


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():
  UpperCAmelCase__        =				True
  UpperCAmelCase__        =				'''p3dn.24xlarge'''
  import smdistributed.dataparallel.tensorflow as sdp
else:
  UpperCAmelCase__        =				False
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case=None		)	->     Tuple:







 """simple docstring"""


 _lowercase				 =tqdm(__snake_case		)
 for i, batch in enumerate(__snake_case		):
  with tf.GradientTape() as tape:
   _lowercase ,       _lowercase				 =batch
   _lowercase				 =model(__snake_case		)
   _lowercase				 =loss(__snake_case	,		outputs.logits		)

  if SDP_ENABLED:
   _lowercase				 =sdp.DistributedGradientTape(__snake_case	,		sparse_as_dense=__snake_case		)

  _lowercase				 =tape.gradient(__snake_case	,		model.trainable_variables		)
  opt.apply_gradients(zip(__snake_case	,		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

 _lowercase				 ={'''loss''': loss_value.numpy()}
 return train_results
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Tuple:







 """simple docstring"""


 _lowercase ,       _lowercase				 =load_dataset('''imdb'''	,		split=['''train''', '''test''']		)

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

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

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

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

 if SDP_ENABLED:
  _lowercase				 =tf_train_dataset.shard(sdp.size()	,		sdp.rank()		)
  _lowercase				 =tf_test_dataset.shard(sdp.size()	,		sdp.rank()		)
 _lowercase				 =tf_train_dataset.batch(__snake_case	,		drop_remainder=__snake_case		)
 _lowercase				 =tf_test_dataset.batch(__snake_case	,		drop_remainder=__snake_case		)

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  UpperCAmelCase__        =				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'''])

  UpperCAmelCase__      ,UpperCAmelCase__        =				parser.parse_known_args()

  # Set up logging
  UpperCAmelCase__        =				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()

    UpperCAmelCase__        =				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
  UpperCAmelCase__        =				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  UpperCAmelCase__        =				AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  UpperCAmelCase__      ,UpperCAmelCase__        =				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
  UpperCAmelCase__        =				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  UpperCAmelCase__        =				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  UpperCAmelCase__        =				[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)
    UpperCAmelCase__        =				time.time()
    UpperCAmelCase__        =				fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    UpperCAmelCase__        =				time.time() - start_train_time
    logger.info('''*** Train ***''')
    logger.info(f'''train_runtime = {end_train_time}''')

    UpperCAmelCase__        =				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):
    UpperCAmelCase__        =				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info('''*** Evaluate ***''')

    UpperCAmelCase__        =				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)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''rembert'''





     def __init__(self						,	UpperCAmelCase=2_5_0_3_0_0						,	UpperCAmelCase=1_1_5_2						,	UpperCAmelCase=3_2						,	UpperCAmelCase=1_8						,	UpperCAmelCase=2_5_6						,	UpperCAmelCase=1_6_6_4						,	UpperCAmelCase=4_6_0_8						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=1e-12						,	UpperCAmelCase=True						,	UpperCAmelCase=0						,	UpperCAmelCase=3_1_2						,	UpperCAmelCase=3_1_3						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(pad_token_id=UpperCAmelCase						,	bos_token_id=UpperCAmelCase						,	eos_token_id=UpperCAmelCase						,	**UpperCAmelCase			)

      _lowercase				 =vocab_size
      _lowercase				 =input_embedding_size
      _lowercase				 =output_embedding_size
      _lowercase				 =max_position_embeddings
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =classifier_dropout_prob
      _lowercase				 =initializer_range
      _lowercase				 =type_vocab_size
      _lowercase				 =layer_norm_eps
      _lowercase				 =use_cache
      _lowercase				 =False







class  lowerCamelCase__  (			lowerCAmelCase):





     @property
     def 	__A   (self			)	->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase				 ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase				 ={0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]			)





     @property
     def 	__A   (self			)	->  float:
      return 1e-4

