





"""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


lowerCamelCase__      =      logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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



class    __SCREAMING_SNAKE_CASE			(  pl.LightningModule      ):







  '''simple docstring'''


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

        _UpperCamelCase					:		Tuple  =				("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					:		Optional[Any]  =				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					:		Any  =				MODEL_MODES[mode]
        if model is None:
              _UpperCamelCase					:		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=__a    ,      )
        else:
              _UpperCamelCase					:		Union[str, Any]  =				model

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      *__a      :				List[Any]    ,      **__a      :				Dict		)						-> Any:
        _UpperCamelCase					:		Any  =				self.model_type.from_pretrained(*__a    ,      **__a		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> List[Any]:
        _UpperCamelCase					:		Dict  =				self.model
        _UpperCamelCase					:		Optional[Any]  =				["bias", "LayerNorm.weight"]
        _UpperCamelCase					:		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:
              _UpperCamelCase					:		List[str]  =				Adafactor(
                  __a    ,      lr=self.hparams.learning_rate    ,      scale_parameter=__a    ,      relative_step=__a		)

        else:
              _UpperCamelCase					:		int  =				AdamW(
                  __a    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon		)
        _UpperCamelCase					:		int  =				optimizer

        _UpperCamelCase					:		Optional[Any]  =				self.get_lr_scheduler()

        return [optimizer], [scheduler]

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				str    ,      __a      :				Dict		)						-> str:
        return self.validation_step(__a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Any		)						-> int:
        return self.validation_end(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> int:
        _UpperCamelCase					:		str  =				max(1    ,      self.hparams.gpus		)  # TODO: consider num_tpu_cores
        _UpperCamelCase					:		Dict  =				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				str		)						-> str:
        if mode == "test":
              _UpperCamelCase					:		str  =				len(self.test_dataloader().dataset		)
        else:
              _UpperCamelCase					:		int  =				self.get_dataloader("train"    ,      self.hparams.train_batch_size    ,      shuffle=__a		)
              _UpperCamelCase					:		Any  =				len(self.train_dataloader().dataset		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				str    ,      __a      :				int    ,      __a      :				bool = False		)						-> List[Any]:
        raise NotImplementedError("You must implement this for your task"		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Optional[int]:
        return self.train_loader

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[Any]:
        return self.get_dataloader("dev"    ,      self.hparams.eval_batch_size    ,      shuffle=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Optional[int]:
        return self.get_dataloader("test"    ,      self.hparams.eval_batch_size    ,      shuffle=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				List[Any]		)						-> Dict:
        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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Dict[str, Any]		)						-> None:
        _UpperCamelCase					:		Union[str, Any]  =				self.output_dir.joinpath("best_tfmr"		)
        _UpperCamelCase					:		int  =				self.step_count
        self.model.save_pretrained(__a		)
        self.tokenizer.save_pretrained(__a		)


  @staticmethod
  def       __SCREAMING_SNAKE_CASE (						__a      :				Any    ,      __a      :				Tuple		)						-> Optional[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=32    ,      type=__a		)
        parser.add_argument("--eval_batch_size"    ,      default=32    ,      type=__a		)
        parser.add_argument("--adafactor"    ,      action="store_true"		)





class    __SCREAMING_SNAKE_CASE			(  pl.Callback      ):







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				List[Any]    ,      __a      :				Tuple		)						-> int:
        _UpperCamelCase					:		Optional[int]  =				trainer.lr_schedulers[0]["scheduler"]
        _UpperCamelCase					:		Tuple  =				{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
        pl_module.logger.log_metrics(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				pl.Trainer    ,      __a      :				pl.LightningModule		)						-> Union[str, Any]:
        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       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				pl.Trainer    ,      __a      :				pl.LightningModule		)						-> str:
        rank_zero_info("***** Test results *****"		)
        _UpperCamelCase					:		Optional[int]  =				trainer.callback_metrics
        # Log and save results to file
        _UpperCamelCase					:		List[str]  =				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     lowercase__			(    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=42						,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     lowercase__			(    lowercase_						,lowercase_						,lowercase_=None						,lowercase_=True						,lowercase_=[]						,lowercase_=None						,lowercase_=None						,**lowercase_						,)    ->						Optional[Any]:




      """simple docstring"""

      pl.seed_everything(args.seed	)

      # init model
      _UpperCamelCase					:		Any  =				Path(model.hparams.output_dir	)
      odir.mkdir(exist_ok=lowercase_	)

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

      _UpperCamelCase					:		Tuple  =				{}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            _UpperCamelCase					:		str  =				16
            _UpperCamelCase					:		Dict  =				args.fpaa_opt_level

      if args.gpus > 1:
            _UpperCamelCase					:		int  =				"ddp"

      _UpperCamelCase					:		Dict  =				args.accumulate_grad_batches
      _UpperCamelCase					:		List[str]  =				extra_train_kwargs.get("accelerator"						,lowercase_	)
      _UpperCamelCase					:		Any  =				extra_train_kwargs.get("profiler"						,lowercase_	)

      _UpperCamelCase					:		Optional[Any]  =				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"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  lowerCamelCase__      =      []

  lowerCamelCase__      =      os.path.join("tests", "models", "**", "test_modeling_*.py")
  lowerCamelCase__      =      glob.glob(pattern)
  # TODO: deal with TF/Flax too
  lowerCamelCase__      =      [
      x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
  ]

  for test_file in test_files:
    lowerCamelCase__      =      get_tester_classes(test_file)
    for tester_class in tester_classes:
      # A few tester classes don't have `parent` parameter in `__init__`.
      # TODO: deal this better
      try:
        lowerCamelCase__      =      tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, "get_config"):
        lowerCamelCase__      =      tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            lowerCamelCase__      =      None
            if k in ["vocab_size"]:
              lowerCamelCase__      =      100
            elif k in ["max_position_embeddings"]:
              lowerCamelCase__      =      128
            elif k in ["hidden_size", "d_model"]:
              lowerCamelCase__      =      40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              lowerCamelCase__      =      5
            if target is not None and v > target:
              failures.append(
                  f"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                  f""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                  f""" than {target}."""
              )

  if len(failures) > 0:
    raise Exception(f"""There were {len(failures)} failures:\n""" + "\n".join(failures))
