



import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
      from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
      from tensorflow.keras.optimizers import Adam

class       _SCREAMING_SNAKE_CASE	(tf.keras.optimizers.schedules.LearningRateSchedule ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							float     ,  UpperCamelCase :							Callable     ,  UpperCamelCase :							int     ,  UpperCamelCase :							float = 1.0     ,  UpperCamelCase :							str = None     ,  )->Optional[int]:
      super().__init__()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	initial_learning_rate
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	warmup_steps
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	power
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	decay_schedule_fn
      __SCREAMING_SNAKE_CASE       :    int       =	name





     def __call__(       self :							List[Any]     ,  UpperCamelCase :							Any				)->Optional[int]:
      with tf.name_scope(self.name or "WarmUp"				) as name:
       # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
       # learning rate will be `global_step/num_warmup_steps * init_lr`.
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.cast(UpperCamelCase     ,  tf.floataa				)
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.cast(self.warmup_steps     ,  tf.floataa				)
       __SCREAMING_SNAKE_CASE       :    Any       =	global_step_float / warmup_steps_float
       __SCREAMING_SNAKE_CASE       :    Tuple       =	self.initial_learning_rate * tf.math.pow(UpperCamelCase     ,  self.power				)
       return tf.cond(
           global_step_float < warmup_steps_float     ,  lambda: warmup_learning_rate     ,  lambda: self.decay_schedule_fn(step - self.warmup_steps				)     ,  name=UpperCamelCase     ,  )






     def 	__snake_case    (       self :							str				)->List[str]:
      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_schedule_fn": self.decay_schedule_fn,
          "warmup_steps": self.warmup_steps,
          "power": self.power,
          "name": self.name,
      }






def 	_lowerCAmelCase						(				__lowerCamelCase   :     float		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     float = 0.0		,	__lowerCamelCase   :     float = 0.9		,	__lowerCamelCase   :     float = 0.999		,	__lowerCamelCase   :     float = 1E-8		,	__lowerCamelCase   :     Optional[float] = None		,	__lowerCamelCase   :     Optional[float] = None		,	__lowerCamelCase   :     float = 0.0		,	__lowerCamelCase   :     float = 1.0		,	__lowerCamelCase   :     Optional[List[str]] = None		,	):
 __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=__lowerCamelCase		,	decay_steps=num_train_steps - num_warmup_steps		,	end_learning_rate=init_lr * min_lr_ratio		,	power=__lowerCamelCase		,	)
 if num_warmup_steps:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	WarmUp(
      initial_learning_rate=__lowerCamelCase		,	decay_schedule_fn=__lowerCamelCase		,	warmup_steps=__lowerCamelCase		,	)
 if weight_decay_rate > 0.0:
  __SCREAMING_SNAKE_CASE       :    int       =	AdamWeightDecay(
      learning_rate=__lowerCamelCase		,	weight_decay_rate=__lowerCamelCase		,	beta_a=__lowerCamelCase		,	beta_a=__lowerCamelCase		,	epsilon=__lowerCamelCase		,	clipnorm=__lowerCamelCase		,	global_clipnorm=__lowerCamelCase		,	exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]		,	include_in_weight_decay=__lowerCamelCase		,	)
 else:
  __SCREAMING_SNAKE_CASE       :    Any       =	tf.keras.optimizers.Adam(
      learning_rate=__lowerCamelCase		,	beta_a=__lowerCamelCase		,	beta_a=__lowerCamelCase		,	epsilon=__lowerCamelCase		,	clipnorm=__lowerCamelCase		,	global_clipnorm=__lowerCamelCase		,	)
 # We return the optimizer and the LR scheduler in order to better track the
 # evolution of the LR independently of the optimizer.
 return optimizer, lr_schedule

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1     ,  UpperCamelCase :							float = 0.9     ,  UpperCamelCase :							float = 0.9_9_9     ,  UpperCamelCase :							float = 1E-7     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							float = 0.0     ,  UpperCamelCase :							Optional[List[str]] = None     ,  UpperCamelCase :							Optional[List[str]] = None     ,  UpperCamelCase :							str = "AdamWeightDecay"     ,  **UpperCamelCase :							int     ,  )->List[Any]:
      super().__init__(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  **UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	weight_decay_rate
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	include_in_weight_decay
      __SCREAMING_SNAKE_CASE       :    Any       =	exclude_from_weight_decay





     @classmethod
     def 	__snake_case    (       cls :							Any     ,  UpperCamelCase :							Optional[int]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	{"WarmUp": WarmUp}
      return super(UpperCamelCase     ,  cls				).from_config(UpperCamelCase     ,  custom_objects=UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							int				)->int:
      super(UpperCamelCase     ,  self				)._prepare_local(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.constant(
          self.weight_decay_rate     ,  name="adam_weight_decay_rate"				)





