


'''simple docstring'''







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
			A   :  List[str]       		=				True
			A   :  Dict       		=				"""p3dn.24xlarge"""
			import smdistributed.dataparallel.tensorflow as sdp
else:
			A   :  Dict       		=				False






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict						,lowerCamelCase  :    str						,lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    Optional[Any]						,lowerCamelCase  :    int						,lowerCamelCase  :    List[str]=None     ):
							_A  :			List[Any]											=      tqdm(lowerCamelCase     )
							for i, batch in enumerate(lowerCamelCase     ):
														with tf.GradientTape() as tape:
																					_A						,				_A  :			int											=      batch
																					_A  :			List[Any]											=      model(lowerCamelCase     )
																					_A  :			Union[str, Any]											=      loss(lowerCamelCase						,outputs.logits     )

														if SDP_ENABLED:
																					_A  :			str											=      sdp.DistributedGradientTape(lowerCamelCase						,sparse_as_dense=lowerCamelCase     )

														_A  :			Dict											=      tape.gradient(lowerCamelCase						,model.trainable_variables     )
														opt.apply_gradients(zip(lowerCamelCase						,model.trainable_variables     )     )

														pbar.set_description(F'Loss: {loss_value:.4f}'     )

														if SDP_ENABLED and i == 0:
																					sdp.broadcast_variables(model.variables						,root_rank=0     )
																					sdp.broadcast_variables(opt.variables()						,root_rank=0     )

														if max_steps and i >= max_steps:
																					break

							_A  :			Dict											=      {'loss': loss_value.numpy()}
							return train_results






def    lowerCAmelCase__ (       lowerCamelCase  :    Optional[Any]						,lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    Optional[Any]     ):
							_A						,				_A  :			Tuple											=      load_dataset('imdb'						,split=['train', 'test']     )

							# Preprocess train dataset
							_A  :			List[Any]											=      train_dataset.map(
							    lambda lowerCamelCase     : tokenizer(e['text']						,truncation=lowerCamelCase						,padding='max_length'     )						,batched=lowerCamelCase     )
							train_dataset.set_format(type='tensorflow'						,columns=['input_ids', 'attention_mask', 'label']     )

							_A  :			Union[str, Any]											=      {
							    x: train_dataset[x].to_tensor(default_value=0						,shape=[None, tokenizer.model_max_length]     )
							    for x in ['input_ids', 'attention_mask']
							}
							_A  :			Optional[int]											=      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])     )

							# Preprocess test dataset
							_A  :			Dict											=      test_dataset.map(
							    lambda lowerCamelCase     : tokenizer(e['text']						,truncation=lowerCamelCase						,padding='max_length'     )						,batched=lowerCamelCase     )
							test_dataset.set_format(type='tensorflow'						,columns=['input_ids', 'attention_mask', 'label']     )

							_A  :			Union[str, Any]											=      {
							    x: test_dataset[x].to_tensor(default_value=0						,shape=[None, tokenizer.model_max_length]     )
							    for x in ['input_ids', 'attention_mask']
							}
							_A  :			List[Any]											=      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])     )

							if SDP_ENABLED:
														_A  :			List[Any]											=      tf_train_dataset.shard(sdp.size()						,sdp.rank()     )
														_A  :			Tuple											=      tf_test_dataset.shard(sdp.size()						,sdp.rank()     )
							_A  :			Union[str, Any]											=      tf_train_dataset.batch(lowerCamelCase						,drop_remainder=lowerCamelCase     )
							_A  :			Tuple											=      tf_test_dataset.batch(lowerCamelCase						,drop_remainder=lowerCamelCase     )

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			A   :  Dict       		=				argparse.ArgumentParser()

			# Hyperparameters sent by the client are passed as command-line arguments to the script.
			parser.add_argument('''--epochs''', type=int, default=3)
			parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
			parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
			parser.add_argument('''--model_name_or_path''', type=str)
			parser.add_argument('''--learning_rate''', type=str, default=5e-5)
			parser.add_argument('''--do_train''', type=bool, default=True)
			parser.add_argument('''--do_eval''', type=bool, default=True)
			parser.add_argument('''--output_dir''', type=str)
			parser.add_argument('''--max_steps''', type=int, default=None)

			# Data, model, and output directories
			parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
			parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
			parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

			A   :  str       		=				parser.parse_known_args()

			# Set up logging
			A   :  str       		=				logging.getLogger(__name__)

			logging.basicConfig(
			    level=logging.getLevelName('''INFO'''),
			    handlers=[logging.StreamHandler(sys.stdout)],
			    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
			)

			if SDP_ENABLED:
						sdp.init()

						A   :  Dict       		=				tf.config.experimental.list_physical_devices('''GPU''')
						for gpu in gpus:
									tf.config.experimental.set_memory_growth(gpu, True)
						if gpus:
									tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
			A   :  Any       		=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			A   :  List[Any]       		=				AutoTokenizer.from_pretrained(args.model_name_or_path)

