


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 						snake_case			(       pl.LightningModule	):
			'''simple docstring'''

			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     argparse.Namespace							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Optional[int]="base"							,							lowerCAmelCase_     :     str=None							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Dict=None							,							**lowerCAmelCase_     :     int							,							)					->					List[Any]:




								"""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(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       0
								SCREAMING_SNAKE_CASE_	      =       Path(self.hparams.output_dir					)
								SCREAMING_SNAKE_CASE_	      =       self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
													SCREAMING_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=lowerCAmelCase_							,							**lowerCAmelCase_							,							)
								else:
													SCREAMING_SNAKE_CASE_	      =       config

								SCREAMING_SNAKE_CASE_	      =       ('''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:
													SCREAMING_SNAKE_CASE_	      =       AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,							cache_dir=lowerCAmelCase_							,							)
								else:
													SCREAMING_SNAKE_CASE_	      =       tokenizer
								SCREAMING_SNAKE_CASE_	      =       MODEL_MODES[mode]
								if model is None:
													SCREAMING_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=lowerCAmelCase_							,							)
								else:
													SCREAMING_SNAKE_CASE_	      =       model

			def _lowercase (	self     :     List[str]							,							*lowerCAmelCase_     :     List[str]							,							**lowerCAmelCase_     :     Optional[Any]					)					->					Dict:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.model_type.from_pretrained(*lowerCAmelCase_							,							**lowerCAmelCase_					)

			def _lowercase (	self     :     Optional[int]					)					->					Optional[Any]:




								"""simple docstring"""






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

			def _lowercase (	self     :     Optional[Any]					)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.model
								SCREAMING_SNAKE_CASE_	      =       ['''bias''', '''LayerNorm.weight''']
								SCREAMING_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:
													SCREAMING_SNAKE_CASE_	      =       Adafactor(
													    lowerCAmelCase_							,							lr=self.hparams.learning_rate							,							scale_parameter=lowerCAmelCase_							,							relative_step=lowerCAmelCase_					)

								else:
													SCREAMING_SNAKE_CASE_	      =       AdamW(
													    lowerCAmelCase_							,							lr=self.hparams.learning_rate							,							eps=self.hparams.adam_epsilon					)
								SCREAMING_SNAKE_CASE_	      =       optimizer

								SCREAMING_SNAKE_CASE_	      =       self.get_lr_scheduler()

								return [optimizer], [scheduler]

			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Optional[Any]					)					->					Optional[int]:




								"""simple docstring"""






								return self.validation_step(lowerCAmelCase_							,							lowerCAmelCase_					)

			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     int					)					->					List[Any]:




								"""simple docstring"""






								return self.validation_end(lowerCAmelCase_					)

			def _lowercase (	self     :     Tuple					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       max(1							,							self.hparams.gpus					)  # TODO: consider num_tpu_cores
								SCREAMING_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 _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Optional[int]					)					->					List[Any]:




								"""simple docstring"""






								if stage == "test":
													SCREAMING_SNAKE_CASE_	      =       len(self.test_dataloader().dataset					)
								else:
													SCREAMING_SNAKE_CASE_	      =       self.get_dataloader('''train'''							,							self.hparams.train_batch_size							,							shuffle=lowerCAmelCase_					)
													SCREAMING_SNAKE_CASE_	      =       len(self.train_dataloader().dataset					)

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     bool = False					)					->					Optional[int]:




								"""simple docstring"""






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

			def _lowercase (	self     :     int					)					->					List[Any]:




								"""simple docstring"""






								return self.train_loader

			def _lowercase (	self     :     List[str]					)					->					Dict:




								"""simple docstring"""






								return self.get_dataloader('''dev'''							,							self.hparams.eval_batch_size							,							shuffle=lowerCAmelCase_					)

			def _lowercase (	self     :     Union[str, Any]					)					->					Optional[Any]:




								"""simple docstring"""






								return self.get_dataloader('''test'''							,							self.hparams.eval_batch_size							,							shuffle=lowerCAmelCase_					)

			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     str					)					->					Optional[Any]:




								"""simple docstring"""






								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 _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Dict[str, Any]					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.output_dir.joinpath('''best_tfmr'''					)
								SCREAMING_SNAKE_CASE_	      =       self.step_count
								self.model.save_pretrained(lowerCAmelCase_					)
								self.tokenizer.save_pretrained(lowerCAmelCase_					)




			@staticmethod
			def _lowercase (	lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     Optional[Any]					)					->					Optional[int]:




								"""simple docstring"""






								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=''''''							,							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 						snake_case			(       pl.Callback	):
			'''simple docstring'''

			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Dict					)					->					str:




								"""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 						snake_case			(       pl.Callback	):
			'''simple docstring'''

			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     Tuple					)					->					List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       trainer.lr_schedulers[0]['''scheduler''']
								SCREAMING_SNAKE_CASE_	      =       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
								pl_module.logger.log_metrics(lowerCAmelCase_					)

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Tuple:




								"""simple docstring"""






								rank_zero_info('''***** Validation results *****'''					)
								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Union[str, Any]:




								"""simple docstring"""






								rank_zero_info('''***** Test results *****'''					)
								SCREAMING_SNAKE_CASE_	      =       trainer.callback_metrics
								# Log and save results to file
								SCREAMING_SNAKE_CASE_	      =       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				(     UpperCAmelCase				,UpperCAmelCase      )->		None:







					'''simple docstring'''



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







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=None				,UpperCAmelCase=True				,UpperCAmelCase=None				,UpperCAmelCase=[]				,UpperCAmelCase=None				,UpperCAmelCase=None				,**UpperCAmelCase				,)->		Dict:







					'''simple docstring'''



					pl.seed_everything(args.seed      )

					# init model
					SCREAMING_SNAKE_CASE_	      =       Path(model.hparams.output_dir      )
					odir.mkdir(exist_ok=UpperCAmelCase      )

					# add custom checkpoints
					if checkpoint_callback is None:
										SCREAMING_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(UpperCAmelCase      )
					if logging_callback is None:
										SCREAMING_SNAKE_CASE_	      =       LoggingCallback()

					SCREAMING_SNAKE_CASE_	      =       {}

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

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

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

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

					if args.do_train:
										trainer.fit(UpperCAmelCase      )

					return trainer






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_														=      {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["MarianTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MarianForCausalLM",
		    "MarianModel",
		    "MarianMTModel",
		    "MarianPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

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


