





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 SCREAMING_SNAKE_CASE_ (					pl.LightningModule   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase="base"	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	,  ):
										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	)
										lowerCamelCase__       =       0
										lowerCamelCase__       =       Path(self.hparams.output_dir	)
										lowerCamelCase__       =       self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															lowerCamelCase__       =       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:
															lowerCamelCase__       =       config

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





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       self.model_type.from_pretrained(*_lowerCAmelCase	,  **_lowerCAmelCase	)





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model
										lowerCamelCase__       =       ["bias", "LayerNorm.weight"]
										lowerCamelCase__       =       [
										    {
										        "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:
															lowerCamelCase__       =       Adafactor(
															    _lowerCAmelCase	,  lr=self.hparams.learning_rate	,  scale_parameter=_lowerCAmelCase	,  relative_step=_lowerCAmelCase	)

										else:
															lowerCamelCase__       =       AdamW(
															    _lowerCAmelCase	,  lr=self.hparams.learning_rate	,  eps=self.hparams.adam_epsilon	)
										lowerCamelCase__       =       optimizer

										lowerCamelCase__       =       self.get_lr_scheduler()

										return [optimizer], [scheduler]





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return self.validation_step(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										return self.validation_end(_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       max(1	,  self.hparams.gpus	)  # TODO: consider num_tpu_cores
										lowerCamelCase__       =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										if stage == "test":
															lowerCamelCase__       =       len(self.test_dataloader().dataset	)
										else:
															lowerCamelCase__       =       self.get_dataloader("train"	,  self.hparams.train_batch_size	,  shuffle=_lowerCAmelCase	)
															lowerCamelCase__       =       len(self.train_dataloader().dataset	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = False	):
										raise NotImplementedError("You must implement this for your task"	)





					def 					__magic_name__ ( self	):
										return self.train_loader





					def 					__magic_name__ ( self	):
										return self.get_dataloader("dev"	,  self.hparams.eval_batch_size	,  shuffle=_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										return self.get_dataloader("test"	,  self.hparams.eval_batch_size	,  shuffle=_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										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 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.output_dir.joinpath("best_tfmr"	)
										lowerCamelCase__       =       self.step_count
										self.model.save_pretrained(_lowerCAmelCase	)
										self.tokenizer.save_pretrained(_lowerCAmelCase	)






					@staticmethod
					def 					__magic_name__ ( _lowerCAmelCase	,  _lowerCAmelCase	):
										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=32	,  type=_lowerCAmelCase	)
										parser.add_argument("--eval_batch_size"	,  default=32	,  type=_lowerCAmelCase	)
										parser.add_argument("--adafactor"	,  action="store_true"	)

class SCREAMING_SNAKE_CASE_ (					pl.Callback   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										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   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
															if param.grad is None:
																				print(_lowerCAmelCase	)

class SCREAMING_SNAKE_CASE_ (					pl.Callback   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       trainer.lr_schedulers[0]["scheduler"]
										lowerCamelCase__       =       {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										rank_zero_info("***** Validation results *****"	)
										lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										rank_zero_info("***** Test results *****"	)
										lowerCamelCase__       =       trainer.callback_metrics
										# Log and save results to file
										lowerCamelCase__       =       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    __UpperCamelCase      (    a,     a) ->None:
					#  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    __UpperCamelCase      (    a,     a,     a=None,     a=True,     a=[],     a=None,     a=None,     **a,     ) ->Union[str, Any]:
					pl.seed_everything(args.seed)

					# init model
					lowerCamelCase__       =       Path(model.hparams.output_dir)
					odir.mkdir(exist_ok=a)

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

					lowerCamelCase__       =       {}

					if args.fpaa:
										lowerCamelCase__       =       16

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

					lowerCamelCase__       =       args.accumulate_grad_batches
					lowerCamelCase__       =       None
					lowerCamelCase__       =       "auto"

					lowerCamelCase__       =       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








from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