			# get datasets
			A   :  Union[str, Any]       		=				get_datasets(
			    tokenizer=tokenizer,
			    train_batch_size=args.per_device_train_batch_size,
			    eval_batch_size=args.per_device_eval_batch_size,
			)

			# fine optimizer and loss
			A   :  Tuple       		=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			A   :  Optional[Any]       		=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			A   :  int       		=				[tf.keras.metrics.SparseCategoricalAccuracy()]
			model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

			# Training
			if args.do_train:
						# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
						A   :  List[str]       		=				time.time()
						A   :  List[str]       		=				fit(
						    model,
						    loss,
						    optimizer,
						    tf_train_dataset,
						    args.epochs,
						    args.per_device_train_batch_size,
						    max_steps=args.max_steps,
						)
						A   :  Union[str, Any]       		=				time.time() - start_train_time
						logger.info('''*** Train ***''')
						logger.info(f"""train_runtime = {end_train_time}""")

						A   :  Any       		=				os.path.join(args.output_dir, '''train_results.txt''')

						if not SDP_ENABLED or sdp.rank() == 0:
									with open(output_eval_file, '''w''') as writer:
												logger.info('''***** Train results *****''')
												logger.info(train_results)
												for key, value in train_results.items():
															logger.info(f"""  {key} = {value}""")
															writer.write(f"""{key} = {value}\n""")

    # Evaluation
			if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
						A   :  List[str]       		=				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info('''*** Evaluate ***''')

						A   :  Tuple       		=				os.path.join(args.output_dir, '''eval_results.txt''')

						with open(output_eval_file, '''w''') as writer:
									logger.info('''***** Eval results *****''')
									logger.info(result)
									for key, value in result.items():
												logger.info(f"""  {key} = {value}""")
												writer.write(f"""{key} = {value}\n""")

    # Save result
			if SDP_ENABLED:
						if sdp.rank() == 0:
									model.save_pretrained(args.output_dir)
									tokenizer.save_pretrained(args.output_dir)
			else:
						model.save_pretrained(args.output_dir)
						tokenizer.save_pretrained(args.output_dir)










'''simple docstring'''







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   :  Optional[Any]       		=				logging.getLogger(__name__)

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

A   :  Optional[int]       		=				{
    '''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   :  Dict       		=				{
    '''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   :  List[str]       		=				sorted(arg_to_scheduler.keys())
A   :  List[str]       		=				'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class 				__lowerCamelCase				( pl.LightningModule ):
    """simple docstring"""


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

           self.save_hyperparameters(SCREAMING_SNAKE_CASE)
           _A  :			Optional[int]											=      0
           _A  :			int											=      Path(self.hparams.output_dir)
           _A  :			str											=      self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                  _A  :			Dict											=      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=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE   ,							)
           else:
                  _A  :			PretrainedConfig											=      config

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

           if tokenizer is None:
                  _A  :			Tuple											=      AutoTokenizer.from_pretrained(
                      self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,							cache_dir=SCREAMING_SNAKE_CASE   ,							)
           else:
                  _A  :			PreTrainedTokenizer											=      tokenizer
           _A  :			List[str]											=      MODEL_MODES[mode]
           if model is None:
                  _A  :			Optional[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=SCREAMING_SNAKE_CASE   ,							)
           else:
                  _A  :			Dict											=      model


    def      A					(						self    :							int   ,							*SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							**SCREAMING_SNAKE_CASE    :							List[Any]):
           _A  :			List[Any]											=      self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


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


    def      A					(						self    :							Dict):
           _A  :			Any											=      self.model
           _A  :			List[Any]											=      ['bias', 'LayerNorm.weight']
           _A  :			Union[str, 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:
                  _A  :			str											=      Adafactor(
                      SCREAMING_SNAKE_CASE   ,							lr=self.hparams.learning_rate   ,							scale_parameter=SCREAMING_SNAKE_CASE   ,							relative_step=SCREAMING_SNAKE_CASE)

           else:
                  _A  :			str											=      AdamW(
                      SCREAMING_SNAKE_CASE   ,							lr=self.hparams.learning_rate   ,							eps=self.hparams.adam_epsilon)
           _A  :			Tuple											=      optimizer

           _A  :			List[Any]											=      self.get_lr_scheduler()

           return [optimizer], [scheduler]


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           return self.validation_step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.validation_end(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple):
           _A  :			Tuple											=      max(1   ,							self.hparams.gpus)  # TODO: consider num_tpu_cores
           _A  :			Optional[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    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           if mode == "test":
                  _A  :			Union[str, Any]											=      len(self.test_dataloader().dataset)
           else:
                  _A  :			Optional[int]											=      self.get_dataloader('train'   ,							self.hparams.train_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      len(self.train_dataloader().dataset)


