'''simple docstring'''




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


_lowercase						: Optional[int]						=						logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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





class     __magic_name__						(    pl.LightningModule):







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

										self.save_hyperparameters(lowercase_    )
										lowercase_					:			Dict										=       0
										lowercase_					:			Any										=       Path(self.hparams.output_dir    )
										lowercase_					:			Union[str, Any]										=       self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																	lowercase_					:			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=lowercase_				,			**lowercase_				,			)
										else:
																	lowercase_					:			PretrainedConfig										=       config

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

										if tokenizer is None:
																	lowercase_					:			str										=       AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,			cache_dir=lowercase_				,			)
										else:
																	lowercase_					:			PreTrainedTokenizer										=       tokenizer
										lowercase_					:			Tuple										=       MODEL_MODES[mode]
										if model is None:
																	lowercase_					:			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=lowercase_				,			)
										else:
																	lowercase_					:			Union[str, Any]										=       model







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			*lowercase_				:  Dict				,			**lowercase_				:  str    ):
										lowercase_					:			str										=       self.model_type.from_pretrained(*lowercase_				,			**lowercase_    )







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Any										=       self.model
										lowercase_					:			Dict										=       ["""bias""", """LayerNorm.weight"""]
										lowercase_					:			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:
																	lowercase_					:			Optional[int]										=       Adafactor(
																	    lowercase_				,			lr=self.hparams.learning_rate				,			scale_parameter=lowercase_				,			relative_step=lowercase_    )

										else:
																	lowercase_					:			Dict										=       AdamW(
																	    lowercase_				,			lr=self.hparams.learning_rate				,			eps=self.hparams.adam_epsilon    )
										lowercase_					:			List[str]										=       optimizer

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

										return [optimizer], [scheduler]







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  int    ):
										return self.validation_step(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Union[str, Any]    ):
										return self.validation_end(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Union[str, Any]										=       max(1				,			self.hparams.gpus    )  # TODO: consider num_tpu_cores
										lowercase_					:			List[str]										=       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Optional[Any]    ):
										if stage == "test":
																	lowercase_					:			Any										=       len(self.test_dataloader().dataset    )
										else:
																	lowercase_					:			List[Any]										=       self.get_dataloader("""train"""				,			self.hparams.train_batch_size				,			shuffle=lowercase_    )
																	lowercase_					:			Union[str, Any]										=       len(self.train_dataloader().dataset    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  str				,			lowercase_				:  int				,			lowercase_				:  bool = False    ):
										raise NotImplementedError("""You must implement this for your task"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										return self.train_loader







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										return self.get_dataloader("""dev"""				,			self.hparams.eval_batch_size				,			shuffle=lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										return self.get_dataloader("""test"""				,			self.hparams.eval_batch_size				,			shuffle=lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  List[Any]    ):
										return os.path.join(
										    self.hparams.data_dir				,			"""cached_{}_{}_{}""".format(
										        lowercase_				,			list(filter(lowercase_				,			self.hparams.model_name_or_path.split("""/"""    )    )    ).pop()				,			str(self.hparams.max_seq_length    )				,			)				,			)







			@pl.utilities.rank_zero_only
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Dict[str, Any]    ):
										lowercase_					:			List[Any]										=       self.output_dir.joinpath("""best_tfmr"""    )
										lowercase_					:			Dict										=       self.step_count
										self.model.save_pretrained(lowercase_    )
										self.tokenizer.save_pretrained(lowercase_    )







