


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


_snake_case   = logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_snake_case   = {
    "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
_snake_case   = {
    "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
}
_snake_case   = sorted(arg_to_scheduler.keys())
_snake_case   = "{" + ", ".join(arg_to_scheduler_choices) + "}"




class    A_   ( pl.LightningModule     ):







 """simple docstring"""





 def __init__(			self					: str       ,    _a					: argparse.Namespace       ,    _a					: str=None       ,    _a					: List[Any]="base"       ,    _a					: Dict=None       ,    _a					: Union[str, Any]=None       ,    _a					: Optional[Any]=None       ,    **_a					: List[Any]       ,    )    ->			int:
      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						:				Dict		  =			0
      UpperCAmelCase						:				str		  =			Path(self.hparams.output_dir				)
      UpperCAmelCase						:				Dict		  =			self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           UpperCAmelCase						:				Optional[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=_a       ,    **_a       ,    )
      else:
           UpperCAmelCase						:				PretrainedConfig		  =			config

      UpperCAmelCase						:				int		  =			("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						:				int		  =			AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,    cache_dir=_a       ,    )
      else:
           UpperCAmelCase						:				PreTrainedTokenizer		  =			tokenizer
      UpperCAmelCase						:				List[Any]		  =			MODEL_MODES[mode]
      if model is None:
           UpperCAmelCase						:				int		  =			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						:				List[str]		  =			model
 def   _UpperCamelCase    (			self					: List[Any]       ,    *_a					: List[Any]       ,    **_a					: str				)    ->			Tuple:
      UpperCAmelCase						:				List[str]		  =			self.model_type.from_pretrained(*_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Tuple:
      UpperCAmelCase						:				Any		  =			arg_to_scheduler[self.hparams.lr_scheduler]
      UpperCAmelCase						:				Union[str, Any]		  =			get_schedule_func(
          self.opt       ,    num_warmup_steps=self.hparams.warmup_steps       ,    num_training_steps=self.total_steps()				)
      UpperCAmelCase						:				Optional[int]		  =			{"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler
 def   _UpperCamelCase    (			self					: str				)    ->			Union[str, Any]:
      UpperCAmelCase						:				Dict		  =			self.model
      UpperCAmelCase						:				List[str]		  =			["bias", "LayerNorm.weight"]
      UpperCAmelCase						:				List[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:
           UpperCAmelCase						:				Any		  =			Adafactor(
               _a       ,    lr=self.hparams.learning_rate       ,    scale_parameter=_a       ,    relative_step=_a				)

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

      UpperCAmelCase						:				Optional[int]		  =			self.get_lr_scheduler()

      return [optimizer], [scheduler]
 def   _UpperCamelCase    (			self					: int       ,    _a					: Dict       ,    _a					: Any				)    ->			Any:
      return self.validation_step(_a       ,    _a				)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: List[str]				)    ->			Tuple:
      return self.validation_end(_a				)
 def   _UpperCamelCase    (			self					: Tuple				)    ->			int:
      UpperCAmelCase						:				List[Any]		  =			max(1       ,    self.hparams.gpus				)  # TODO: consider num_tpu_cores
      UpperCAmelCase						:				List[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   _UpperCamelCase    (			self					: str       ,    _a					: Tuple				)    ->			List[str]:
      if mode == "test":
           UpperCAmelCase						:				Optional[int]		  =			len(self.test_dataloader().dataset				)
      else:
           UpperCAmelCase						:				str		  =			self.get_dataloader("train"       ,    self.hparams.train_batch_size       ,    shuffle=_a				)
           UpperCAmelCase						:				str		  =			len(self.train_dataloader().dataset				)
 def   _UpperCamelCase    (			self					: int       ,    _a					: str       ,    _a					: int       ,    _a					: bool = False				)    ->			Optional[Any]:
      raise NotImplementedError("You must implement this for your task"				)
 def   _UpperCamelCase    (			self					: int				)    ->			str:
      return self.train_loader
 def   _UpperCamelCase    (			self					: Dict				)    ->			Optional[int]:
      return self.get_dataloader("dev"       ,    self.hparams.eval_batch_size       ,    shuffle=_a				)
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			str:
      return self.get_dataloader("test"       ,    self.hparams.eval_batch_size       ,    shuffle=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Any				)    ->			str:
      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					: Any       ,    _a					: Dict[str, Any]				)    ->			None:
      UpperCAmelCase						:				Any		  =			self.output_dir.joinpath("best_tfmr"				)
      UpperCAmelCase						:				List[Any]		  =			self.step_count
      self.model.save_pretrained(_a				)
      self.tokenizer.save_pretrained(_a				)

