

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__  :      str			=    logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCamelCase__  :      List[Any]			=    {
    "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__  :      str			=    {
    "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__  :      Any			=    sorted(arg_to_scheduler.keys())
UpperCamelCase__  :      str			=    "{" + ", ".join(arg_to_scheduler_choices) + "}"



class       __snake_case  (    pl.LightningModule       ):

  def __init__(					self						,		_A						,		_A=None						,		_A="base"						,		_A=None						,		_A=None						,		_A=None						,		**_A						,		):
      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_									=						0
      SCREAMING_SNAKE_CASE_									=						Path(self.hparams.output_dir)
      SCREAMING_SNAKE_CASE_									=						self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
          SCREAMING_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=_A						,		**_A						,		)
      else:
          SCREAMING_SNAKE_CASE_									=						config

      SCREAMING_SNAKE_CASE_									=						('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_									=						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_									=						tokenizer
      SCREAMING_SNAKE_CASE_									=						MODEL_MODES[mode]
      if model is None:
          SCREAMING_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=_A						,		)
      else:
          SCREAMING_SNAKE_CASE_									=						model

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      SCREAMING_SNAKE_CASE_									=						self.model_type.from_pretrained(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						arg_to_scheduler[self.hparams.lr_scheduler]
      SCREAMING_SNAKE_CASE_									=						get_schedule_func(
          self.opt						,		num_warmup_steps=self.hparams.warmup_steps						,		num_training_steps=self.total_steps())
      SCREAMING_SNAKE_CASE_									=						{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
      return scheduler

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.model
      SCREAMING_SNAKE_CASE_									=						['bias', 'LayerNorm.weight']
      SCREAMING_SNAKE_CASE_									=						[
          {
              '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_									=						Adafactor(
              _A						,		lr=self.hparams.learning_rate						,		scale_parameter=_A						,		relative_step=_A)

      else:
          SCREAMING_SNAKE_CASE_									=						AdamW(
              _A						,		lr=self.hparams.learning_rate						,		eps=self.hparams.adam_epsilon)
      SCREAMING_SNAKE_CASE_									=						optimizer

      SCREAMING_SNAKE_CASE_									=						self.get_lr_scheduler()

      return [optimizer], [scheduler]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      return self.validation_step(_A						,		_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.validation_end(_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						max(1						,		self.hparams.gpus)  # TODO: consider num_tpu_cores
      SCREAMING_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 						lowerCAmelCase__     (					self						,		_A):
      if stage == "test":
          SCREAMING_SNAKE_CASE_									=						len(self.test_dataloader().dataset)
      else:
          SCREAMING_SNAKE_CASE_									=						self.get_dataloader('train'						,		self.hparams.train_batch_size						,		shuffle=_A)
          SCREAMING_SNAKE_CASE_									=						len(self.train_dataloader().dataset)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = False):
      raise NotImplementedError('You must implement this for your task')

  def 						lowerCAmelCase__     (					self):
      return self.train_loader

  def 						lowerCAmelCase__     (					self):
      return self.get_dataloader('dev'						,		self.hparams.eval_batch_size						,		shuffle=_A)

  def 						lowerCAmelCase__     (					self):
      return self.get_dataloader('test'						,		self.hparams.eval_batch_size						,		shuffle=_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      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 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						self.output_dir.joinpath('best_tfmr')
      SCREAMING_SNAKE_CASE_									=						self.step_count
      self.model.save_pretrained(_A)
      self.tokenizer.save_pretrained(_A)


