
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_  :	str 				=					logging.getLogger(__name__)

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

lowerCamelCase_  :	Optional[int] 				=					{
    """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_  :	List[Any] 				=					{
    """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_  :	Union[str, Any] 				=					sorted(arg_to_scheduler.keys())
lowerCamelCase_  :	Tuple 				=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class     _UpperCamelCase			(   pl.LightningModule					):






			'''simple docstring'''

			def __init__(    self	:    List[Any]  ,		snake_case_	:    argparse.Namespace  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    List[str]="base"  ,		snake_case_	:    List[str]=None  ,		snake_case_	:    Dict=None  ,		snake_case_	:    List[str]=None  ,		**snake_case_	:    Tuple  ,		):
							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_ )
							UpperCamelCase_:							Union[str, Any]		=  0
							UpperCamelCase_:							List[str]		=  Path(self.hparams.output_dir )
							UpperCamelCase_:							str		=  self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
											UpperCamelCase_:							List[str]		=  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:
											UpperCamelCase_:							PretrainedConfig		=  config

							UpperCamelCase_:							Optional[Any]		=  ("""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:
											UpperCamelCase_:							Any		=  AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,		cache_dir=snake_case_  ,		)
							else:
											UpperCamelCase_:							PreTrainedTokenizer		=  tokenizer
							UpperCamelCase_:							Tuple		=  MODEL_MODES[mode]
							if model is None:
											UpperCamelCase_:							Dict		=  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:
											UpperCamelCase_:							Tuple		=  model

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		*snake_case_	:    int  ,		**snake_case_	:    str ):
							UpperCamelCase_:							Tuple		=  self.model_type.from_pretrained(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Union[str, Any]		=  arg_to_scheduler[self.hparams.lr_scheduler]
							UpperCamelCase_:							Any		=  get_schedule_func(
							    self.opt  ,		num_warmup_steps=self.hparams.warmup_steps  ,		num_training_steps=self.total_steps() )
							UpperCamelCase_:							int		=  {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
							return scheduler

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

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

							UpperCamelCase_:							List[Any]		=  self.get_lr_scheduler()

							return [optimizer], [scheduler]

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    int  ,		snake_case_	:    List[Any] ):
							return self.validation_step(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							return self.validation_end(snake_case_ )

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

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

			def  lowerCAmelCase__		(    self	:    str ):
							return self.train_loader

			def  lowerCAmelCase__		(    self	:    int ):
							return self.get_dataloader("""dev"""  ,		self.hparams.eval_batch_size  ,		shuffle=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							return self.get_dataloader("""test"""  ,		self.hparams.eval_batch_size  ,		shuffle=snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    Optional[int] ):
							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	:    List[str]  ,		snake_case_	:    Dict[str, Any] ):
							UpperCamelCase_:							Any		=  self.output_dir.joinpath("""best_tfmr""" )
							UpperCamelCase_:							Any		=  self.step_count
							self.model.save_pretrained(snake_case_ )
							self.tokenizer.save_pretrained(snake_case_ )




			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    str  ,		snake_case_	:    Dict ):
							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	:    Union[str, Any]  ,		snake_case_	:    str  ,		snake_case_	:    Optional[int] ):
							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	:    Any  ,		snake_case_	:    Dict  ,		snake_case_	:    Union[str, Any] ):
							# print(pl_module.model.rag)
							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	:    Optional[int]  ,		snake_case_	:    str  ,		snake_case_	:    List[Any] ):
							UpperCamelCase_:							Optional[Any]		=  trainer.lr_schedulers[0]["""scheduler"""]
							UpperCamelCase_:							int		=  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
							pl_module.logger.log_metrics(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    pl.Trainer  ,		snake_case_	:    pl.LightningModule ):
							rank_zero_info("""***** Validation results *****""" )
							UpperCamelCase_:							Optional[Any]		=  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	:    List[Any]  ,		snake_case_	:    pl.Trainer  ,		snake_case_	:    pl.LightningModule ):
							rank_zero_info("""***** Test results *****""" )
							UpperCamelCase_:							Tuple		=  trainer.callback_metrics
							# Log and save results to file
							UpperCamelCase_:							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  )					->		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(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       ,      )					->		List[Any]:
				pl.seed_everything(args.seed  )

				# init model
				UpperCamelCase_:							Tuple		=  Path(model.hparams.output_dir  )
				odir.mkdir(exist_ok=lowerCamelCase  )

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

				UpperCamelCase_:							Optional[Any]		=  {}

				if args.fpaa:
								UpperCamelCase_:							List[Any]		=  16

				if args.gpus > 1:
								UpperCamelCase_:							Dict		=  """auto"""
								UpperCamelCase_:							Optional[int]		=  """ddp"""

				UpperCamelCase_:							Optional[int]		=  args.accumulate_grad_batches
				UpperCamelCase_:							Optional[Any]		=  None
				UpperCamelCase_:							Dict		=  """auto"""

				UpperCamelCase_:							Tuple		=  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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Dict 				=					{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	"""luke"""

			def __init__(    self	:    Tuple  ,		snake_case_	:    List[Any]=5_0267  ,		snake_case_	:    Any=50_0000  ,		snake_case_	:    str=768  ,		snake_case_	:    int=256  ,		snake_case_	:    str=12  ,		snake_case_	:    int=12  ,		snake_case_	:    Dict=3072  ,		snake_case_	:    Optional[Any]="gelu"  ,		snake_case_	:    Dict=0.1  ,		snake_case_	:    List[str]=0.1  ,		snake_case_	:    int=512  ,		snake_case_	:    Dict=2  ,		snake_case_	:    List[Any]=0.02  ,		snake_case_	:    int=1e-12  ,		snake_case_	:    Union[str, Any]=True  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Dict=1  ,		snake_case_	:    Optional[int]=0  ,		snake_case_	:    List[str]=2  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(pad_token_id=snake_case_  ,		bos_token_id=snake_case_  ,		eos_token_id=snake_case_  ,		**snake_case_ )

							UpperCamelCase_:							Dict		=  vocab_size
							UpperCamelCase_:							Tuple		=  entity_vocab_size
							UpperCamelCase_:							Optional[int]		=  hidden_size
							UpperCamelCase_:							Any		=  entity_emb_size
							UpperCamelCase_:							str		=  num_hidden_layers
							UpperCamelCase_:							Union[str, Any]		=  num_attention_heads
							UpperCamelCase_:							Dict		=  hidden_act
							UpperCamelCase_:							Dict		=  intermediate_size
							UpperCamelCase_:							str		=  hidden_dropout_prob
							UpperCamelCase_:							List[str]		=  attention_probs_dropout_prob
							UpperCamelCase_:							int		=  max_position_embeddings
							UpperCamelCase_:							int		=  type_vocab_size
							UpperCamelCase_:							List[Any]		=  initializer_range
							UpperCamelCase_:							Union[str, Any]		=  layer_norm_eps
							UpperCamelCase_:							Tuple		=  use_entity_aware_attention
							UpperCamelCase_:							int		=  classifier_dropout






