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=UpperCamelCase_						,					)
     else:
       _lowercase: int						     =    model






   def  _a						(	self   :       int						,					*UpperCamelCase_   :       Union[str, Any]						,					**UpperCamelCase_   :       Tuple     )->			Union[str, Any]:
     _lowercase: Optional[Any]						     =    self.model_type.from_pretrained(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       str     )->			List[Any]:
     _lowercase: str						     =    arg_to_scheduler[self.hparams.lr_scheduler]
     _lowercase: Any						     =    get_schedule_func(
         self.opt						,					num_warmup_steps=self.hparams.warmup_steps						,					num_training_steps=self.total_steps()     )
     _lowercase: Optional[int]						     =    {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
     return scheduler






   def  _a						(	self   :       str     )->			List[Any]:
     _lowercase: List[Any]						     =    self.model
     _lowercase: Tuple						     =    ['bias', 'LayerNorm.weight']
     _lowercase: Union[str, 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:
       _lowercase: Union[str, Any]						     =    Adafactor(
           UpperCamelCase_						,					lr=self.hparams.learning_rate						,					scale_parameter=UpperCamelCase_						,					relative_step=UpperCamelCase_     )

     else:
       _lowercase: Dict						     =    AdamW(
           UpperCamelCase_						,					lr=self.hparams.learning_rate						,					eps=self.hparams.adam_epsilon     )
     _lowercase: Union[str, Any]						     =    optimizer

     _lowercase: List[Any]						     =    self.get_lr_scheduler()

     return [optimizer], [scheduler]






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       List[str]     )->			Any:
     return self.validation_step(UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       str						,					UpperCamelCase_   :       Dict     )->			int:
     return self.validation_end(UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			int:
     _lowercase: Dict						     =    max(1						,					self.hparams.gpus     )  # TODO: consider num_tpu_cores
     _lowercase: Union[str, Any]						     =    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   :       Tuple						,					UpperCamelCase_   :       Any     )->			Optional[Any]:
     if stage == "test":
       _lowercase: Tuple						     =    len(self.test_dataloader().dataset     )
     else:
       _lowercase: Tuple						     =    self.get_dataloader('train'						,					self.hparams.train_batch_size						,					shuffle=UpperCamelCase_     )
       _lowercase: str						     =    len(self.train_dataloader().dataset     )






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       bool = False     )->			Union[str, Any]:
     raise NotImplementedError('You must implement this for your task'     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     return self.train_loader






   def  _a						(	self   :       Optional[Any]     )->			Optional[Any]:
     return self.get_dataloader('dev'						,					self.hparams.eval_batch_size						,					shuffle=UpperCamelCase_     )






   def  _a						(	self   :       Tuple     )->			Any:
     return self.get_dataloader('test'						,					self.hparams.eval_batch_size						,					shuffle=UpperCamelCase_     )






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       List[str]     )->			Optional[int]:
     return os.path.join(
         self.hparams.data_dir						,					'cached_{}_{}_{}'.format(
             UpperCamelCase_						,					list(filter(UpperCamelCase_						,					self.hparams.model_name_or_path.split('/'     )     )     ).pop()						,					str(self.hparams.max_seq_length     )						,					)						,					)






   @pl.utilities.rank_zero_only
   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       Dict[str, Any]     )->			None:
     _lowercase: List[str]						     =    self.output_dir.joinpath('best_tfmr'     )
     _lowercase: Optional[Any]						     =    self.step_count
     self.model.save_pretrained(UpperCamelCase_     )
     self.tokenizer.save_pretrained(UpperCamelCase_     )






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

class      UpperCAmelCase						(			pl.Callback   ):






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Optional[int]     )->			Union[str, Any]:
     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   ):






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Optional[Any]     )->			Tuple:
     _lowercase: Optional[Any]						     =    trainer.lr_schedulers[0]['scheduler']
     _lowercase: str						     =    {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
     pl_module.logger.log_metrics(UpperCamelCase_     )






   def  _a						(	self   :       Optional[int]						,					UpperCamelCase_   :       pl.Trainer						,					UpperCamelCase_   :       pl.LightningModule     )->			Optional[Any]:
     rank_zero_info('***** Validation results *****'     )
     _lowercase: List[str]						     =    trainer.callback_metrics
     # Log results
     for key in sorted(UpperCamelCase_     ):
       if key not in ["log", "progress_bar"]:
         rank_zero_info('{} = {}\n'.format(UpperCamelCase_						,					str(metrics[key]     )     )     )






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


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		None:





  """simple docstring"""





  parser.add_argument(
      '--output_dir'					,     default=UpperCamelCase					,     type=UpperCamelCase					,     required=UpperCamelCase					,     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=UpperCamelCase					,     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=UpperCamelCase							)
  parser.add_argument('--max_grad_norm'					,     dest='gradient_clip_val'					,     default=1.0					,     type=UpperCamelCase					,     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=UpperCamelCase					,     default=1					,     help='Number of updates steps to accumulate before performing a backward/update pass.'					,     )
  parser.add_argument('--seed'					,     type=UpperCamelCase					,     default=42					,     help='random seed for initialization'							)
  parser.add_argument(
      '--data_dir'					,     default=UpperCamelCase					,     type=UpperCamelCase					,     required=UpperCamelCase					,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					,     )





def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=None					,     UpperCamelCase=True					,     UpperCamelCase=None					,     UpperCamelCase=[]					,     UpperCamelCase=None					,     UpperCamelCase=None					,     **UpperCamelCase					,     )			->		Tuple:





  """simple docstring"""





  pl.seed_everything(args.seed							)

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

  # add custom checkpoints
  if checkpoint_callback is None:
    _lowercase: List[str]						     =    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(UpperCamelCase							)
  if logging_callback is None:
    _lowercase: Any						     =    LoggingCallback()

  _lowercase: Optional[Any]						     =    {}

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

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

  _lowercase: str						     =    args.accumulate_grad_batches
  _lowercase: Optional[Any]						     =    None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  _lowercase: int						     =    'auto'

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

  if args.do_train:
    trainer.fit(UpperCamelCase							)

  return trainer






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
