

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__             =		logging.getLogger(__name__)

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

lowerCAmelCase__             =		{
    """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__             =		{
    """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__             =		sorted(arg_to_scheduler.keys())
lowerCAmelCase__             =		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 						lowercase     (					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						,							):
			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 :		str								=    0
			_UpperCamelCase :		int								=    Path(self.hparams.output_dir)
			_UpperCamelCase :		Optional[Any]								=    self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
				_UpperCamelCase :		Tuple								=    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 :		str								=    ('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 :		Tuple								=    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 :		Optional[int]								=    MODEL_MODES[mode]
			if model is None:
				_UpperCamelCase :		int								=    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 :		Union[str, Any]								=    model


		def 		A__       (					self						,							*__snake_case						,							**__snake_case):
			_UpperCamelCase :		List[Any]								=    self.model_type.from_pretrained(*__snake_case						,							**__snake_case)


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


		def 		A__       (					self):
			_UpperCamelCase :		Optional[int]								=    self.model
			_UpperCamelCase :		Tuple								=    ['bias', 'LayerNorm.weight']
			_UpperCamelCase :		Optional[Any]								=    [
			    {
			        '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:
				_UpperCamelCase :		Dict								=    Adafactor(
				    __snake_case						,							lr=self.hparams.learning_rate						,							scale_parameter=__snake_case						,							relative_step=__snake_case)

			else:
				_UpperCamelCase :		Tuple								=    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 		A__       (					self						,							__snake_case						,							__snake_case):
			return self.validation_step(__snake_case						,							__snake_case)


		def 		A__       (					self						,							__snake_case):
			return self.validation_end(__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    max(1						,							self.hparams.gpus)  # TODO: consider num_tpu_cores
			_UpperCamelCase :		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 		A__       (					self						,							__snake_case):
			if mode == "test":
				_UpperCamelCase :		Any								=    len(self.test_dataloader().dataset)
			else:
				_UpperCamelCase :		List[str]								=    self.get_dataloader('train'						,							self.hparams.train_batch_size						,							shuffle=__snake_case)
				_UpperCamelCase :		List[str]								=    len(self.train_dataloader().dataset)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case = False):
			raise NotImplementedError('You must implement this for your task')


		def 		A__       (					self):
			return self.train_loader


		def 		A__       (					self):
			return self.get_dataloader('dev'						,							self.hparams.eval_batch_size						,							shuffle=__snake_case)


		def 		A__       (					self):
			return self.get_dataloader('test'						,							self.hparams.eval_batch_size						,							shuffle=__snake_case)


		def 		A__       (					self						,							__snake_case):
			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 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		str								=    self.output_dir.joinpath('best_tfmr')
			_UpperCamelCase :		Union[str, Any]								=    self.step_count
			self.model.save_pretrained(__snake_case)
			self.tokenizer.save_pretrained(__snake_case)






		@staticmethod
		def 		A__       (					__snake_case						,							__snake_case):
			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=''						,							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 						lowercase     (					pl.Callback				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    trainer.lr_schedulers[0]['scheduler']
			_UpperCamelCase :		Union[str, Any]								=    {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
			pl_module.logger.log_metrics(__snake_case)


		def 		A__       (					self						,							__snake_case						,							__snake_case):
			rank_zero_info('***** Validation results *****')
			_UpperCamelCase :		Dict								=    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 		A__       (					self						,							__snake_case						,							__snake_case):
			rank_zero_info('***** Test results *****')
			_UpperCamelCase :		Any								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[str]						)    ->					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=4_2				,							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        lowerCamelCase_  (				UpperCAmelCase_				:  BaseTransformer				,							UpperCAmelCase_				:  argparse.Namespace				,							UpperCAmelCase_				:  int=None				,							UpperCAmelCase_				:  Optional[Any]=True				,							UpperCAmelCase_				:  List[str]=[]				,							UpperCAmelCase_				:  List[Any]=None				,							UpperCAmelCase_				:  List[Any]=None				,							**UpperCAmelCase_				:  List[Any]				,							)    ->					Tuple:




	'''simple docstring'''




	pl.seed_everything(args.seed						)

	# init model
	_UpperCamelCase :		str								=    Path(model.hparams.output_dir						)
	odir.mkdir(exist_ok=UpperCAmelCase_						)

	# 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(UpperCAmelCase_						)
	if logging_callback is None:
		_UpperCamelCase :		List[Any]								=    LoggingCallback()

	_UpperCamelCase :		Optional[int]								=    {}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		_UpperCamelCase :		Dict								=    1_6
		_UpperCamelCase :		Dict								=    args.fpaa_opt_level

	if args.gpus > 1:
		_UpperCamelCase :		str								=    'ddp'

	_UpperCamelCase :		Any								=    args.accumulate_grad_batches
	_UpperCamelCase :		Any								=    extra_train_kwargs.get('accelerator'				,							UpperCAmelCase_						)
	_UpperCamelCase :		Dict								=    extra_train_kwargs.get('profiler'				,							UpperCAmelCase_						)

	_UpperCamelCase :		List[str]								=    pl.Trainer.from_argparse_args(
	    UpperCAmelCase_				,							weights_summary=UpperCAmelCase_				,							callbacks=[logging_callback] + extra_callbacks				,							logger=UpperCAmelCase_				,							checkpoint_callback=UpperCAmelCase_				,							**UpperCAmelCase_				,							)

	if args.do_train:
		trainer.fit(UpperCAmelCase_						)

	return trainer







from typing import TYPE_CHECKING

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


lowerCAmelCase__             =		{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)




