



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 						snake_case  (		pl.LightningModule     ):



   '''simple docstring'''






   def __init__(    self							:	Optional[Any] ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	str=None ,						lowerCAmelCase							:	List[Any]="base" ,						lowerCAmelCase							:	Any=None ,						lowerCAmelCase							:	Tuple=None ,						lowerCAmelCase							:	List[str]=None ,						**lowerCAmelCase							:	int ,						) -> Optional[Any]:

    """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)
    _snake_case			:     List[str] 		=    0
    _snake_case			:     Tuple 		=    Path(self.hparams.output_dir)
    _snake_case			:     Any 		=    self.hparams.cache_dir if self.hparams.cache_dir else None
    if config is None:
     _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=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE ,						)
    else:
     _snake_case			:     PretrainedConfig 		=    config

    _snake_case			:     Union[str, 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:
     _snake_case			:     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:
     _snake_case			:     PreTrainedTokenizer 		=    tokenizer
    _snake_case			:     List[str] 		=    MODEL_MODES[mode]
    if model is None:
     _snake_case			:     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=_SCREAMING_SNAKE_CASE ,						)
    else:
     _snake_case			:     Dict 		=    model

   def    UpperCamelCase_     (    self							:	List[Any] ,						*lowerCAmelCase							:	int ,						**lowerCAmelCase							:	Union[str, Any]) -> str:

    """simple docstring"""







    _snake_case			:     int 		=    self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)

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

    """simple docstring"""







    _snake_case			:     List[Any] 		=    arg_to_scheduler[self.hparams.lr_scheduler]
    _snake_case			:     str 		=    get_schedule_func(
        self.opt ,						num_warmup_steps=self.hparams.warmup_steps ,						num_training_steps=self.total_steps())
    _snake_case			:     Any 		=    {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
    return scheduler

   def    UpperCamelCase_     (    self							:	Optional[int]) -> Optional[Any]:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.model
    _snake_case			:     List[Any] 		=    ['''bias''', '''LayerNorm.weight''']
    _snake_case			:     List[Any] 		=    [
        {
            '''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:
     _snake_case			:     Dict 		=    Adafactor(
         _SCREAMING_SNAKE_CASE ,						lr=self.hparams.learning_rate ,						scale_parameter=_SCREAMING_SNAKE_CASE ,						relative_step=_SCREAMING_SNAKE_CASE)

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

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

    return [optimizer], [scheduler]

   def    UpperCamelCase_     (    self							:	Union[str, Any] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	str) -> Optional[Any]:

    """simple docstring"""







    return self.validation_step(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	Dict) -> List[Any]:

    """simple docstring"""







    return self.validation_end(_SCREAMING_SNAKE_CASE)

   def    UpperCamelCase_     (    self							:	Tuple) -> int:

    """simple docstring"""







    _snake_case			:     Tuple 		=    max(1 ,						self.hparams.gpus)  # TODO: consider num_tpu_cores
    _snake_case			:     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							:	str ,						lowerCAmelCase							:	List[Any]) -> List[Any]:

    """simple docstring"""







    if stage == "test":
     _snake_case			:     str 		=    len(self.test_dataloader().dataset)
    else:
     _snake_case			:     Optional[int] 		=    self.get_dataloader("""train""" ,						self.hparams.train_batch_size ,						shuffle=_SCREAMING_SNAKE_CASE)
     _snake_case			:     Dict 		=    len(self.train_dataloader().dataset)

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	Tuple = False) -> Optional[Any]:

    """simple docstring"""







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

   def    UpperCamelCase_     (    self							:	List[Any]) -> Optional[Any]:

    """simple docstring"""







    return self.train_loader

   def    UpperCamelCase_     (    self							:	Optional[int]) -> str:

    """simple docstring"""







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

   def    UpperCamelCase_     (    self							:	int) -> int:

    """simple docstring"""







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

   def    UpperCamelCase_     (    self							:	int ,						lowerCAmelCase							:	int) -> 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							:	Optional[Any] ,						lowerCAmelCase							:	Optional[Any]) -> None:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    self.output_dir.joinpath("""best_tfmr""")
    _snake_case			:     List[str] 		=    self.step_count
    self.model.save_pretrained(_SCREAMING_SNAKE_CASE)
    self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)


