
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 						__magic_name__    (       pl.LightningModule    ):


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

   self.save_hyperparameters(lowerCAmelCase__    )
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  Path(self.hparams.output_dir    )
   UpperCAmelCase					      =  self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
     UpperCAmelCase					      =  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=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
   else:
     UpperCAmelCase					      =  config

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

   if tokenizer is None:
     UpperCAmelCase					      =  AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,						cache_dir=lowerCAmelCase__			,						)
   else:
     UpperCAmelCase					      =  tokenizer
   UpperCAmelCase					      =  MODEL_MODES[mode]
   if model is None:
     UpperCAmelCase					      =  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=lowerCAmelCase__			,						)
   else:
     UpperCAmelCase					      =  model


 def _UpperCamelCase	(						self		: int			,						*lowerCAmelCase__		: List[Any]			,						**lowerCAmelCase__		: Optional[int]    )    ->      Tuple:
   UpperCAmelCase					      =  self.model_type.from_pretrained(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      List[str]:
   UpperCAmelCase					      =  arg_to_scheduler[self.hparams.lr_scheduler]
   UpperCAmelCase					      =  get_schedule_func(
       self.opt			,						num_warmup_steps=self.hparams.warmup_steps			,						num_training_steps=self.total_steps()    )
   UpperCAmelCase					      =  {"scheduler": scheduler, "interval": "step", "frequency": 1}
   return scheduler


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  self.model
   UpperCAmelCase					      =  ["bias", "LayerNorm.weight"]
   UpperCAmelCase					      =  [
       {
           "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:
     UpperCAmelCase					      =  Adafactor(
         lowerCAmelCase__			,						lr=self.hparams.learning_rate			,						scale_parameter=lowerCAmelCase__			,						relative_step=lowerCAmelCase__    )

   else:
     UpperCAmelCase					      =  AdamW(
         lowerCAmelCase__			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon    )
   UpperCAmelCase					      =  optimizer

   UpperCAmelCase					      =  self.get_lr_scheduler()

   return [optimizer], [scheduler]


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Optional[Any]    )    ->      Optional[int]:
   return self.validation_step(lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: Tuple    )    ->      List[str]:
   return self.validation_end(lowerCAmelCase__    )


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


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


 def _UpperCamelCase	(						self		: str    )    ->      Optional[int]:
   return self.train_loader


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Optional[Any]:
   return self.get_dataloader("dev"			,						self.hparams.eval_batch_size			,						shuffle=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   return self.get_dataloader("test"			,						self.hparams.eval_batch_size			,						shuffle=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Tuple    )    ->      str:
   return os.path.join(
       self.hparams.data_dir			,						"cached_{}_{}_{}".format(
           lowerCAmelCase__			,						list(filter(lowerCAmelCase__			,						self.hparams.model_name_or_path.split("/"    )    )    ).pop()			,						str(self.hparams.max_seq_length    )			,						)			,						)


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


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






class 						__magic_name__    (       pl.Callback    ):


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Tuple    )    ->      Any:
   if (
       trainer.is_global_zero and trainer.global_rank == 0
   ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
     pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 						__magic_name__    (       pl.Callback    ):


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






class 						__magic_name__    (       pl.Callback    ):


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: Tuple    )    ->      int:
   UpperCAmelCase					      =  trainer.lr_schedulers[0]["scheduler"]
   UpperCAmelCase					      =  {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
   pl_module.logger.log_metrics(lowerCAmelCase__    )


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


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: pl.Trainer			,						lowerCAmelCase__		: pl.LightningModule    )    ->      Optional[int]:
   rank_zero_info("***** Test results *****"    )
   UpperCAmelCase					      =  trainer.callback_metrics
   # Log and save results to file
   UpperCAmelCase					      =  os.path.join(pl_module.hparams.output_dir			,						"test_results.txt"    )
   with open(lowerCAmelCase__			,						"w"    ) as writer:
     for key in sorted(lowerCAmelCase__    ):
       if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(lowerCAmelCase__			,						str(metrics[key]    )    )    )
         writer.write("{} = {}\n".format(lowerCAmelCase__			,						str(metrics[key]    )    )    )

def      _lowerCAmelCase(	__A   ,       __A			):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      "--output_dir"   ,       default=str(Path(__A			).parent / "test_run" / "model_checkpoints"			)   ,       type=__A   ,       help="The output directory where the model predictions and checkpoints will be written."   ,       )
  parser.add_argument(
      "--fp16"   ,       action="store_true"   ,       help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,       )

  parser.add_argument(
      "--fp16_opt_level"   ,       type=__A   ,       default="O2"   ,       help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )   ,       )
  parser.add_argument("--n_tpu_cores"   ,       dest="tpu_cores"   ,       type=__A			)
  parser.add_argument("--max_grad_norm"   ,       dest="gradient_clip_val"   ,       default=1.0   ,       type=__A   ,       help="Max gradient norm"			)
  parser.add_argument("--do_train"   ,       action="store_true"   ,       help="Whether to run training."			)
  parser.add_argument("--do_predict"   ,       action="store_true"   ,       help="Whether to run predictions on the test set."			)
  parser.add_argument(
      "--gradient_accumulation_steps"   ,       dest="accumulate_grad_batches"   ,       type=__A   ,       default=1   ,       help="Number of updates steps to accumulate before performing a backward/update pass."   ,       )
  parser.add_argument("--seed"   ,       type=__A   ,       default=42   ,       help="random seed for initialization"			)
  parser.add_argument(
      "--data_dir"   ,       default=str(Path(__A			).parent / "test_run" / "dummy-train-data"			)   ,       type=__A   ,       help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,       )





def      _lowerCAmelCase(	__A   ,       __A   ,       __A=None   ,       __A=True   ,       __A=[]   ,       __A=None   ,       __A=None   ,       **__A   ,       ):
  pl.seed_everything(args.seed			)

  # init model
  UpperCAmelCase					      =  Path(model.hparams.output_dir			)
  odir.mkdir(exist_ok=__A			)

  # add custom checkpoints
  if checkpoint_callback is None:
    UpperCAmelCase					      =  pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir   ,       prefix="checkpoint"   ,       monitor="val_loss"   ,       mode="min"   ,       save_top_k=1			)
  if early_stopping_callback:
    extra_callbacks.append(__A			)
  if logging_callback is None:
    UpperCAmelCase					      =  LoggingCallback()

  UpperCAmelCase					      =  {}

  if args.fpaa:
    UpperCAmelCase					      =  16

  if args.gpus > 1:
    UpperCAmelCase					      =  "auto"
    UpperCAmelCase					      =  "ddp"

  UpperCAmelCase					      =  args.accumulate_grad_batches
  UpperCAmelCase					      =  None
  UpperCAmelCase					      =  "auto"

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

  if args.do_train:
    trainer.fit(__A			)

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






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