 @staticmethod
 def   _UpperCamelCase    (			_a					: Dict       ,    _a					: Tuple				)    ->			int:
      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					: Union[str, Any]       ,    _a					: int       ,    _a					: Union[str, Any]				)    ->			Dict:
      UpperCAmelCase						:				Any		  =			trainer.lr_schedulers[0]["scheduler"]
      UpperCAmelCase						:				Dict		  =			{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
      pl_module.logger.log_metrics(_a				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: pl.Trainer       ,    _a					: pl.LightningModule				)    ->			Optional[int]:
      rank_zero_info("***** Validation results *****"				)
      UpperCAmelCase						:				Any		  =			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					: List[str]       ,    _a					: pl.Trainer       ,    _a					: pl.LightningModule				)    ->			Optional[int]:
      rank_zero_info("***** Test results *****"				)
      UpperCAmelCase						:				int		  =			trainer.callback_metrics
      # Log and save results to file
      UpperCAmelCase						:				Optional[Any]		  =			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   (		A__		,		A__ ):
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"		,		default=A__		,		type=A__		,		required=A__		,		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=A__		,		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=A__ )
     parser.add_argument("--max_grad_norm"		,		dest="gradient_clip_val"		,		default=1.0		,		type=A__		,		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=A__		,		default=1		,		help="Number of updates steps to accumulate before performing a backward/update pass."		,		)
     parser.add_argument("--seed"		,		type=A__		,		default=4_2		,		help="random seed for initialization" )
     parser.add_argument(
         "--data_dir"		,		default=A__		,		type=A__		,		required=A__		,		help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,		)







def   A   (		A__		,		A__		,		A__=None		,		A__=True		,		A__=[]		,		A__=None		,		A__=None		,		**A__		,		):
     pl.seed_everything(args.seed )

     # init model
     UpperCAmelCase						:				Optional[int]		  =			Path(model.hparams.output_dir )
     odir.mkdir(exist_ok=A__ )

     # add custom checkpoints
     if checkpoint_callback is None:
          UpperCAmelCase						:				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(A__ )
     if logging_callback is None:
          UpperCAmelCase						:				Optional[int]		  =			LoggingCallback()

     UpperCAmelCase						:				int		  =			{}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          UpperCAmelCase						:				List[str]		  =			1_6
          UpperCAmelCase						:				int		  =			args.fpaa_opt_level

     if args.gpus > 1:
          UpperCAmelCase						:				Optional[Any]		  =			"ddp"

     UpperCAmelCase						:				Any		  =			args.accumulate_grad_batches
     UpperCAmelCase						:				int		  =			extra_train_kwargs.get("accelerator"		,		A__ )
     UpperCAmelCase						:				Optional[Any]		  =			extra_train_kwargs.get("profiler"		,		A__ )

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

     if args.do_train:
          trainer.fit(A__ )

     return trainer







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_snake_case   = TypeVar("T")
_snake_case   = TypeVar("U")




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: List[Any]       ,    _a					: T | None       ,    _a					: U | None				)    ->			Dict:
      UpperCAmelCase						:				str		  =			key
      UpperCAmelCase						:				Dict		  =			val
      UpperCAmelCase						:				int		  =			0
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None

 def __repr__(			self					: List[str]				)    ->			str:
      return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
          self.key       ,    self.val       ,    self.freq       ,    self.next is not None       ,    self.prev is not None				)




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: Any				)    ->			None:
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			self.rear, self.head
 def __repr__(			self					: int				)    ->			str:
      UpperCAmelCase						:				Union[str, Any]		  =			["DoubleLinkedList"]
      UpperCAmelCase						:				int		  =			self.head
      while node.next is not None:
           rep.append(str(_a				)				)
           UpperCAmelCase						:				Union[str, Any]		  =			node.next
      rep.append(str(self.rear				)				)
      return ",\n    ".join(_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:
      UpperCAmelCase						:				str		  =			self.rear.prev

