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__		:					Optional[Any]						=					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

SCREAMING_SNAKE_CASE__		:					List[str]						=					{
    "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__		:					Optional[int]						=					{
    "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__		:					Optional[Any]						=					"{" + ", ".join(arg_to_scheduler_choices) + "}"

class       snake_case					(      pl.LightningModule						):




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

       SCREAMING_SNAKE_CASE__				:				Dict			=		('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:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     , cache_dir=__A     , )
       else:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		tokenizer
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		MODEL_MODES[mode]
       if model is None:
            SCREAMING_SNAKE_CASE__				:				List[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:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		model




  def 	__lowercase(      self     :	int     , *a_     :	int     , **a_     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_type.from_pretrained(*__A     , **__A				)




  def 	__lowercase(      self     :	Optional[int]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		arg_to_scheduler[self.hparams.lr_scheduler]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_schedule_func(
           self.opt     , num_warmup_steps=self.hparams.warmup_steps     , num_training_steps=self.total_steps()				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
       return scheduler




  def 	__lowercase(      self     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		['bias', 'LayerNorm.weight']
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[
           {
               'params': [
                   p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)
               ],  # check this named paramters
               '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:
            SCREAMING_SNAKE_CASE__				:				Any			=		Adafactor(
                __A     , lr=self.hparams.learning_rate     , scale_parameter=__A     , relative_step=__A				)

       else:
            SCREAMING_SNAKE_CASE__				:				str			=		AdamW(
                __A     , lr=self.hparams.learning_rate     , eps=self.hparams.adam_epsilon				)
       SCREAMING_SNAKE_CASE__				:				int			=		optimizer

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_lr_scheduler()

       return [optimizer], [scheduler]




  def 	__lowercase(      self     :	List[Any]     , a_     :	Any     , a_     :	Optional[Any]				)->       int:




       """simple docstring"""
       return self.validation_step(__A     , __A				)




  def 	__lowercase(      self     :	int     , a_     :	Any				)->       Optional[int]:




       """simple docstring"""
       return self.validation_end(__A				)




  def 	__lowercase(      self     :	Optional[int]				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		max(1     , self.hparams.gpus				)  # TODO: consider num_tpu_cores
       SCREAMING_SNAKE_CASE__				:				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 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[str]				)->       Optional[int]:




       """simple docstring"""
       if stage == "test":
            SCREAMING_SNAKE_CASE__				:				int			=		len(self.test_dataloader().dataset				)
       else:
            SCREAMING_SNAKE_CASE__				:				Any			=		self.get_dataloader('train'     , self.hparams.train_batch_size     , shuffle=__A				)
            SCREAMING_SNAKE_CASE__				:				str			=		len(self.train_dataloader().dataset				)




  def 	__lowercase(      self     :	int     , a_     :	str     , a_     :	int     , a_     :	bool = False				)->       Tuple:




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




  def 	__lowercase(      self     :	Union[str, Any]				)->       str:




       """simple docstring"""
       return self.train_loader




  def 	__lowercase(      self     :	Dict				)->       int:




       """simple docstring"""
       return self.get_dataloader('dev'     , self.hparams.eval_batch_size     , shuffle=__A				)




  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[Any]:




       """simple docstring"""
       return self.get_dataloader('test'     , self.hparams.eval_batch_size     , shuffle=__A				)




  def 	__lowercase(      self     :	Tuple     , a_     :	List[Any]				)->       List[str]:




       """simple docstring"""
       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 	__lowercase(      self     :	Optional[Any]     , a_     :	Dict[str, Any]				)->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.output_dir.joinpath('best_tfmr'				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.step_count
       self.model.save_pretrained(__A				)
       self.tokenizer.save_pretrained(__A				)




  @staticmethod
  def 	__lowercase(      a_     :	Union[str, Any]     , a_     :	Optional[Any]				)->       Dict:




       """simple docstring"""
       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=str(Path(__A				).parent / 'test_run' / 'cache'				)     , 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       snake_case					(      pl.Callback						):




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	str     , a_     :	List[str]				)->       Union[str, 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       snake_case					(      pl.Callback						):




  def 	__lowercase(      self     :	str     , a_     :	int     , a_     :	List[str]				)->       List[Any]:




