"""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


_UpperCamelCase								=		logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_UpperCamelCase								=		{
    "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
_UpperCamelCase								=		{
    "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
}
_UpperCamelCase								=		sorted(arg_to_scheduler.keys())
_UpperCamelCase								=		"{" + ", ".join(arg_to_scheduler_choices) + "}"
class 						__UpperCAmelCase    (pl.LightningModule     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_="base"   ,      snake_case_=None   ,      snake_case_=None   ,      snake_case_=None   ,      **snake_case_   ,      ):







										'''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(snake_case_							)
										A__       :     Tuple								=			0
										A__       :     Tuple								=			Path(self.hparams.output_dir							)
										A__       :     Any								=			self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																	A__       :     Union[str, 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=snake_case_   ,      **snake_case_   ,      )
										else:
																	A__       :     PretrainedConfig								=			config

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

										if tokenizer is None:
																	A__       :     str								=			AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,      cache_dir=snake_case_   ,      )
										else:
																	A__       :     PreTrainedTokenizer								=			tokenizer
										A__       :     str								=			MODEL_MODES[mode]
										if model is None:
																	A__       :     List[str]								=			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=snake_case_   ,      )
										else:
																	A__       :     Optional[int]								=			model





			def 		lowerCamelCase					(			self   ,      *snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.model_type.from_pretrained(*snake_case_   ,      **snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.model
										A__       :     Optional[Any]								=			["""bias""", """LayerNorm.weight"""]
										A__       :     List[str]								=			[
										    {
										        """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:
																	A__       :     Optional[Any]								=			Adafactor(
																	    snake_case_   ,      lr=self.hparams.learning_rate   ,      scale_parameter=snake_case_   ,      relative_step=snake_case_							)

										else:
																	A__       :     str								=			AdamW(
																	    snake_case_   ,      lr=self.hparams.learning_rate   ,      eps=self.hparams.adam_epsilon							)
										A__       :     str								=			optimizer

										A__       :     Dict								=			self.get_lr_scheduler()

										return [optimizer], [scheduler]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										return self.validation_step(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.validation_end(snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			max(1   ,      self.hparams.gpus							)  # TODO: consider num_tpu_cores
										A__       :     Optional[int]								=			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										if stage == "test":
																	A__       :     Optional[Any]								=			len(self.test_dataloader().dataset							)
										else:
																	A__       :     int								=			self.get_dataloader("""train"""   ,      self.hparams.train_batch_size   ,      shuffle=snake_case_							)
																	A__       :     int								=			len(self.train_dataloader().dataset							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_ = False							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.train_loader





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return os.path.join(
										    self.hparams.data_dir   ,      """cached_{}_{}_{}""".format(
										        snake_case_   ,      list(filter(snake_case_   ,      self.hparams.model_name_or_path.split("""/"""							)							)							).pop()   ,      str(self.hparams.max_seq_length							)   ,      )   ,      )





			@pl.utilities.rank_zero_only
			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Any								=			self.output_dir.joinpath("""best_tfmr"""							)
										A__       :     Any								=			self.step_count
										self.model.save_pretrained(snake_case_							)
										self.tokenizer.save_pretrained(snake_case_							)





			@staticmethod
			def 		lowerCamelCase					(			snake_case_   ,      snake_case_							):







										'''simple docstring'''


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





class 						__UpperCAmelCase    (pl.Callback     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''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 						__UpperCAmelCase    (pl.Callback     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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





class 						__UpperCAmelCase    (pl.Callback     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			trainer.lr_schedulers[0]["""scheduler"""]
										A__       :     Any								=			{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
										pl_module.logger.log_metrics(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										rank_zero_info("""***** Validation results *****"""							)
										A__       :     Optional[int]								=			trainer.callback_metrics
										# Log results
										for key in sorted(snake_case_							):
																	if key not in ["log", "progress_bar"]:
																								rank_zero_info("""{} = {}\n""".format(snake_case_   ,      str(metrics[key]							)							)							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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





def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							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  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=None			,  lowerCAmelCase=True			,  lowerCAmelCase=[]			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  **lowerCAmelCase			,  ):
							pl.seed_everything(args.seed							)

							# init model
							A__       :     Union[str, Any]								=			Path(model.hparams.output_dir							)
							odir.mkdir(exist_ok=lowerCAmelCase							)

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

							A__       :     Optional[Any]								=			{}

							if args.fpaa:
														A__       :     List[str]								=			16

							if args.gpus > 1:
														A__       :     Optional[int]								=			"""auto"""
														A__       :     Tuple								=			"""ddp"""

							A__       :     int								=			args.accumulate_grad_batches
							A__       :     Union[str, Any]								=			None
							A__       :     Any								=			"""auto"""

							A__       :     List[str]								=			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






"""simple docstring"""



from typing import List

from .keymap import KEYMAP, get_character

def  _A(		lowerCAmelCase							):

							def decorator(lowerCAmelCase							):
														A__       :     Any								=			getattr(lowerCAmelCase			,  """handle_key"""			,  []							)
														handle += [key]
														setattr(lowerCAmelCase			,  """handle_key"""			,  lowerCAmelCase							)
														return func

							return decorator

def  _A(		*lowerCAmelCase							):

							def decorator(lowerCAmelCase							):
														A__       :     Optional[Any]								=			getattr(lowerCAmelCase			,  """handle_key"""			,  []							)
														handle += keys
														setattr(lowerCAmelCase			,  """handle_key"""			,  lowerCAmelCase							)
														return func

							return decorator
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __new__(			cls   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			super().__new__(cls   ,      snake_case_   ,      snake_case_   ,      snake_case_							)
										if not hasattr(snake_case_   ,      """key_handler"""							):
																	setattr(snake_case_   ,      """key_handler"""   ,      {}							)
										setattr(snake_case_   ,      """handle_input"""   ,      KeyHandler.handle_input							)

										for value in attrs.values():
																	A__       :     Dict								=			getattr(snake_case_   ,      """handle_key"""   ,      []							)
																	for key in handled_keys:
																								A__       :     Optional[Any]								=			value
										return new_cls





			@staticmethod
			def 		lowerCamelCase					(			cls							):







										'''simple docstring'''


										A__       :     int								=			get_character()
										if char != KEYMAP["undefined"]:
																	A__       :     Union[str, Any]								=			ord(snake_case_							)
										A__       :     int								=			cls.key_handler.get(snake_case_							)
										if handler:
																	A__       :     int								=			char
																	return handler(cls							)
										else:
																	return None





def  _A(		cls							):
							return KeyHandler(cls.__name__			,  cls.__bases__			,  cls.__dict__.copy()							)





