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







class 			__A					(			pl.LightningModule      ):
 def __init__( self      :     int					, lowerCamelCase      :     argparse.Namespace					, lowerCamelCase      :     List[Any]=None					, lowerCamelCase      :     Any="base"					, lowerCamelCase      :     int=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     List[str]					, ):



   """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__							)
   __A		:  List[str]       				=		0
   __A		:  str       				=		Path(self.hparams.output_dir							)
   __A		:  Optional[int]       				=		self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
     __A		:  Optional[int]       				=		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:
     __A		:  List[str]       				=		config

   __A		:  List[str]       				=		("""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:
     __A		:  Any       				=		AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					, cache_dir=lowercase__					, )
   else:
     __A		:  Optional[Any]       				=		tokenizer
   __A		:  str       				=		MODEL_MODES[mode]
   if model is None:
     __A		:  Tuple       				=		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:
     __A		:  Optional[Any]       				=		model
 def 		lowercase_( self      :     List[str]					, *lowerCamelCase      :     Union[str, Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Optional[int]       				=		self.model_type.from_pretrained(*lowercase__					, **lowercase__							)
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  List[str]       				=		arg_to_scheduler[self.hparams.lr_scheduler]
   __A		:  Union[str, Any]       				=		get_schedule_func(
       self.opt					, num_warmup_steps=self.hparams.warmup_steps					, num_training_steps=self.total_steps()							)
   __A		:  List[Any]       				=		{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
   return scheduler
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		self.model
   __A		:  Optional[Any]       				=		["""bias""", """LayerNorm.weight"""]
   __A		:  Dict       				=		[
       {
           """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:
     __A		:  str       				=		Adafactor(
         lowercase__					, lr=self.hparams.learning_rate					, scale_parameter=lowercase__					, relative_step=lowercase__							)

   else:
     __A		:  Any       				=		AdamW(
         lowercase__					, lr=self.hparams.learning_rate					, eps=self.hparams.adam_epsilon							)
   __A		:  Optional[Any]       				=		optimizer

   __A		:  str       				=		self.get_lr_scheduler()

   return [optimizer], [scheduler]
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     int					, lowerCamelCase      :     List[Any]							):



   """simple docstring"""





   return self.validation_step(lowercase__					, lowercase__							)
 def 		lowercase_( self      :     str					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   return self.validation_end(lowercase__							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  str       				=		max(1					, self.hparams.gpus							)  # TODO: consider num_tpu_cores
   __A		:  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 		lowercase_( self      :     Tuple					, lowerCamelCase      :     int							):



   """simple docstring"""





   if mode == "test":
     __A		:  List[str]       				=		len(self.test_dataloader().dataset							)
   else:
     __A		:  Any       				=		self.get_dataloader("""train"""					, self.hparams.train_batch_size					, shuffle=lowercase__							)
     __A		:  int       				=		len(self.train_dataloader().dataset							)
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     str					, lowerCamelCase      :     int					, lowerCamelCase      :     bool = False							):



   """simple docstring"""





   raise NotImplementedError("""You must implement this for your task"""							)
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   return self.train_loader
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   return self.get_dataloader("""dev"""					, self.hparams.eval_batch_size					, shuffle=lowercase__							)
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   return self.get_dataloader("""test"""					, self.hparams.eval_batch_size					, shuffle=lowercase__							)
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     Tuple							):



   """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 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     Dict[str, Any]							):



   """simple docstring"""





   __A		:  str       				=		self.output_dir.joinpath("""best_tfmr"""							)
   __A		:  List[Any]       				=		self.step_count
   self.model.save_pretrained(lowercase__							)
   self.tokenizer.save_pretrained(lowercase__							)






 @staticmethod
 def 		lowercase_( lowerCamelCase      :     Dict					, lowerCamelCase      :     Any							):



   """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 		lowercase_( self      :     List[str]					, lowerCamelCase      :     int					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A		:  List[Any]       				=		trainer.lr_schedulers[0]["""scheduler"""]
   __A		:  Union[str, Any]       				=		{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
   pl_module.logger.log_metrics(lowercase__							)
 def 		lowercase_( self      :     int					, lowerCamelCase      :     pl.Trainer					, lowerCamelCase      :     pl.LightningModule							):



   """simple docstring"""





   rank_zero_info("""***** Validation results *****"""							)
   __A		:  Any       				=		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 		lowercase_( self      :     Dict					, lowerCamelCase      :     pl.Trainer					, lowerCamelCase      :     pl.LightningModule							):



   """simple docstring"""





   rank_zero_info("""***** Test results *****"""							)
   __A		:  List[str]       				=		trainer.callback_metrics
   # Log and save results to file
   __A		:  List[Any]       				=		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_  (    __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Dict:





  """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 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	str=None      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]=True      ,  __SCREAMING_SNAKE_CASE    :	int=[]      ,  __SCREAMING_SNAKE_CASE    :	Tuple=None      ,  __SCREAMING_SNAKE_CASE    :	Tuple=None      ,  **__SCREAMING_SNAKE_CASE    :	Dict      ,  )					->							Any:





  """simple docstring"""

  pl.seed_everything(args.seed			)

  # init model
  __A		:  int       				=		Path(model.hparams.output_dir			)
  odir.mkdir(exist_ok=_UpperCAmelCase			)

  # add custom checkpoints
  if checkpoint_callback is None:
    __A		:  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:
    __A		:  Union[str, Any]       				=		LoggingCallback()

  __A		:  Optional[int]       				=		{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    __A		:  int       				=		16
    __A		:  Optional[Any]       				=		args.fpaa_opt_level

  if args.gpus > 1:
    __A		:  List[str]       				=		"""ddp"""

  __A		:  Optional[Any]       				=		args.accumulate_grad_batches
  __A		:  List[Any]       				=		extra_train_kwargs.get("""accelerator"""      ,  _UpperCAmelCase			)
  __A		:  Optional[int]       				=		extra_train_kwargs.get("""profiler"""      ,  _UpperCAmelCase			)

  __A		:  Optional[Any]       				=		pl.Trainer.from_argparse_args(
      _UpperCAmelCase      ,  weights_summary=_UpperCAmelCase      ,  callbacks=[logging_callback] + extra_callbacks      ,  logger=_UpperCAmelCase      ,  checkpoint_callback=_UpperCAmelCase      ,  **_UpperCAmelCase      ,  )

  if args.do_train:
    trainer.fit(_UpperCAmelCase			)

  return trainer








import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :Any    ):
										return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.rust_tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Any    ):
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]