      # All nodes other than self.head are guaranteed to have non-None previous
      assert previous is not None

      UpperCAmelCase						:				Any		  =			node
      UpperCAmelCase						:				Optional[int]		  =			previous
      UpperCAmelCase						:				Optional[int]		  =			node
      UpperCAmelCase						:				Union[str, Any]		  =			self.rear
      node.freq += 1
      self._position_node(_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:

      while node.prev is not None and node.prev.freq > node.freq:
           # swap node with previous node
           UpperCAmelCase						:				List[str]		  =			node.prev

           UpperCAmelCase						:				Any		  =			previous_node.prev
           UpperCAmelCase						:				int		  =			node.prev
           UpperCAmelCase						:				Tuple		  =			previous_node
           UpperCAmelCase						:				Optional[int]		  =			node

 def   _UpperCamelCase    (			self					: str       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			DoubleLinkedListNode[T, U] | None:

      if node.prev is None or node.next is None:
           return None

      UpperCAmelCase						:				Dict		  =			node.next
      UpperCAmelCase						:				Optional[Any]		  =			node.prev
      UpperCAmelCase						:				List[Any]		  =			None
      UpperCAmelCase						:				int		  =			None
      return node




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 _UpperCamelCase				: dict[Callable[[T], U], LFUCache[T, U]]      =					{}
 def __init__(			self					: List[Any]       ,    _a					: int				)    ->			int:
      UpperCAmelCase						:				DoubleLinkedList[T, U]		  =			DoubleLinkedList()
      UpperCAmelCase						:				Any		  =			capacity
      UpperCAmelCase						:				Union[str, Any]		  =			0
      UpperCAmelCase						:				Any		  =			0
      UpperCAmelCase						:				Dict		  =			0
      UpperCAmelCase						:				dict[T, DoubleLinkedListNode[T, U]]		  =			{}
 def __repr__(			self					: Union[str, Any]				)    ->			str:
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )
 def __contains__(			self					: Optional[int]       ,    _a					: T				)    ->			bool:
      return key in self.cache
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: T				)    ->			U | None:

      if key in self.cache:
           self.hits += 1
           UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			self.cache[key]
           UpperCAmelCase						:				Optional[int]		  =			self.list.remove(self.cache[key]				)
           assert node == value_node

           # node is guaranteed not None because it is in self.cache
           assert node is not None
           self.list.add(_a				)
           return node.val
      self.miss += 1
      return None
 def   _UpperCamelCase    (			self					: str       ,    _a					: T       ,    _a					: U				)    ->			None:

      if key not in self.cache:
           if self.num_keys >= self.capacity:
                # delete first node when over capacity
                UpperCAmelCase						:				Any		  =			self.list.head.next

                # guaranteed to have a non-None first node when num_keys > 0
                # explain to type checker via assertions
                assert first_node is not None
                assert first_node.key is not None
                assert self.list.remove(_a				) is not None
                # first_node guaranteed to be in list

                del self.cache[first_node.key]
                self.num_keys -= 1
           UpperCAmelCase						:				Optional[Any]		  =			DoubleLinkedListNode(_a       ,    _a				)
           self.list.add(self.cache[key]				)
           self.num_keys += 1

      else:
           UpperCAmelCase						:				Any		  =			self.list.remove(self.cache[key]				)
           assert node is not None  # node guaranteed to be in list
           UpperCAmelCase						:				str		  =			value
           self.list.add(_a				)

 @classmethod
 def   _UpperCamelCase    (			cls					: type[LFUCache[T, U]]       ,    _a					: int = 1_2_8				)    ->			Callable[[Callable[[T], U]], Callable[..., U]]:

      def cache_decorator_inner(_a					: Callable[[T], U]				) -> Callable[..., U]:
           def cache_decorator_wrapper(*_a					: T				) -> U:
                if func not in cls.decorator_function_to_instance_map:
                     UpperCAmelCase						:				List[Any]		  =			LFUCache(_a				)

                UpperCAmelCase						:				List[Any]		  =			cls.decorator_function_to_instance_map[func].get(args[0]				)
                if result is None:
                     UpperCAmelCase						:				List[Any]		  =			func(*_a				)
                     cls.decorator_function_to_instance_map[func].put(args[0]       ,    _a				)
                return result

           def cache_info() -> LFUCache[T, U]:
                return cls.decorator_function_to_instance_map[func]

           setattr(_a       ,    "cache_info"       ,    _a				)  # noqa: B010

           return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
      import doctest

      doctest.testmod()