   @staticmethod
   def    UpperCamelCase_     (    lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	Dict) -> Tuple:

    """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=str(Path(_SCREAMING_SNAKE_CASE).parent / """test_run""" / """cache""") ,						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 						snake_case  (		pl.Callback     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	Tuple ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	List[str]) -> Optional[int]:

    """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     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	str ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	int) -> int:

    """simple docstring"""







    for name, param in pl_module.model.rag.named_parameters():
     if param.grad is None:
      print(_SCREAMING_SNAKE_CASE)







class 						snake_case  (		pl.Callback     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	str) -> int:

    """simple docstring"""







    _snake_case			:     List[str] 		=    trainer.lr_schedulers[0]['''scheduler''']
    _snake_case			:     List[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							:	List[str] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Optional[int]) -> List[str]:

    """simple docstring"""







    rank_zero_info("""***** Validation results *****""")
    _snake_case			:     str 		=    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							:	Optional[Any] ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	Optional[Any]) -> Any:

    """simple docstring"""







    rank_zero_info("""***** Test results *****""")
    _snake_case			:     Optional[Any] 		=    trainer.callback_metrics
    # Log and save results to file
    _snake_case			:     Optional[int] 		=    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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		str )     ->   List[str]:
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		List[Any] ,				SCREAMING_SNAKE_CASE__				:		List[Any] ,				SCREAMING_SNAKE_CASE__				:		Optional[int]=None ,				SCREAMING_SNAKE_CASE__				:		Any=True ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any]=[] ,				SCREAMING_SNAKE_CASE__				:		List[Any]=None ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any]=None ,				**SCREAMING_SNAKE_CASE__				:		str ,				)     ->   Dict:
 pl.seed_everything(args.seed )

 # init model
 _snake_case			:     Optional[Any] 		=    Path(model.hparams.output_dir )
 odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )

 # add custom checkpoints
 if checkpoint_callback is None:
  _snake_case			:     str 		=    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:
  _snake_case			:     Optional[Any] 		=    LoggingCallback()

 _snake_case			:     Union[str, Any] 		=    {}

 if args.fpaa:
  _snake_case			:     str 		=    16

 if args.gpus > 1:
  _snake_case			:     str 		=    '''auto'''
  _snake_case			:     Optional[int] 		=    '''ddp'''

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

 _snake_case			:     Optional[int] 		=    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 math import isclose, sqrt


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					point_y / 4 / point_x
  A_      :				Union[str, Any]		     =					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
  A_      :				int		     =					(1 - normal_gradient * normal_gradient) / (
      1 + normal_gradient * normal_gradient
  )
  A_      :				List[str]		     =					(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

  # to find the next point, solve the simultaeneous equations:
  # y^2 + 4x^2 = 100
  # y - b = m * (x - a)
  # ==> A x^2 + B x + C = 0
  A_      :				List[str]		     =					outgoing_gradient**2 + 4
  A_      :				List[str]		     =					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
  A_      :				Any		     =					(point_y - outgoing_gradient * point_x) ** 2 - 100

  A_      :				str		     =					(
      -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)
  A_      :				Any		     =					(
      -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)

  # two solutions, one of which is our input point
  A_      :				int		     =					x_minus if isclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else x_plus
  A_      :				Any		     =					point_y + outgoing_gradient * (next_x - point_x)

  return next_x, next_y, outgoing_gradient


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE = 1.4 ,			SCREAMING_SNAKE_CASE = -9.6       ):
  A_      :				int		     =					0
  A_      :				float		     =					first_x_coord
  A_      :				float		     =					first_y_coord
  A_      :				float		     =					(1_0.1 - point_y) / (0.0 - point_x)

  while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
    A_     ,       A_     ,       A_      :				List[str]		     =					next_point(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    num_reflections += 1

  return num_reflections


if __name__ == "__main__":
     print(F'''{solution() = }''')