			@staticmethod
			def 	SCREAMING_SNAKE_CASE_     (	lowercase_				:  Optional[int]				,			lowercase_				:  Dict    ):
										parser.add_argument(
										    """--model_name_or_path"""				,			default=lowercase_				,			type=lowercase_				,			required=lowercase_				,			help="""Path to pretrained model or model identifier from huggingface.co/models"""				,			)
										parser.add_argument(
										    """--config_name"""				,			default=""""""				,			type=lowercase_				,			help="""Pretrained config name or path if not the same as model_name"""    )
										parser.add_argument(
										    """--tokenizer_name"""				,			default=lowercase_				,			type=lowercase_				,			help="""Pretrained tokenizer name or path if not the same as model_name"""				,			)
										parser.add_argument(
										    """--cache_dir"""				,			default=str(Path(lowercase_    ).parent / """test_run""" / """cache"""    )				,			type=lowercase_				,			help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,			)
										parser.add_argument(
										    """--encoder_layerdrop"""				,			type=lowercase_				,			help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,			)
										parser.add_argument(
										    """--decoder_layerdrop"""				,			type=lowercase_				,			help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,			)
										parser.add_argument(
										    """--dropout"""				,			type=lowercase_				,			help="""Dropout probability (Optional). Goes into model.config"""				,			)
										parser.add_argument(
										    """--attention_dropout"""				,			type=lowercase_				,			help="""Attention dropout probability (Optional). Goes into model.config"""				,			)
										parser.add_argument("""--learning_rate"""				,			default=5E-5				,			type=lowercase_				,			help="""The initial learning rate for Adam."""    )
										parser.add_argument(
										    """--lr_scheduler"""				,			default="""linear"""				,			choices=lowercase_				,			metavar=lowercase_				,			type=lowercase_				,			help="""Learning rate scheduler"""				,			)
										parser.add_argument("""--weight_decay"""				,			default=0.0				,			type=lowercase_				,			help="""Weight decay if we apply some."""    )
										parser.add_argument("""--adam_epsilon"""				,			default=1E-8				,			type=lowercase_				,			help="""Epsilon for Adam optimizer."""    )
										parser.add_argument("""--warmup_steps"""				,			default=0				,			type=lowercase_				,			help="""Linear warmup over warmup_steps."""    )
										parser.add_argument("""--num_workers"""				,			default=4				,			type=lowercase_				,			help="""kwarg passed to DataLoader"""    )
										parser.add_argument("""--num_train_epochs"""				,			dest="""max_epochs"""				,			default=3				,			type=lowercase_    )
										parser.add_argument("""--train_batch_size"""				,			default=32				,			type=lowercase_    )
										parser.add_argument("""--eval_batch_size"""				,			default=32				,			type=lowercase_    )
										parser.add_argument("""--adafactor"""				,			action="""store_true"""    )





class     __magic_name__						(    pl.Callback):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[Any]				,			lowercase_				:  List[str]    ):
										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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  int    ):
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
																	if param.grad is None:
																								print(lowercase_    )





class     __magic_name__						(    pl.Callback):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  str				,			lowercase_				:  Any    ):
										lowercase_					:			str										=       trainer.lr_schedulers[0]["""scheduler"""]
										lowercase_					:			Union[str, Any]										=       {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
										pl_module.logger.log_metrics(lowercase_    )







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







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




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




def 							lowerCamelCase	(      UpperCAmelCase__       :					BaseTransformer      ,      UpperCAmelCase__       :					argparse.Namespace      ,      UpperCAmelCase__       :					Optional[int]=None      ,      UpperCAmelCase__       :					Tuple=True      ,      UpperCAmelCase__       :					Tuple=[]      ,      UpperCAmelCase__       :					Union[str, Any]=None      ,      UpperCAmelCase__       :					Optional[Any]=None      ,      **UpperCAmelCase__       :					Tuple      ,      )			->      Dict:
							pl.seed_everything(args.seed )

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

							# add custom checkpoints
							if checkpoint_callback is None:
														lowercase_					:			int										=       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(UpperCAmelCase__ )
							if logging_callback is None:
														lowercase_					:			Optional[int]										=       LoggingCallback()

							lowercase_					:			Any										=       {}

							if args.fpaa:
														lowercase_					:			Union[str, Any]										=       16

							if args.gpus > 1:
														lowercase_					:			Optional[Any]										=       """auto"""
														lowercase_					:			Any										=       """ddp"""

							lowercase_					:			List[Any]										=       args.accumulate_grad_batches
							lowercase_					:			Union[str, Any]										=       None
							lowercase_					:			List[str]										=       """auto"""

							lowercase_					:			Any										=       pl.Trainer.from_argparse_args(
							    UpperCAmelCase__      ,      weights_summary=UpperCAmelCase__      ,      callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,      logger=UpperCAmelCase__      ,      val_check_interval=1      ,      num_sanity_val_steps=2      ,      **UpperCAmelCase__      ,      )

							if args.do_train:
														trainer.fit(UpperCAmelCase__ )

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase						: Dict						=						{"configuration_mmbt": ["MMBTConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Dict						=						["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						_lowercase						: Optional[int]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





