


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase	:		Union[str, Any]		  =						{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		List[str]		  =						[
				    'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Wav2Vec2ConformerForAudioFrameClassification',
				    'Wav2Vec2ConformerForCTC',
				    'Wav2Vec2ConformerForPreTraining',
				    'Wav2Vec2ConformerForSequenceClassification',
				    'Wav2Vec2ConformerForXVector',
				    'Wav2Vec2ConformerModel',
				    'Wav2Vec2ConformerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavaveca_conformer import (
				    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    WavaVecaConformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavaveca_conformer import (
								    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavaVecaConformerForAudioFrameClassification,
								    WavaVecaConformerForCTC,
								    WavaVecaConformerForPreTraining,
								    WavaVecaConformerForSequenceClassification,
								    WavaVecaConformerForXVector,
								    WavaVecaConformerModel,
								    WavaVecaConformerPreTrainedModel,
								)

else:
				import sys

				_lowerCAmelCase	:		List[str]		  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""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


__lowercase          = logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

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






class 		_A    (    pl.LightningModule    ):







    """simple docstring"""




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

      a		:						Union[str, 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:
        a		:						int 				=     AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,					cache_dir=__UpperCAmelCase							,					)
      else:
        a		:						PreTrainedTokenizer 				=     tokenizer
      a		:						str 				=     MODEL_MODES[mode]
      if model is None:
        a		:						Union[str, 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:
        a		:						int 				=     model






    def       __snake_case				(   self							:       List[str]							,					*__UpperCAmelCase							:       List[Any]							,					**__UpperCAmelCase							:       int):
      a		:						List[Any] 				=     self.model_type.from_pretrained(*__UpperCAmelCase							,					**__UpperCAmelCase)






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






    def       __snake_case				(   self							:       Tuple):
      a		:						Optional[Any] 				=     self.model
      a		:						List[Any] 				=     ["bias", "LayerNorm.weight"]
      a		:						Optional[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:
        a		:						List[Any] 				=     Adafactor(
            __UpperCAmelCase							,					lr=self.hparams.learning_rate							,					scale_parameter=__UpperCAmelCase							,					relative_step=__UpperCAmelCase)

      else:
        a		:						Union[str, Any] 				=     AdamW(
            __UpperCAmelCase							,					lr=self.hparams.learning_rate							,					eps=self.hparams.adam_epsilon)
      a		:						int 				=     optimizer

      a		:						Dict 				=     self.get_lr_scheduler()

      return [optimizer], [scheduler]






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       List[str]							,					__UpperCAmelCase							:       Tuple):
      return self.validation_step(__UpperCAmelCase							,					__UpperCAmelCase)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       Optional[int]):
      return self.validation_end(__UpperCAmelCase)






    def       __snake_case				(   self							:       str):
      a		:						Union[str, Any] 				=     max(1							,					self.hparams.gpus)  # TODO: consider num_tpu_cores
      a		:						Optional[int] 				=     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							:       List[str]							,					__UpperCAmelCase							:       Any):
      if stage == "test":
        a		:						Any 				=     len(self.test_dataloader().dataset)
      else:
        a		:						str 				=     self.get_dataloader("train"							,					self.hparams.train_batch_size							,					shuffle=__UpperCAmelCase)
        a		:						Dict 				=     len(self.train_dataloader().dataset)






    def       __snake_case				(   self							:       List[str]							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       bool = False):
      raise NotImplementedError("You must implement this for your task")






    def       __snake_case				(   self							:       Tuple):
      return self.train_loader






    def       __snake_case				(   self							:       str):
      return self.get_dataloader("dev"							,					self.hparams.eval_batch_size							,					shuffle=__UpperCAmelCase)






    def       __snake_case				(   self							:       Optional[Any]):
      return self.get_dataloader("test"							,					self.hparams.eval_batch_size							,					shuffle=__UpperCAmelCase)






    def       __snake_case				(   self							:       Tuple							,					__UpperCAmelCase							:       Tuple):
      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       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       Dict[str, Any]):
      a		:						Optional[Any] 				=     self.output_dir.joinpath("best_tfmr")
      a		:						Union[str, Any] 				=     self.step_count
      self.model.save_pretrained(__UpperCAmelCase)
      self.tokenizer.save_pretrained(__UpperCAmelCase)






    @staticmethod
    def       __snake_case				(   __UpperCAmelCase							:       str							,					__UpperCAmelCase							:       int):
      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 		_A    (    pl.Callback    ):







    """simple docstring"""




    def       __snake_case				(   self							:       int							,					__UpperCAmelCase							:       Optional[int]							,					__UpperCAmelCase							:       Tuple):
      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 		_A    (    pl.Callback    ):







    """simple docstring"""




    def       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       str):
      a		:						List[str] 				=     trainer.lr_schedulers[0]["scheduler"]
      a		:						Tuple 				=     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
      pl_module.logger.log_metrics(__UpperCAmelCase)






    def       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       pl.Trainer							,					__UpperCAmelCase							:       pl.LightningModule):
      rank_zero_info("***** Validation results *****")
      a		:						Any 				=     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       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       pl.Trainer							,					__UpperCAmelCase							:       pl.LightningModule):
      rank_zero_info("***** Test results *****")
      a		:						Tuple 				=     trainer.callback_metrics
      # Log and save results to file
      a		:						List[str] 				=     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 	lowercase     (							A_    ,  A_			)-> None:
  '''simple docstring'''







  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=42    ,  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 	lowercase     (							A_    ,  A_    ,  A_=None    ,  A_=True    ,  A_=None    ,  A_=[]    ,  A_=None    ,  A_=None    ,  **A_    ,  )-> str:
  '''simple docstring'''







  pl.seed_everything(args.seed			)

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

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

  a		:						Any 				=     {}

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

  if args.gpus > 1:
    a		:						Optional[Any] 				=     "auto"  # "ddp"
    a		:						List[str] 				=     "ddp"

  a		:						Dict 				=     args.accumulate_grad_batches
  a		:						Tuple 				=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  a		:						List[str] 				=     "auto"

  a		:						Union[str, Any] 				=     pl.Trainer.from_argparse_args(
      A_    ,  weights_summary=A_    ,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,  logger=A_    ,  **A_    ,  )

  if args.do_train:
    trainer.fit(A_			)

  return trainer