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self._do_use_weight_decay(var.name				)
      if do_decay:
       return var.assign_sub(
           learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]     ,  use_locking=self._use_locking     ,  )
      return tf.no_op()





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Optional[int]=None     ,  **UpperCamelCase :							str				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    int       =	list(zip(*UpperCamelCase				)				)
      return super(UpperCamelCase     ,  self				).apply_gradients(zip(UpperCamelCase     ,  UpperCamelCase				)     ,  name=UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Any				)->Optional[Any]:
      if apply_state is None:
       return self._decayed_lr_t[var_dtype], {}

      __SCREAMING_SNAKE_CASE       :    Any       =	apply_state or {}
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	apply_state.get((var_device, var_dtype)				)
      if coefficients is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	self._fallback_apply_state(UpperCamelCase     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    List[str]       =	coefficients

      return coefficients["lr_t"], {"apply_state": apply_state}





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							int=None				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    str       =	self._get_lr(var.device     ,  var.dtype.base_dtype     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	self._decay_weights_op(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)
      with tf.control_dependencies([decay]				):
       return super(UpperCamelCase     ,  self				)._resource_apply_dense(UpperCamelCase     ,  UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							List[str]=None				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	self._get_lr(var.device     ,  var.dtype.base_dtype     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self._decay_weights_op(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)
      with tf.control_dependencies([decay]				):
       return super(UpperCamelCase     ,  self				)._resource_apply_sparse(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	super().get_config()
      config.update({"weight_decay_rate": self.weight_decay_rate}				)
      return config






     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Optional[Any]				)->Tuple:
      if self.weight_decay_rate == 0:
       return False

      if self._include_in_weight_decay:
       for r in self._include_in_weight_decay:
        if re.search(UpperCamelCase     ,  UpperCamelCase				) is not None:
         return True

      if self._exclude_from_weight_decay:
       for r in self._exclude_from_weight_decay:
        if re.search(UpperCamelCase     ,  UpperCamelCase				) is not None:
         return False
      return True






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	[]
      __SCREAMING_SNAKE_CASE       :    Dict       =	None





     @property
     def 	__snake_case    (       self :							str				)->str:
      if self._accum_steps is None:
       __SCREAMING_SNAKE_CASE       :    Dict       =	tf.Variable(
           tf.constant(0     ,  dtype=tf.intaa				)     ,  trainable=UpperCamelCase     ,  synchronization=tf.VariableSynchronization.ON_READ     ,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,  )

      return self._accum_steps.value()





     @property
     def 	__snake_case    (       self :							Any				)->int:
      if not self._gradients:
       raise ValueError("The accumulator should be called first to initialize the gradients"				)
      return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]





     def __call__(       self :							str     ,  UpperCamelCase :							int				)->Optional[Any]:
      if not self._gradients:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.step  # Create the step variable.
       self._gradients.extend(
           [
               tf.Variable(
                   tf.zeros_like(UpperCamelCase				)     ,  trainable=UpperCamelCase     ,  synchronization=tf.VariableSynchronization.ON_READ     ,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,  )
               if gradient is not None
               else gradient
               for gradient in gradients
           ]				)
      if len(UpperCamelCase				) != len(self._gradients				):
       raise ValueError(F"""Expected {len(self._gradients				)} gradients, but got {len(UpperCamelCase				)}"""				)

      for accum_gradient, gradient in zip(self._gradients     ,  UpperCamelCase				):
       if accum_gradient is not None and gradient is not None:
        accum_gradient.assign_add(UpperCamelCase				)

      self._accum_steps.assign_add(1				)






     def 	__snake_case    (       self :							Any				)->Any:
      if not self._gradients:
       return
      self._accum_steps.assign(0				)
      for gradient in self._gradients:
       if gradient is not None:
        gradient.assign(tf.zeros_like(UpperCamelCase				)				)











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


_lowerCamelCase						    =  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase						    =  {
    """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
_lowerCamelCase						    =  {
    """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
}
_lowerCamelCase						    =  sorted(arg_to_scheduler.keys())
_lowerCamelCase						    =  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class       _SCREAMING_SNAKE_CASE	(pl.LightningModule ):





     def __init__(       self :							Dict     ,  UpperCamelCase :							argparse.Namespace     ,  UpperCamelCase :							str=None     ,  UpperCamelCase :							Tuple="base"     ,  UpperCamelCase :							Optional[Any]=None     ,  UpperCamelCase :							List[Any]=None     ,  UpperCamelCase :							Optional[int]=None     ,  **UpperCamelCase :							int     ,  )->Dict:
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	0
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Path(self.hparams.output_dir				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	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=UpperCamelCase     ,  **UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PretrainedConfig       =	config

