




"""simple docstring"""

import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


a							      =     logging.getLogger(__name__)

a							      =     tf.data.AUTOTUNE






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  argparse.ArgumentParser(description='Train a masked language model on TPU.'				)
      parser.add_argument(
          '--pretrained_model_config'							,    type=_snake_case							,    default='roberta-base'							,    help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'							,    )
      parser.add_argument(
          '--tokenizer'							,    type=_snake_case							,    default='unigram-tokenizer-wikitext'							,    help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'							,    )

      parser.add_argument(
          '--per_replica_batch_size'							,    type=_snake_case							,    default=8							,    help='Batch size per TPU core.'							,    )

      parser.add_argument(
          '--no_tpu'							,    action='store_true'							,    help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'							,    )

      parser.add_argument(
          '--tpu_name'							,    type=_snake_case							,    help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'							,    default='local'							,    )

      parser.add_argument(
          '--tpu_zone'							,    type=_snake_case							,    help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'							,    )

      parser.add_argument(
          '--gcp_project'							,    type=_snake_case							,    help='Google cloud project name. Only used for non-Colab TPU nodes.'				)

      parser.add_argument(
          '--bfloat16'							,    action='store_true'							,    help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'							,    )

      parser.add_argument(
          '--train_dataset'							,    type=_snake_case							,    help='Path to training dataset to load. If the path begins with `gs://`'
          ' then the dataset will be loaded from a Google Cloud Storage bucket.'							,    )

      parser.add_argument(
          '--shuffle_buffer_size'							,    type=_snake_case							,    default=2**18							,    help='Size of the shuffle buffer (in samples)'							,    )

      parser.add_argument(
          '--eval_dataset'							,    type=_snake_case							,    help='Path to evaluation dataset to load. If the path begins with `gs://`'
          ' then the dataset will be loaded from a Google Cloud Storage bucket.'							,    )

      parser.add_argument(
          '--num_epochs'							,    type=_snake_case							,    default=1							,    help='Number of epochs to train for.'							,    )

      parser.add_argument(
          '--learning_rate'							,    type=_snake_case							,    default=1E-4							,    help='Learning rate to use for training.'							,    )

      parser.add_argument(
          '--weight_decay_rate'							,    type=_snake_case							,    default=1E-3							,    help='Weight decay rate to use for training.'							,    )

      parser.add_argument(
          '--max_length'							,    type=_snake_case							,    default=5_12							,    help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'							,    )

      parser.add_argument(
          '--mlm_probability'							,    type=_snake_case							,    default=0.15							,    help='Fraction of tokens to mask during training.'							,    )

      parser.add_argument('--output_dir'							,    type=_snake_case							,    required=_snake_case							,    help='Path to save model checkpoints to.'				)
      parser.add_argument('--hub_model_id'							,    type=_snake_case							,    help='Model ID to upload to on the Hugging Face Hub.'				)

      _A     =  parser.parse_args()
      return args






def 		_snake_case	(   _snake_case				:   int				)       ->   List[Any]:






      '''simple docstring'''


      try:
            if args.tpu_name:
                  _A     =  tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name							,    zone=args.tpu_zone							,    project=args.gcp_project				)
            else:
                  _A     =  tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
            raise RuntimeError(
                'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
                '--gcp_project. When running on a TPU VM, use --tpu_name local.'				)

      tf.config.experimental_connect_to_cluster(_snake_case				)
      tf.tpu.experimental.initialize_tpu_system(_snake_case				)

      return tpu






def 		_snake_case	(   _snake_case				:   int				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  0
      for file in file_list:
            _A     =  file.split('/'				)[-1]
            _A     =  re.search(R'-\d+-(\d+)\.tfrecord'							,    _snake_case				).group(1				)
            _A     =  int(_snake_case				)
            num_samples += sample_count

      return num_samples






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   str							,    _snake_case				:   List[Any]							,    _snake_case				:   Optional[Any]							,    _snake_case				:   str							,    _snake_case				:   Union[str, Any]=None				)       ->   str:






