


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


_SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_SCREAMING_SNAKE_CASE    :				Dict       =					{
    "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
_SCREAMING_SNAKE_CASE    :				Union[str, 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
}
_SCREAMING_SNAKE_CASE    :				str       =					sorted(arg_to_scheduler.keys())
_SCREAMING_SNAKE_CASE    :				Any       =					"{" + ", ".join(arg_to_scheduler_choices) + "}"

class 						A__      (					pl.LightningModule			):







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case="base"    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      **__snake_case    ,      ):
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(__snake_case					)
         snake_case					=       0
         snake_case					=       Path(self.hparams.output_dir					)
         snake_case					=       self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
              snake_case					=       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=__snake_case    ,      **__snake_case    ,      )
         else:
              snake_case					=       config

         snake_case					=       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
         for p in extra_model_params:
              if getattr(self.hparams    ,      __snake_case    ,      __snake_case					):
                   assert hasattr(self.config    ,      __snake_case					), F'''model config doesn\'t have a `{p}` attribute'''
                   setattr(self.config    ,      __snake_case    ,      getattr(self.hparams    ,      __snake_case					)					)

         if tokenizer is None:
              snake_case					=       AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,      cache_dir=__snake_case    ,      )
         else:
              snake_case					=       tokenizer
         snake_case					=       MODEL_MODES[mode]
         if model is None:
              snake_case					=       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=__snake_case    ,      )
         else:
              snake_case					=       model
    def 	a_    ( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       self.model_type.from_pretrained(*__snake_case    ,      **__snake_case					)
    def 	a_    ( self					):
         snake_case					=       arg_to_scheduler[self.hparams.lr_scheduler]
         snake_case					=       get_schedule_func(
             self.opt    ,      num_warmup_steps=self.hparams.warmup_steps    ,      num_training_steps=self.total_steps()					)
         snake_case					=       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
         return scheduler
    def 	a_    ( self					):
         snake_case					=       self.model
         snake_case					=       ['''bias''', '''LayerNorm.weight''']
         snake_case					=       [
             {
                 '''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:
              snake_case					=       Adafactor(
                  __snake_case    ,      lr=self.hparams.learning_rate    ,      scale_parameter=__snake_case    ,      relative_step=__snake_case					)

         else:
              snake_case					=       AdamW(
                  __snake_case    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon					)
         snake_case					=       optimizer

         snake_case					=       self.get_lr_scheduler()

         return [optimizer], [scheduler]
    def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
         return self.validation_step(__snake_case    ,      __snake_case					)
    def 	a_    ( self    ,      __snake_case					):
         return self.validation_end(__snake_case					)
    def 	a_    ( self					):
         snake_case					=       max(1    ,      self.hparams.gpus					)  # TODO: consider num_tpu_cores
         snake_case					=       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
    def 	a_    ( self    ,      __snake_case					):
         if stage == "test":
              snake_case					=       len(self.test_dataloader().dataset					)
         else:
              snake_case					=       self.get_dataloader('''train'''    ,      self.hparams.train_batch_size    ,      shuffle=__snake_case					)
              snake_case					=       len(self.train_dataloader().dataset					)
    def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = False					):
         raise NotImplementedError('''You must implement this for your task'''					)
    def 	a_    ( self					):
         return self.train_loader
    def 	a_    ( self					):
         return self.get_dataloader('''dev'''    ,      self.hparams.eval_batch_size    ,      shuffle=__snake_case					)
    def 	a_    ( self					):
         return self.get_dataloader('''test'''    ,      self.hparams.eval_batch_size    ,      shuffle=__snake_case					)
    def 	a_    ( self    ,      __snake_case					):
         return os.path.join(
             self.hparams.data_dir    ,      '''cached_{}_{}_{}'''.format(
                 __snake_case    ,      list(filter(__snake_case    ,      self.hparams.model_name_or_path.split('''/'''					)					)					).pop()    ,      str(self.hparams.max_seq_length					)    ,      )    ,      )
    @pl.utilities.rank_zero_only
    def 	a_    ( self    ,      __snake_case					):
         snake_case					=       self.output_dir.joinpath('''best_tfmr'''					)
         snake_case					=       self.step_count
         self.model.save_pretrained(__snake_case					)
         self.tokenizer.save_pretrained(__snake_case					)




