

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


__A     						=      logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__A     						=      {
    '''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
__A     						=      {
    '''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
}
__A     						=      sorted(arg_to_scheduler.keys())
__A     						=      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class     lowercase_       (    pl.LightningModule	):



      def __init__(     self :							List[str]     ,   A__ :							Union[str, Any]     ,   A__ :							Dict=None     ,   A__ :							Dict="base"     ,   A__ :							Any=None     ,   A__ :							str=None     ,   A__ :							Tuple=None     ,   **A__ :							Tuple     ,   )       ->	Optional[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(__SCREAMING_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=__SCREAMING_SNAKE_CASE     ,   **__SCREAMING_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     ,   __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:
          _snake_case		=				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:
          _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=__SCREAMING_SNAKE_CASE     ,   )
        else:
          _snake_case		=				model



      def 		UpperCamelCase_						(     self :							Dict     ,   *A__ :							Optional[Any]     ,   **A__ :							Optional[Any]						)       ->	Tuple:
        _snake_case		=				self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE     ,   **__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							int						)       ->	List[str]:
        _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 		UpperCamelCase_						(     self :							str						)       ->	List[str]:
        _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(
              __SCREAMING_SNAKE_CASE     ,   lr=self.hparams.learning_rate     ,   scale_parameter=__SCREAMING_SNAKE_CASE     ,   relative_step=__SCREAMING_SNAKE_CASE						)

        else:
          _snake_case		=				AdamW(
              __SCREAMING_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 		UpperCamelCase_						(     self :							Any     ,   A__ :							int     ,   A__ :							List[str]						)       ->	str:
        return self.validation_step(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							int						)       ->	List[Any]:
        return self.validation_end(__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							int						)       ->	int:
        _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 		UpperCamelCase_						(     self :							Any     ,   A__ :							Dict						)       ->	Optional[int]:







        if stage == "test":
          _snake_case		=				len(self.test_dataloader().dataset						)
        else:
          _snake_case		=				self.get_dataloader('''train'''     ,   self.hparams.train_batch_size     ,   shuffle=__SCREAMING_SNAKE_CASE						)
          _snake_case		=				len(self.train_dataloader().dataset						)



      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Optional[Any]     ,   A__ :							Optional[int]     ,   A__ :							List[Any] = False						)       ->	Union[str, Any]:
        raise NotImplementedError('''You must implement this for your task'''						)



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Tuple:
        return self.train_loader



      def 		UpperCamelCase_						(     self :							Any						)       ->	Tuple:
        return self.get_dataloader('''dev'''     ,   self.hparams.eval_batch_size     ,   shuffle=__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							int						)       ->	List[str]:
        return self.get_dataloader('''test'''     ,   self.hparams.eval_batch_size     ,   shuffle=__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Any     ,   A__ :							Dict						)       ->	int:
        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 :							Dict     ,   A__ :							int						)       ->	None:
        _snake_case		=				self.output_dir.joinpath('''best_tfmr'''						)
        _snake_case		=				self.step_count
        self.model.save_pretrained(__SCREAMING_SNAKE_CASE						)
        self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE						)







      @staticmethod
      def 		UpperCamelCase_						(     A__ :							List[Any]     ,   A__ :							Any						)       ->	str:
        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	):



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							List[Any]     ,   A__ :							Tuple						)       ->	List[Any]:







        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	):



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Any     ,   A__ :							List[str]						)       ->	str:
        _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(__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Optional[Any]     ,   A__ :							List[str]						)       ->	List[Any]:
        rank_zero_info('''***** Validation results *****'''						)
        _snake_case		=				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 :							Union[str, Any]     ,   A__ :							Union[str, Any]     ,   A__ :							List[str]						)       ->	Optional[int]:
        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(__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  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      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  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase=None					,			_UpperCamelCase=True					,			_UpperCamelCase=None					,			_UpperCamelCase=[]					,			_UpperCamelCase=None					,			_UpperCamelCase=None					,			**_UpperCamelCase					,			)				->      Dict:



  """simple docstring"""






  pl.seed_everything(args.seed	)

  # init model
  _snake_case		=				Path(model.hparams.output_dir	)
  odir.mkdir(exist_ok=A_	)

  # 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(A_	)
  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(
      A_					,			weights_summary=A_					,			callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]					,			logger=A_					,			**A_					,			)

  if args.do_train:
    trainer.fit(A_	)

  return trainer





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

