


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


__UpperCAmelCase      			=  logging.getLogger(__name__)

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

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



class 							lowercase__(						pl.LightningModule			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE="base"   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						Optional[int]:







         """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(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=0
         UpperCamelCase__ :			int 						=Path(self.hparams.output_dir)
         UpperCamelCase__ :			Union[str, Any] 						=self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
            UpperCamelCase__ :			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=__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE   ,			)
         else:
            UpperCamelCase__ :			PretrainedConfig 						=config

         UpperCamelCase__ :			List[Any] 						=("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
         for p in extra_model_params:
            if getattr(self.hparams   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
               assert hasattr(self.config   ,			__SCREAMING_SNAKE_CASE), F'''model config doesn\'t have a `{p}` attribute'''
               setattr(self.config   ,			__SCREAMING_SNAKE_CASE   ,			getattr(self.hparams   ,			__SCREAMING_SNAKE_CASE))

         if tokenizer is None:
            UpperCamelCase__ :			Union[str, Any] 						=AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,			cache_dir=__SCREAMING_SNAKE_CASE   ,			)
         else:
            UpperCamelCase__ :			PreTrainedTokenizer 						=tokenizer
         UpperCamelCase__ :			Dict 						=MODEL_MODES[mode]
         if model is None:
            UpperCamelCase__ :			Optional[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=__SCREAMING_SNAKE_CASE   ,			)
         else:
            UpperCamelCase__ :			Optional[int] 						=model




      def    UpperCAmelCase							(		self   ,			*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=arg_to_scheduler[self.hparams.lr_scheduler]
         UpperCamelCase__ :			Optional[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)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.model
         UpperCamelCase__ :			Optional[int] 						=["bias", "LayerNorm.weight"]
         UpperCamelCase__ :			List[str] 						=[
             {
                 "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__ :			Union[str, Any] 						=Adafactor(
                __SCREAMING_SNAKE_CASE   ,			lr=self.hparams.learning_rate   ,			scale_parameter=__SCREAMING_SNAKE_CASE   ,			relative_step=__SCREAMING_SNAKE_CASE)

         else:
            UpperCamelCase__ :			Optional[int] 						=AdamW(
                __SCREAMING_SNAKE_CASE   ,			lr=self.hparams.learning_rate   ,			eps=self.hparams.adam_epsilon)
         UpperCamelCase__ :			List[str] 						=optimizer

         UpperCamelCase__ :			Any 						=self.get_lr_scheduler()

         return [optimizer], [scheduler]




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         return self.validation_step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         return self.validation_end(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[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   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         if stage == "test":
            UpperCamelCase__ :			List[Any] 						=len(self.test_dataloader().dataset)
         else:
            UpperCamelCase__ :			str 						=self.get_dataloader("train"   ,			self.hparams.train_batch_size   ,			shuffle=__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Optional[Any] 						=len(self.train_dataloader().dataset)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = False)					->						Union[str, Any]:







         """simple docstring"""







         raise NotImplementedError("You must implement this for your task")




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         return self.train_loader




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         return self.get_dataloader("dev"   ,			self.hparams.eval_batch_size   ,			shuffle=__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         return self.get_dataloader("test"   ,			self.hparams.eval_batch_size   ,			shuffle=__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         return os.path.join(
             self.hparams.data_dir   ,			"cached_{}_{}_{}".format(
                 __SCREAMING_SNAKE_CASE   ,			list(filter(__SCREAMING_SNAKE_CASE   ,			self.hparams.model_name_or_path.split("/"))).pop()   ,			str(self.hparams.max_seq_length)   ,			)   ,			)




