

'''simple docstring'''




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


_a			:	List[str]								=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_a			:	Dict								=  {
    '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
_a			:	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
}
_a			:	Any								=  sorted(arg_to_scheduler.keys())
_a			:	Dict								=  '{' + ', '.join(arg_to_scheduler_choices) + '}'



class     lowercase_		(						pl.LightningModule	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=None				,	a_="base"				,	a_=None				,	a_=None				,	a_=None				,	**a_				,	)							->			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_       )
    UpperCAmelCase  							=		0
    UpperCAmelCase  							=		Path(self.hparams.output_dir       )
    UpperCAmelCase  							=		self.hparams.cache_dir if self.hparams.cache_dir else None
    if config is None:
      UpperCAmelCase  							=		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:
      UpperCAmelCase  							=		config

    UpperCAmelCase  							=		('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:
      UpperCAmelCase  							=		AutoTokenizer.from_pretrained(
          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,	cache_dir=a_				,	)
    else:
      UpperCAmelCase  							=		tokenizer
    UpperCAmelCase  							=		MODEL_MODES[mode]
    if model is None:
      UpperCAmelCase  							=		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:
      UpperCAmelCase  							=		model
  def   snake_case_     (       self				,	*a_				,	**a_       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_type.from_pretrained(*a_				,	**a_       )
  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		arg_to_scheduler[self.hparams.lr_scheduler]
    UpperCAmelCase  							=		get_schedule_func(
        self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps()       )
    UpperCAmelCase  							=		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
    return scheduler
  def   snake_case_     (       self       )							->			Optional[int]:


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

    else:
      UpperCAmelCase  							=		AdamW(
          a_				,	lr=self.hparams.learning_rate				,	eps=self.hparams.adam_epsilon       )
    UpperCAmelCase  							=		optimizer

    UpperCAmelCase  							=		self.get_lr_scheduler()

    return [optimizer], [scheduler]
  def   snake_case_     (       self				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    return self.validation_step(a_				,	a_       )
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    return self.validation_end(a_       )
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		max(1				,	self.hparams.gpus       )  # TODO: consider num_tpu_cores
    UpperCAmelCase  							=		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
    return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    if stage == "test":
      UpperCAmelCase  							=		len(self.test_dataloader().dataset       )
    else:
      UpperCAmelCase  							=		self.get_dataloader('train'				,	self.hparams.train_batch_size				,	shuffle=a_       )
      UpperCAmelCase  							=		len(self.train_dataloader().dataset       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = False       )							->			str:


    """simple docstring"""
    raise NotImplementedError('You must implement this for your task'       )
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    return self.train_loader
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    return self.get_dataloader('dev'				,	self.hparams.eval_batch_size				,	shuffle=a_       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return self.get_dataloader('test'				,	self.hparams.eval_batch_size				,	shuffle=a_       )
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """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   snake_case_     (       self				,	a_       )							->			None:


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





  @staticmethod
  def   snake_case_     (       a_				,	a_       )							->			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     lowercase_		(						pl.Callback	):

  '''simple docstring'''





  def   snake_case_     (       self				,	a_				,	a_       )							->			List[str]:


    """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     lowercase_		(						pl.Callback	):

  '''simple docstring'''





  def   snake_case_     (       self				,	a_				,	a_       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		trainer.lr_schedulers[0]['scheduler']
    UpperCAmelCase  							=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
    pl_module.logger.log_metrics(a_       )
  def   snake_case_     (       self				,	a_				,	a_       )							->			int:


    """simple docstring"""
    rank_zero_info('***** Validation results *****'       )
    UpperCAmelCase  							=		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   snake_case_     (       self				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    rank_zero_info('***** Test results *****'       )
    UpperCAmelCase  							=		trainer.callback_metrics
    # Log and save results to file
    UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				List[Any]  ):
  #  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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				BaseTransformer    ,				SCREAMING_SNAKE_CASE :				argparse.Namespace    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=None    ,				SCREAMING_SNAKE_CASE :				int=True    ,				SCREAMING_SNAKE_CASE :				Dict=None    ,				SCREAMING_SNAKE_CASE :				Any=[]    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				**SCREAMING_SNAKE_CASE :				int    ,				):
  pl.seed_everything(args.seed  )

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

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

  UpperCAmelCase  							=		{}

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

  if args.gpus > 1:
    UpperCAmelCase  							=		'auto'  # "ddp"
    UpperCAmelCase  							=		'ddp'

  UpperCAmelCase  							=		args.accumulate_grad_batches
  UpperCAmelCase  							=		None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  UpperCAmelCase  							=		'auto'

  UpperCAmelCase  							=		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





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


