



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


__magic_name__       =     logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__magic_name__       =     {
    '''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
__magic_name__       =     {
    '''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
}
__magic_name__       =     sorted(arg_to_scheduler.keys())
__magic_name__       =     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class 	a__       (       pl.LightningModule       ):


					"""simple docstring"""



					def __init__(		self     :int      ,  lowercase__     :argparse.Namespace      ,  lowercase__     :List[Any]=None      ,  lowercase__     :Any="base"      ,  lowercase__     :int=None      ,  lowercase__     :Dict=None      ,  lowercase__     :Union[str, Any]=None      ,  **lowercase__     :List[str]      ,  ):
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowercase__    )
										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=lowercase__      ,  **lowercase__      ,  )
										else:
															lowercase						   =       config

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

										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=lowercase__      ,  )
										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=lowercase__      ,  )
										else:
															lowercase						   =       model

					def 				__UpperCAmelCase		(		self     :List[str]      ,  *lowercase__     :Union[str, Any]      ,  **lowercase__     :Union[str, Any]    ):
										lowercase						   =       self.model_type.from_pretrained(*lowercase__      ,  **lowercase__    )

					def 				__UpperCAmelCase		(		self     :Dict    ):
										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 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										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(
															    lowercase__      ,  lr=self.hparams.learning_rate      ,  scale_parameter=lowercase__      ,  relative_step=lowercase__    )

										else:
															lowercase						   =       AdamW(
															    lowercase__      ,  lr=self.hparams.learning_rate      ,  eps=self.hparams.adam_epsilon    )
										lowercase						   =       optimizer

										lowercase						   =       self.get_lr_scheduler()

										return [optimizer], [scheduler]

					def 				__UpperCAmelCase		(		self     :List[Any]      ,  lowercase__     :int      ,  lowercase__     :List[Any]    ):
										return self.validation_step(lowercase__      ,  lowercase__    )

					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :Tuple    ):
										return self.validation_end(lowercase__    )

					def 				__UpperCAmelCase		(		self     :int    ):
										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 				__UpperCAmelCase		(		self     :Tuple      ,  lowercase__     :int    ):
										if mode == "test":
															lowercase						   =       len(self.test_dataloader().dataset    )
										else:
															lowercase						   =       self.get_dataloader('train'      ,  self.hparams.train_batch_size      ,  shuffle=lowercase__    )
															lowercase						   =       len(self.train_dataloader().dataset    )

					def 				__UpperCAmelCase		(		self     :Any      ,  lowercase__     :str      ,  lowercase__     :int      ,  lowercase__     :bool = False    ):
										raise NotImplementedError('You must implement this for your task'    )

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

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

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										return self.get_dataloader('test'      ,  self.hparams.eval_batch_size      ,  shuffle=lowercase__    )

					def 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  lowercase__     :Tuple    ):
										return os.path.join(
										    self.hparams.data_dir      ,  'cached_{}_{}_{}'.format(
										        lowercase__      ,  list(filter(lowercase__      ,  self.hparams.model_name_or_path.split('/'    )    )    ).pop()      ,  str(self.hparams.max_seq_length    )      ,  )      ,  )

					@pl.utilities.rank_zero_only
					def 				__UpperCAmelCase		(		self     :Optional[Any]      ,  lowercase__     :Dict[str, Any]    ):
										lowercase						   =       self.output_dir.joinpath('best_tfmr'    )
										lowercase						   =       self.step_count
										self.model.save_pretrained(lowercase__    )
										self.tokenizer.save_pretrained(lowercase__    )



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




class 	a__       (       pl.Callback       ):


					"""simple docstring"""



					def 				__UpperCAmelCase		(		self     :List[str]      ,  lowercase__     :int      ,  lowercase__     :Tuple    ):
										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(lowercase__    )

					def 				__UpperCAmelCase		(		self     :int      ,  lowercase__     :pl.Trainer      ,  lowercase__     :pl.LightningModule    ):
										rank_zero_info('***** Validation results *****'    )
										lowercase						   =       trainer.callback_metrics
										# Log results
										for key in sorted(lowercase__    ):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowercase__      ,  str(metrics[key]    )    )    )



					def 				__UpperCAmelCase		(		self     :Dict      ,  lowercase__     :pl.Trainer      ,  lowercase__     :pl.LightningModule    ):
										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(lowercase__      ,  'w'    ) as writer:
															for key in sorted(lowercase__    ):
																				if key not in ["log", "progress_bar"]:
																									rank_zero_info('{} = {}\n'.format(lowercase__      ,  str(metrics[key]    )    )    )
																									writer.write('{} = {}\n'.format(lowercase__      ,  str(metrics[key]    )    )    )





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""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 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=[]       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			**_UpperCAmelCase       ,			):
					"""simple docstring"""






					pl.seed_everything(args.seed				)

					# init model
					lowercase						   =       Path(model.hparams.output_dir				)
					odir.mkdir(exist_ok=_UpperCAmelCase				)

					# 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(_UpperCAmelCase				)
					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'       ,			_UpperCAmelCase				)
					lowercase						   =       extra_train_kwargs.get('profiler'       ,			_UpperCAmelCase				)

					lowercase						   =       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











import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