    @staticmethod
    def 	a_    ( __snake_case    ,      __snake_case					):
         parser.add_argument(
             '''--model_name_or_path'''    ,      default=__snake_case    ,      type=__snake_case    ,      required=__snake_case    ,      help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,      )
         parser.add_argument(
             '''--config_name'''    ,      default=''''''    ,      type=__snake_case    ,      help='''Pretrained config name or path if not the same as model_name'''					)
         parser.add_argument(
             '''--tokenizer_name'''    ,      default=__snake_case    ,      type=__snake_case    ,      help='''Pretrained tokenizer name or path if not the same as model_name'''    ,      )
         parser.add_argument(
             '''--cache_dir'''    ,      default=''''''    ,      type=__snake_case    ,      help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''    ,      )
         parser.add_argument(
             '''--encoder_layerdrop'''    ,      type=__snake_case    ,      help='''Encoder layer dropout probability (Optional). Goes into model.config'''    ,      )
         parser.add_argument(
             '''--decoder_layerdrop'''    ,      type=__snake_case    ,      help='''Decoder layer dropout probability (Optional). Goes into model.config'''    ,      )
         parser.add_argument(
             '''--dropout'''    ,      type=__snake_case    ,      help='''Dropout probability (Optional). Goes into model.config'''    ,      )
         parser.add_argument(
             '''--attention_dropout'''    ,      type=__snake_case    ,      help='''Attention dropout probability (Optional). Goes into model.config'''    ,      )
         parser.add_argument('''--learning_rate'''    ,      default=5E-5    ,      type=__snake_case    ,      help='''The initial learning rate for Adam.'''					)
         parser.add_argument(
             '''--lr_scheduler'''    ,      default='''linear'''    ,      choices=__snake_case    ,      metavar=__snake_case    ,      type=__snake_case    ,      help='''Learning rate scheduler'''    ,      )
         parser.add_argument('''--weight_decay'''    ,      default=0.0    ,      type=__snake_case    ,      help='''Weight decay if we apply some.'''					)
         parser.add_argument('''--adam_epsilon'''    ,      default=1E-8    ,      type=__snake_case    ,      help='''Epsilon for Adam optimizer.'''					)
         parser.add_argument('''--warmup_steps'''    ,      default=0    ,      type=__snake_case    ,      help='''Linear warmup over warmup_steps.'''					)
         parser.add_argument('''--num_workers'''    ,      default=4    ,      type=__snake_case    ,      help='''kwarg passed to DataLoader'''					)
         parser.add_argument('''--num_train_epochs'''    ,      dest='''max_epochs'''    ,      default=3    ,      type=__snake_case					)
         parser.add_argument('''--train_batch_size'''    ,      default=3_2    ,      type=__snake_case					)
         parser.add_argument('''--eval_batch_size'''    ,      default=3_2    ,      type=__snake_case					)
         parser.add_argument('''--adafactor'''    ,      action='''store_true'''					)






class 						A__      (					pl.Callback			):







    """simple docstring"""

    def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
         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 	a_    ( self    ,      __snake_case    ,      __snake_case					):
         snake_case					=       trainer.lr_schedulers[0]['''scheduler''']
         snake_case					=       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
         pl_module.logger.log_metrics(__snake_case					)
    def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
         rank_zero_info('''***** Validation results *****'''					)
         snake_case					=       trainer.callback_metrics
         # Log results
         for key in sorted(__snake_case					):
              if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(__snake_case    ,      str(metrics[key]					)					)					)




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






def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=True							,UpperCamelCase_=None							,UpperCamelCase_=[]							,UpperCamelCase_=None							,UpperCamelCase_=None							,**UpperCamelCase_							,):



     """simple docstring"""






     pl.seed_everything(args.seed       )

     # init model
     snake_case					=       Path(model.hparams.output_dir       )
     odir.mkdir(exist_ok=UpperCamelCase_       )

     # add custom checkpoints
     if checkpoint_callback is None:
          snake_case					=       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:
          snake_case					=       LoggingCallback()

     snake_case					=       {}

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

     if args.gpus > 1:
          snake_case					=       '''auto'''  # "ddp"
          snake_case					=       '''ddp'''

     snake_case					=       args.accumulate_grad_batches
     snake_case					=       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     snake_case					=       '''auto'''

     snake_case					=       pl.Trainer.from_argparse_args(
         UpperCamelCase_							,weights_summary=UpperCamelCase_							,callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,logger=UpperCamelCase_							,**UpperCamelCase_							,)

     if args.do_train:
          trainer.fit(UpperCamelCase_       )

     return trainer








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




