





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       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, 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(__UpperCAmelCase	)
										_snake_case						=					0
										_snake_case						=					Path(self.hparams.output_dir	)
										_snake_case						=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															_snake_case						=					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=__UpperCAmelCase					,    **__UpperCAmelCase					,    )
										else:
															_snake_case						=					config

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

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




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				Optional[int]:
										_snake_case						=					self.model_type.from_pretrained(*__UpperCAmelCase					,    **__UpperCAmelCase	)




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




					def 						lowerCAmelCase      (		self	)		->				Dict:
										_snake_case						=					self.model
										_snake_case						=					["bias", "LayerNorm.weight"]
										_snake_case						=					[
										    {
										        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
										        "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:
															_snake_case						=					Adafactor(
															    __UpperCAmelCase					,    lr=self.hparams.learning_rate					,    scale_parameter=__UpperCAmelCase					,    relative_step=__UpperCAmelCase	)

										else:
															_snake_case						=					AdamW(
															    __UpperCAmelCase					,    lr=self.hparams.learning_rate					,    eps=self.hparams.adam_epsilon	)
										_snake_case						=					optimizer

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return self.validation_step(__UpperCAmelCase					,    __UpperCAmelCase	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				str:
										return self.validation_end(__UpperCAmelCase	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					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					,    lowerCAmelCase_	)		->				Dict:
										if stage == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=__UpperCAmelCase	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				Union[str, Any]:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				str:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=__UpperCAmelCase	)




					def 						lowerCAmelCase      (		self	)		->				str:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=__UpperCAmelCase	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Dict:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        __UpperCAmelCase					,    list(filter(__UpperCAmelCase					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				int:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(__UpperCAmelCase	)
										self.tokenizer.save_pretrained(__UpperCAmelCase	)






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





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				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       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					trainer.lr_schedulers[0]["scheduler"]
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(__UpperCAmelCase	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Union[str, Any]:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(__UpperCAmelCase	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(__UpperCAmelCase					,    str(metrics[key]	)	)	)






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Union[str, Any]:
										rank_zero_info('***** Test results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log and save results to file
										_snake_case						=					os.path.join(pl_module.hparams.output_dir					,    'test_results.txt'	)
										with open(__UpperCAmelCase					,    'w'	) as writer:
															for key in sorted(__UpperCAmelCase	):
																				if key not in ["log", "progress_bar"]:
																									rank_zero_info('{} = {}\n'.format(__UpperCAmelCase					,    str(metrics[key]	)	)	)
																									writer.write('{} = {}\n'.format(__UpperCAmelCase					,    str(metrics[key]	)	)	)





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			None:



					'''simple docstring'''




					parser.add_argument(
					    '--output_dir'							,     default=A_							,     type=A_							,     required=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=A_							,     type=A_							,     required=A_							,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'							,     )





def     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							Union[str, Any]=None							,     UpperCamelCase__      :							Tuple=True							,     UpperCamelCase__      :							Optional[int]=None							,     UpperCamelCase__      :							Optional[Any]=[]							,     UpperCamelCase__      :							Union[str, Any]=None							,     UpperCamelCase__      :							int=None							,     **UpperCamelCase__      :							List[str]							,     )      ->			str:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=A_    )

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

					_snake_case						=					{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_snake_case						=					16
										# train_params["amp_level"] = args.fp16_opt_level

					if args.gpus > 1:
										_snake_case						=					"auto"  # "ddp"
										_snake_case						=					"ddp"

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
					_snake_case						=					"auto"

					_snake_case						=					pl.Trainer.from_argparse_args(
					    A_							,     weights_summary=A_							,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,     logger=A_							,     **A_							,     )

					if args.do_train:
										trainer.fit(A_    )

					return trainer







"""simple docstring"""







from ..utils import DummyObject, requires_backends






class 		_A    (    metaclass=_a    ):







    """simple docstring"""




    UpperCAmelCase			:				int      =			["""torch""", """transformers""", """invisible_watermark"""]






    def __init__(   self							:       int							,					*__UpperCAmelCase							:       List[Any]							,					**__UpperCAmelCase							:       Optional[int]):
      requires_backends(self							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       Optional[Any]							,					*__UpperCAmelCase							:       Any							,					**__UpperCAmelCase							:       int):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       List[Any]							,					*__UpperCAmelCase							:       Optional[Any]							,					**__UpperCAmelCase							:       Tuple):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






class 		_A    (    metaclass=_a    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[int]      =			["""torch""", """transformers""", """invisible_watermark"""]






    def __init__(   self							:       int							,					*__UpperCAmelCase							:       Tuple							,					**__UpperCAmelCase							:       Tuple):
      requires_backends(self							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       int							,					*__UpperCAmelCase							:       str							,					**__UpperCAmelCase							:       Optional[int]):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       Any							,					*__UpperCAmelCase							:       Optional[int]							,					**__UpperCAmelCase							:       Union[str, Any]):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