  @staticmethod
  def 						lowerCAmelCase__     (					_A						,		_A):
      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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      # 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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						trainer.lr_schedulers[0]['scheduler']
      SCREAMING_SNAKE_CASE_									=						{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
      pl_module.logger.log_metrics(_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      rank_zero_info('***** Validation results *****')
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      rank_zero_info('***** Test results *****')
      SCREAMING_SNAKE_CASE_									=						trainer.callback_metrics
      # Log and save results to file
      SCREAMING_SNAKE_CASE_									=						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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    parser.add_argument(
        '--output_dir' ,   default=str(Path(_SCREAMING_SNAKE_CASE       ).parent / 'test_run' / 'model_checkpoints'       ) ,   type=_SCREAMING_SNAKE_CASE ,   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=_SCREAMING_SNAKE_CASE ,   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=_SCREAMING_SNAKE_CASE       )
    parser.add_argument('--max_grad_norm' ,   dest='gradient_clip_val' ,   default=1.0 ,   type=_SCREAMING_SNAKE_CASE ,   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=_SCREAMING_SNAKE_CASE ,   default=1 ,   help='Number of updates steps to accumulate before performing a backward/update pass.' ,   )
    parser.add_argument('--seed' ,   type=_SCREAMING_SNAKE_CASE ,   default=42 ,   help='random seed for initialization'       )
    parser.add_argument(
        '--data_dir' ,   default=str(Path(_SCREAMING_SNAKE_CASE       ).parent / 'test_run' / 'dummy-train-data'       ) ,   type=_SCREAMING_SNAKE_CASE ,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' ,   )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		BaseTransformer ,   _SCREAMING_SNAKE_CASE					:		argparse.Namespace ,   _SCREAMING_SNAKE_CASE					:		Any=None ,   _SCREAMING_SNAKE_CASE					:		Dict=True ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=[] ,   _SCREAMING_SNAKE_CASE					:		Tuple=None ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=None ,   **_SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   ):





    """simple docstring"""


    pl.seed_everything(args.seed       )

    # init model
    SCREAMING_SNAKE_CASE_									=						Path(model.hparams.output_dir       )
    odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )

    # add custom checkpoints
    if checkpoint_callback is None:
        SCREAMING_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(_SCREAMING_SNAKE_CASE       )
    if logging_callback is None:
        SCREAMING_SNAKE_CASE_									=						LoggingCallback()

    SCREAMING_SNAKE_CASE_									=						{}

    if args.fpaa:
        SCREAMING_SNAKE_CASE_									=						16

    if args.gpus > 1:
        SCREAMING_SNAKE_CASE_									=						'auto'
        SCREAMING_SNAKE_CASE_									=						'ddp'

    SCREAMING_SNAKE_CASE_									=						args.accumulate_grad_batches
    SCREAMING_SNAKE_CASE_									=						None
    SCREAMING_SNAKE_CASE_									=						'auto'

    SCREAMING_SNAKE_CASE_									=						pl.Trainer.from_argparse_args(
        _SCREAMING_SNAKE_CASE ,   weights_summary=_SCREAMING_SNAKE_CASE ,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,   logger=_SCREAMING_SNAKE_CASE ,   val_check_interval=1 ,   num_sanity_val_steps=2 ,   **_SCREAMING_SNAKE_CASE ,   )

    if args.do_train:
        trainer.fit(_SCREAMING_SNAKE_CASE       )

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





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__  :      int			=    Lock()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,   10       ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            SCREAMING_SNAKE_CASE_									=						min(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            SCREAMING_SNAKE_CASE_									=						max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe()       )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    SCREAMING_SNAKE_CASE_									=						Pipe()
    SCREAMING_SNAKE_CASE_									=						Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
    SCREAMING_SNAKE_CASE_									=						temp_rs
    SCREAMING_SNAKE_CASE_									=						temp_rr

    for i in range(1 ,   len(_SCREAMING_SNAKE_CASE       ) - 1       ):
        SCREAMING_SNAKE_CASE_									=						Pipe()
        SCREAMING_SNAKE_CASE_									=						Pipe()
        process_array_.append(
            Process(
                target=_SCREAMING_SNAKE_CASE ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,   )       )
        SCREAMING_SNAKE_CASE_									=						temp_rs
        SCREAMING_SNAKE_CASE_									=						temp_rr

    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(
                len(_SCREAMING_SNAKE_CASE       ) - 1,
                arr[len(_SCREAMING_SNAKE_CASE       ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(_SCREAMING_SNAKE_CASE       ) - 1],
            ) ,   )       )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,   len(_SCREAMING_SNAKE_CASE       )       ):
        SCREAMING_SNAKE_CASE_									=						result_pipe[p][0].recv()
        process_array_[p].join()
    return arr


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						list(range(10 ,   0 ,   -1       )       )
    print('Initial List'       )
    print(*_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						odd_even_transposition(_SCREAMING_SNAKE_CASE       )
    print('Sorted List\n'       )
    print(*_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()