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							bool = False):
           raise NotImplementedError('You must implement this for your task')


    def      A					(						self    :							Optional[int]):
           return self.train_loader


    def      A					(						self    :							Tuple):
           return self.get_dataloader('dev'   ,							self.hparams.eval_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           return self.get_dataloader('test'   ,							self.hparams.eval_batch_size   ,							shuffle=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           return os.path.join(
               self.hparams.data_dir   ,							'cached_{}_{}_{}'.format(
                   SCREAMING_SNAKE_CASE   ,							list(filter(SCREAMING_SNAKE_CASE   ,							self.hparams.model_name_or_path.split('/'))).pop()   ,							str(self.hparams.max_seq_length)   ,							)   ,							)


    @pl.utilities.rank_zero_only
    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Dict[str, Any]):
           _A  :			List[Any]											=      self.output_dir.joinpath('best_tfmr')
           _A  :			str											=      self.step_count
           self.model.save_pretrained(SCREAMING_SNAKE_CASE)
           self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)


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




class 				__lowerCamelCase				( pl.Callback ):
    """simple docstring"""


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							str):
           _A  :			Tuple											=      trainer.lr_schedulers[0]['scheduler']
           _A  :			Any											=      {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
           pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							pl.Trainer   ,							SCREAMING_SNAKE_CASE    :							pl.LightningModule):
           rank_zero_info('***** Validation results *****')
           _A  :			Tuple											=      trainer.callback_metrics
           # Log results
           for key in sorted(SCREAMING_SNAKE_CASE):
                  if key not in ["log", "progress_bar"]:
                         rank_zero_info('{} = {}\n'.format(SCREAMING_SNAKE_CASE   ,							str(metrics[key])))


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							pl.Trainer   ,							SCREAMING_SNAKE_CASE    :							pl.LightningModule):
           rank_zero_info('***** Test results *****')
           _A  :			str											=      trainer.callback_metrics
           # Log and save results to file
           _A  :			str											=      os.path.join(pl_module.hparams.output_dir   ,							'test_results.txt')
           with open(SCREAMING_SNAKE_CASE   ,							'w') as writer:
                  for key in sorted(SCREAMING_SNAKE_CASE):
                         if key not in ["log", "progress_bar"]:
                                rank_zero_info('{} = {}\n'.format(SCREAMING_SNAKE_CASE   ,							str(metrics[key])))
                                writer.write('{} = {}\n'.format(SCREAMING_SNAKE_CASE   ,							str(metrics[key])))






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    int     ):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '--output_dir'						,default=lowerCamelCase						,type=lowerCamelCase						,required=lowerCamelCase						,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=lowerCamelCase						,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=lowerCamelCase     )
       parser.add_argument('--max_grad_norm'						,dest='gradient_clip_val'						,default=1.0						,type=lowerCamelCase						,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=lowerCamelCase						,default=1						,help='Number of updates steps to accumulate before performing a backward/update pass.'						,)
       parser.add_argument('--seed'						,type=lowerCamelCase						,default=42						,help='random seed for initialization'     )
       parser.add_argument(
           '--data_dir'						,default=lowerCamelCase						,type=lowerCamelCase						,required=lowerCamelCase						,help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,)






def    lowerCAmelCase__ (       lowerCamelCase  :    BaseTransformer						,lowerCamelCase  :    argparse.Namespace						,lowerCamelCase  :    Union[str, Any]=None						,lowerCamelCase  :    Optional[Any]=True						,lowerCamelCase  :    List[Any]=[]						,lowerCamelCase  :    Dict=None						,lowerCamelCase  :    List[Any]=None						,**lowerCamelCase  :    str						,):
       pl.seed_everything(args.seed     )

       # init model
       _A  :			Optional[Any]											=      Path(model.hparams.output_dir     )
       odir.mkdir(exist_ok=lowerCamelCase     )

       # add custom checkpoints
       if checkpoint_callback is None:
              _A  :			Optional[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(lowerCamelCase     )
       if logging_callback is None:
              _A  :			Dict											=      LoggingCallback()

       _A  :			Union[str, Any]											=      {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _A  :			str											=      16
              _A  :			Optional[int]											=      args.fpaa_opt_level

       if args.gpus > 1:
              _A  :			Union[str, Any]											=      'ddp'

       _A  :			List[Any]											=      args.accumulate_grad_batches
       _A  :			Tuple											=      extra_train_kwargs.get('accelerator'						,lowerCamelCase     )
       _A  :			int											=      extra_train_kwargs.get('profiler'						,lowerCamelCase     )

       _A  :			Union[str, Any]											=      pl.Trainer.from_argparse_args(
           lowerCamelCase						,weights_summary=lowerCamelCase						,callbacks=[logging_callback] + extra_callbacks						,logger=lowerCamelCase						,checkpoint_callback=lowerCamelCase						,**lowerCamelCase						,)

       if args.do_train:
              trainer.fit(lowerCamelCase     )

       return trainer