      @pl.utilities.rank_zero_only
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.output_dir.joinpath("best_tfmr")
         UpperCamelCase__ :			Union[str, Any] 						=self.step_count
         self.model.save_pretrained(__SCREAMING_SNAKE_CASE)
         self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         parser.add_argument(
             "--model_name_or_path"   ,			default=__SCREAMING_SNAKE_CASE   ,			type=__SCREAMING_SNAKE_CASE   ,			required=__SCREAMING_SNAKE_CASE   ,			help="Path to pretrained model or model identifier from huggingface.co/models"   ,			)
         parser.add_argument(
             "--config_name"   ,			default=""   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Pretrained config name or path if not the same as model_name")
         parser.add_argument(
             "--tokenizer_name"   ,			default=__SCREAMING_SNAKE_CASE   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Pretrained tokenizer name or path if not the same as model_name"   ,			)
         parser.add_argument(
             "--cache_dir"   ,			default=""   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Where do you want to store the pre-trained models downloaded from huggingface.co"   ,			)
         parser.add_argument(
             "--encoder_layerdrop"   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Encoder layer dropout probability (Optional). Goes into model.config"   ,			)
         parser.add_argument(
             "--decoder_layerdrop"   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Decoder layer dropout probability (Optional). Goes into model.config"   ,			)
         parser.add_argument(
             "--dropout"   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Dropout probability (Optional). Goes into model.config"   ,			)
         parser.add_argument(
             "--attention_dropout"   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Attention dropout probability (Optional). Goes into model.config"   ,			)
         parser.add_argument("--learning_rate"   ,			default=5E-5   ,			type=__SCREAMING_SNAKE_CASE   ,			help="The initial learning rate for Adam.")
         parser.add_argument(
             "--lr_scheduler"   ,			default="linear"   ,			choices=__SCREAMING_SNAKE_CASE   ,			metavar=__SCREAMING_SNAKE_CASE   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Learning rate scheduler"   ,			)
         parser.add_argument("--weight_decay"   ,			default=0.0   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon"   ,			default=1E-8   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Epsilon for Adam optimizer.")
         parser.add_argument("--warmup_steps"   ,			default=0   ,			type=__SCREAMING_SNAKE_CASE   ,			help="Linear warmup over warmup_steps.")
         parser.add_argument("--num_workers"   ,			default=4   ,			type=__SCREAMING_SNAKE_CASE   ,			help="kwarg passed to DataLoader")
         parser.add_argument("--num_train_epochs"   ,			dest="max_epochs"   ,			default=3   ,			type=__SCREAMING_SNAKE_CASE)
         parser.add_argument("--train_batch_size"   ,			default=32   ,			type=__SCREAMING_SNAKE_CASE)
         parser.add_argument("--eval_batch_size"   ,			default=32   ,			type=__SCREAMING_SNAKE_CASE)
         parser.add_argument("--adafactor"   ,			action="store_true")




class 							lowercase__(						pl.Callback			):






      '''simple docstring'''







      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=trainer.lr_schedulers[0]["scheduler"]
         UpperCamelCase__ :			str 						={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
         pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         rank_zero_info("***** Validation results *****")
         UpperCamelCase__ :			Union[str, Any] 						=trainer.callback_metrics
         # Log results
         for key in sorted(__SCREAMING_SNAKE_CASE):
            if key not in ["log", "progress_bar"]:
               rank_zero_info("{} = {}\n".format(__SCREAMING_SNAKE_CASE   ,			str(metrics[key])))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         rank_zero_info("***** Test results *****")
         UpperCamelCase__ :			List[str] 						=trainer.callback_metrics
         # Log and save results to file
         UpperCamelCase__ :			List[Any] 						=os.path.join(pl_module.hparams.output_dir   ,			"test_results.txt")
         with open(__SCREAMING_SNAKE_CASE   ,			"w") as writer:
            for key in sorted(__SCREAMING_SNAKE_CASE):
               if key not in ["log", "progress_bar"]:
                  rank_zero_info("{} = {}\n".format(__SCREAMING_SNAKE_CASE   ,			str(metrics[key])))
                  writer.write("{} = {}\n".format(__SCREAMING_SNAKE_CASE   ,			str(metrics[key])))




def 	_lowerCamelCase  (		A_  :	List[Any]							,  A_  :	Dict      )			->			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=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 	_lowerCamelCase  (		A_  :	BaseTransformer							,  A_  :	argparse.Namespace							,  A_  :	List[str]=None							,  A_  :	Optional[Any]=True							,  A_  :	Optional[int]=None							,  A_  :	Optional[Any]=[]							,  A_  :	Tuple=None							,  A_  :	List[Any]=None							,  **A_  :	Any							,  )			->			Dict:

   '''simple docstring'''






   pl.seed_everything(args.seed      )

   # init model
   UpperCamelCase__ :			Optional[Any] 						=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__ :			Any 						=LoggingCallback()

   UpperCamelCase__ :			int 						={}

   # TODO: remove with PyTorch 1.6 since pl uses native amp
   if args.fpaa:
      UpperCamelCase__ :			Optional[int] 						=1_6
      # train_params["amp_level"] = args.fp16_opt_level

   if args.gpus > 1:
      UpperCamelCase__ :			int 						="auto"  # "ddp"
      UpperCamelCase__ :			Any 						="ddp"

   UpperCamelCase__ :			Optional[int] 						=args.accumulate_grad_batches
   UpperCamelCase__ :			Optional[int] 						=None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
   UpperCamelCase__ :			List[Any] 						="auto"

   UpperCamelCase__ :			List[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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      			=  {
    """configuration_switch_transformers""": [
        """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwitchTransformersConfig""",
        """SwitchTransformersOnnxConfig""",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SwitchTransformersEncoderModel""",
         """SwitchTransformersForConditionalGeneration""",
         """SwitchTransformersModel""",
         """SwitchTransformersPreTrainedModel""",
         """SwitchTransformersTop1Router""",
         """SwitchTransformersSparseMLP""",
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