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

      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=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PreTrainedTokenizer       =	tokenizer
      __SCREAMING_SNAKE_CASE       :    str       =	MODEL_MODES[mode]
      if model is None:
       __SCREAMING_SNAKE_CASE       :    Optional[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=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model





     def 	__snake_case    (       self :							Dict     ,  *UpperCamelCase :							Dict     ,  **UpperCamelCase :							Union[str, Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_type.from_pretrained(*UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	arg_to_scheduler[self.hparams.lr_scheduler]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	get_schedule_func(
          self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler





     def 	__snake_case    (       self :							int				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model
      __SCREAMING_SNAKE_CASE       :    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       :    Dict       =	Adafactor(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  scale_parameter=UpperCamelCase     ,  relative_step=UpperCamelCase				)

      else:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	AdamW(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	optimizer

      __SCREAMING_SNAKE_CASE       :    Any       =	self.get_lr_scheduler()

      return [optimizer], [scheduler]





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Any				)->Optional[Any]:
      return self.validation_step(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Tuple				)->List[Any]:
      return self.validation_end(UpperCamelCase				)





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	max(1     ,  self.hparams.gpus				)  # TODO: consider num_tpu_cores
      __SCREAMING_SNAKE_CASE       :    List[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 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Optional[Any]:
      if stage == "test":
       __SCREAMING_SNAKE_CASE       :    List[str]       =	len(self.test_dataloader().dataset				)
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_dataloader("train"     ,  self.hparams.train_batch_size     ,  shuffle=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	len(self.train_dataloader().dataset				)





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							int     ,  UpperCamelCase :							bool = False				)->List[Any]:
      raise NotImplementedError("You must implement this for your task"				)





     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      return self.train_loader





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      return self.get_dataloader("dev"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Any:
      return self.get_dataloader("test"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							List[str]				)->int:
      return os.path.join(
          self.hparams.data_dir     ,  "cached_{}_{}_{}".format(
              UpperCamelCase     ,  list(filter(UpperCamelCase     ,  self.hparams.model_name_or_path.split("/"				)				)				).pop()     ,  str(self.hparams.max_seq_length				)     ,  )     ,  )





     @pl.utilities.rank_zero_only
     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Dict[str, Any]				)->None:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.output_dir.joinpath("best_tfmr"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.step_count
      self.model.save_pretrained(UpperCamelCase				)
      self.tokenizer.save_pretrained(UpperCamelCase				)






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






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Tuple				)->Tuple:
      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       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Optional[Any]				)->Tuple:
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
       if param.grad is None:
        print(UpperCamelCase				)






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Dict				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	trainer.lr_schedulers[0]["scheduler"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
      pl_module.logger.log_metrics(UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Union[str, Any]:
      rank_zero_info("***** Validation results *****"				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	trainer.callback_metrics
      # Log results
      for key in sorted(UpperCamelCase				):
       if key not in ["log", "progress_bar"]:
        rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Optional[Any]:
      rank_zero_info("***** Test results *****"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	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(UpperCamelCase     ,  "w"				) as writer:
       for key in sorted(UpperCamelCase				):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)
         writer.write("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     BaseTransformer		,	__lowerCamelCase   :     argparse.Namespace		,	__lowerCamelCase   :     Union[str, Any]=None		,	__lowerCamelCase   :     List[Any]=True		,	__lowerCamelCase   :     List[Any]=[]		,	__lowerCamelCase   :     Any=None		,	__lowerCamelCase   :     Optional[int]=None		,	**__lowerCamelCase   :     Any		,	):



 """simple docstring"""





 pl.seed_everything(args.seed     )

 # init model
 __SCREAMING_SNAKE_CASE       :    List[str]       =	Path(model.hparams.output_dir     )
 odir.mkdir(exist_ok=__lowerCamelCase     )

 # 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(__lowerCamelCase     )
 if logging_callback is None:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	LoggingCallback()

 __SCREAMING_SNAKE_CASE       :    str       =	{}

 if args.fpaa:
  __SCREAMING_SNAKE_CASE       :    Any       =	16

 if args.gpus > 1:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"auto"
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"ddp"

 __SCREAMING_SNAKE_CASE       :    List[str]       =	args.accumulate_grad_batches
 __SCREAMING_SNAKE_CASE       :    List[str]       =	None
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"auto"

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pl.Trainer.from_argparse_args(
     __lowerCamelCase		,	weights_summary=__lowerCamelCase		,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,	logger=__lowerCamelCase		,	val_check_interval=1		,	num_sanity_val_steps=2		,	**__lowerCamelCase		,	)

 if args.do_train:
  trainer.fit(__lowerCamelCase     )

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