       """simple docstring"""
       # print(pl_module.model.rag)
       for name, param in pl_module.model.rag.named_parameters():
            if param.grad is None:
                 print(__A				)




class       snake_case					(      pl.Callback						):




  def 	__lowercase(      self     :	int     , a_     :	Union[str, Any]     , a_     :	Any				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		trainer.lr_schedulers[0]['scheduler']
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
       pl_module.logger.log_metrics(__A				)




  def 	__lowercase(      self     :	str     , a_     :	pl.Trainer     , a_     :	pl.LightningModule				)->       Optional[Any]:




       """simple docstring"""
       rank_zero_info('***** Validation results *****'				)
       SCREAMING_SNAKE_CASE__				:				Union[str, 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 	__lowercase(      self     :	Tuple     , a_     :	pl.Trainer     , a_     :	pl.LightningModule				)->       Optional[Any]:




       """simple docstring"""
       rank_zero_info('***** Test results *****'				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		trainer.callback_metrics
       # Log and save results to file
       SCREAMING_SNAKE_CASE__				:				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     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     parser.add_argument(
         '--output_dir'     ,  default=str(Path(_lowercase ).parent / 'test_run' / 'model_checkpoints' )     ,  type=_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=str(Path(_lowercase ).parent / 'test_run' / 'dummy-train-data' )     ,  type=_lowercase     ,  help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'     ,  )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			List[str]     ,  lowercase__  :			int=None     ,  lowercase__  :			Optional[Any]=True     ,  lowercase__  :			Dict=[]     ,  lowercase__  :			Any=None     ,  lowercase__  :			List[Any]=None     ,  **lowercase__  :			int     ,  ):







     '''simple docstring'''


     pl.seed_everything(args.seed )

     # init model
     SCREAMING_SNAKE_CASE__				:				Dict			=		Path(model.hparams.output_dir )
     odir.mkdir(exist_ok=_lowercase )

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

     SCREAMING_SNAKE_CASE__				:				int			=		{}

     if args.fpaa:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		16

     if args.gpus > 1:
          SCREAMING_SNAKE_CASE__				:				Dict			=		'auto'
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'ddp'

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		args.accumulate_grad_batches
     SCREAMING_SNAKE_CASE__				:				Any			=		None
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		'auto'

     SCREAMING_SNAKE_CASE__				:				List[str]			=		pl.Trainer.from_argparse_args(
         _lowercase     ,  weights_summary=_lowercase     ,  callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]     ,  logger=_lowercase     ,  val_check_interval=1     ,  num_sanity_val_steps=2     ,  **_lowercase     ,  )

     if args.do_train:
          trainer.fit(_lowercase )

     else:
          print('RAG modeling tests with new set functions successfuly executed!' )
     return trainer




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           [],
           [],
           [],
       ]




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	int				)->       None:




       """simple docstring"""
       try:
            if len(self.queues[priority]				) >= 100:
                 raise OverflowError('Maximum queue size is 100'				)
            self.queues[priority].append(a_				)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       for queue in self.queues:
            if queue:
                 return queue.pop(0				)
       raise UnderFlowError('All queues are empty'				)




  def __str__(      self     :	Any				)->       str:




       """simple docstring"""
       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]




  def 	__lowercase(      self     :	List[str]     , a_     :	int				)->       None:




       """simple docstring"""
       if len(self.queue				) == 100:
            raise OverFlowError('Maximum queue size is 100'				)
       self.queue.append(a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if not self.queue:
            raise UnderFlowError('The queue is empty'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(self.queue				)
            self.queue.remove(a_				)
            return data




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




       """simple docstring"""
       return str(self.queue				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		FixedPriorityQueue()
     fpq.enqueue(0     ,  10 )
     fpq.enqueue(1     ,  70 )
     fpq.enqueue(0     ,  1_00 )
     fpq.enqueue(2     ,  1 )
     fpq.enqueue(2     ,  5 )
     fpq.enqueue(1     ,  7 )
     fpq.enqueue(2     ,  4 )
     fpq.enqueue(1     ,  64 )
     fpq.enqueue(0     ,  1_28 )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		ElementPriorityQueue()
     epq.enqueue(10 )
     epq.enqueue(70 )
     epq.enqueue(1_00 )
     epq.enqueue(1 )
     epq.enqueue(5 )
     epq.enqueue(7 )
     epq.enqueue(4 )
     epq.enqueue(64 )
     epq.enqueue(1_28 )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()



