



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


SCREAMING_SNAKE_CASE__							=    logging.getLogger(__name__)

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

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





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



											"""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(_UpperCAmelCase    )
											__lowercase								= 0
											__lowercase								= Path(self.hparams.output_dir    )
											__lowercase								= self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
															__lowercase								= 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:
															__lowercase								= config

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





							def       a__			(			self  :       Optional[Any]					,			*_UpperCAmelCase  :       str					,			**_UpperCAmelCase  :       List[str]    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.model_type.from_pretrained(*_UpperCAmelCase					,			**_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Tuple:



											"""simple docstring"""


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





							def       a__			(			self  :       Tuple    )						->		Dict:



											"""simple docstring"""


											__lowercase								= self.model
											__lowercase								= ['bias', 'LayerNorm.weight']
											__lowercase								= [
											    {
											        '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:
															__lowercase								= Adafactor(
															    _UpperCAmelCase					,			lr=self.hparams.learning_rate					,			scale_parameter=_UpperCAmelCase					,			relative_step=_UpperCAmelCase    )

											else:
															__lowercase								= AdamW(
															    _UpperCAmelCase					,			lr=self.hparams.learning_rate					,			eps=self.hparams.adam_epsilon    )
											__lowercase								= optimizer

											__lowercase								= self.get_lr_scheduler()

											return [optimizer], [scheduler]





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       Union[str, Any]    )						->		Any:



											"""simple docstring"""


											return self.validation_step(_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Optional[int]    )						->		int:



											"""simple docstring"""


											return self.validation_end(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]    )						->		List[str]:



											"""simple docstring"""


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





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



											"""simple docstring"""


											if mode == "test":
															__lowercase								= len(self.test_dataloader().dataset    )
											else:
															__lowercase								= self.get_dataloader('train'					,			self.hparams.train_batch_size					,			shuffle=_UpperCAmelCase    )
															__lowercase								= len(self.train_dataloader().dataset    )





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       bool = False    )						->		List[Any]:



											"""simple docstring"""


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





							def       a__			(			self  :       List[str]    )						->		List[Any]:



											"""simple docstring"""


											return self.train_loader





							def       a__			(			self  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


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





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



											"""simple docstring"""


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





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       Dict    )						->		int:



											"""simple docstring"""


											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       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       Dict[str, Any]    )						->		Any:



											"""simple docstring"""


											__lowercase								= self.output_dir.joinpath('best_tfmr'    )
											__lowercase								= self.step_count
											self.model.save_pretrained(_UpperCAmelCase    )
											self.tokenizer.save_pretrained(_UpperCAmelCase    )





							@staticmethod
							def       a__			(			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       str    )						->		Optional[Any]:



											"""simple docstring"""


											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      A__       (						pl.Callback    ):





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       Union[str, Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= trainer.lr_schedulers[0]['scheduler']
											__lowercase								= {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
											pl_module.logger.log_metrics(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       pl.Trainer					,			_UpperCAmelCase  :       pl.LightningModule    )						->		Any:



											"""simple docstring"""


											rank_zero_info('***** Validation results *****'    )
											__lowercase								= 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       a__			(			self  :       str					,			_UpperCAmelCase  :       pl.Trainer					,			_UpperCAmelCase  :       pl.LightningModule    )						->		Tuple:



											"""simple docstring"""


											rank_zero_info('***** Test results *****'    )
											__lowercase								= trainer.callback_metrics
											# Log and save results to file
											__lowercase								= 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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Any      )					->						None:
				parser.add_argument(
				    '--output_dir'   ,							default=snake_case__   ,							type=snake_case__   ,							required=snake_case__   ,							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=snake_case__   ,							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=snake_case__      )
				parser.add_argument('--max_grad_norm'   ,							dest='gradient_clip_val'   ,							default=1.0   ,							type=snake_case__   ,							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=snake_case__   ,							default=1   ,							help='Number of updates steps to accumulate before performing a backward/update pass.'   ,							)
				parser.add_argument('--seed'   ,							type=snake_case__   ,							default=42   ,							help='random seed for initialization'      )
				parser.add_argument(
				    '--data_dir'   ,							default=snake_case__   ,							type=snake_case__   ,							required=snake_case__   ,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'   ,							)




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

				# init model
				__lowercase								= Path(model.hparams.output_dir      )
				odir.mkdir(exist_ok=snake_case__      )

				# add custom checkpoints
				if checkpoint_callback is None:
								__lowercase								= 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(snake_case__      )
				if logging_callback is None:
								__lowercase								= LoggingCallback()

				__lowercase								= {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								__lowercase								= 16
								__lowercase								= args.fpaa_opt_level

				if args.gpus > 1:
								__lowercase								= 'ddp'

				__lowercase								= args.accumulate_grad_batches
				__lowercase								= extra_train_kwargs.get('accelerator'   ,							snake_case__      )
				__lowercase								= extra_train_kwargs.get('profiler'   ,							snake_case__      )

				__lowercase								= pl.Trainer.from_argparse_args(
				    snake_case__   ,							weights_summary=snake_case__   ,							callbacks=[logging_callback] + extra_callbacks   ,							logger=snake_case__   ,							checkpoint_callback=snake_case__   ,							**snake_case__   ,							)

				if args.do_train:
								trainer.fit(snake_case__      )

				return trainer







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

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