      '''simple docstring'''


      _A     =  count_samples(_snake_case				)
      _A     =  tf.data.Dataset.from_tensor_slices(_snake_case				)
      if shuffle:
            _A     =  dataset.shuffle(len(_snake_case				)				)
      _A     =  tf.data.TFRecordDataset(_snake_case							,    num_parallel_reads=_snake_case				)
      # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
      _A     =  dataset.apply(tf.data.experimental.assert_cardinality(_snake_case				)				)
      _A     =  dataset.map(_snake_case							,    num_parallel_calls=_snake_case				)
      if shuffle:
            assert shuffle_buffer_size is not None
            _A     =  dataset.shuffle(args.shuffle_buffer_size				)
      _A     =  dataset.batch(_snake_case							,    drop_remainder=_snake_case				)
      _A     =  dataset.map(_snake_case							,    num_parallel_calls=_snake_case				)
      _A     =  dataset.prefetch(_snake_case				)
      return dataset






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


      if not args.no_tpu:
            _A     =  initialize_tpu(_snake_case				)
            _A     =  tf.distribute.TPUStrategy(_snake_case				)
      else:
            _A     =  tf.distribute.OneDeviceStrategy(device='/gpu:0'				)

      if args.bfloataa:
            tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'				)

      _A     =  AutoTokenizer.from_pretrained(args.tokenizer				)
      _A     =  AutoConfig.from_pretrained(args.pretrained_model_config				)
      _A     =  tokenizer.vocab_size

      _A     =  tf.io.gfile.glob(os.path.join(args.train_dataset							,    '*.tfrecord'				)				)
      if not training_records:
            raise ValueError(F'''No .tfrecord files found in {args.train_dataset}.'''				)
      _A     =  tf.io.gfile.glob(os.path.join(args.eval_dataset							,    '*.tfrecord'				)				)
      if not eval_records:
            raise ValueError(F'''No .tfrecord files found in {args.eval_dataset}.'''				)

      _A     =  count_samples(_snake_case				)

      _A     =  num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      _A     =  steps_per_epoch * args.num_epochs

      with strategy.scope():
            _A     =  TFAutoModelForMaskedLM.from_config(_snake_case				)
            model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
            _A  ,       _A     =  create_optimizer(
                num_train_steps=_snake_case							,    num_warmup_steps=total_train_steps // 20							,    init_lr=args.learning_rate							,    weight_decay_rate=args.weight_decay_rate							,    )

            # Transformers models compute the right loss for their task by default when labels are passed, and will
            # use this for training unless you specify your own loss function in compile().
            model.compile(optimizer=_snake_case							,    metrics=['accuracy']				)

      def decode_fn(_snake_case				:   Dict				):
            _A     =  {
                'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)				),
                'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)				),
            }
            return tf.io.parse_single_example(_snake_case							,    _snake_case				)

      # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
      # use their methods in our data pipeline.
      _A     =  DataCollatorForLanguageModeling(
          tokenizer=_snake_case							,    mlm_probability=args.mlm_probability							,    mlm=_snake_case							,    return_tensors='tf'				)

      def mask_with_collator(_snake_case				:   Tuple				):
            # TF really needs an isin() function
            _A     =  (
                ~tf.cast(batch['attention_mask']							,    tf.bool				)
                | (batch['input_ids'] == tokenizer.cls_token_id)
                | (batch['input_ids'] == tokenizer.sep_token_id)
            )
            _A  ,       _A     =  data_collator.tf_mask_tokens(
                batch['input_ids']							,    vocab_size=len(_snake_case				)							,    mask_token_id=tokenizer.mask_token_id							,    special_tokens_mask=_snake_case							,    )
            return batch

      _A     =  args.per_replica_batch_size * strategy.num_replicas_in_sync

      _A     =  prepare_dataset(
          _snake_case							,    decode_fn=_snake_case							,    mask_fn=_snake_case							,    batch_size=_snake_case							,    shuffle=_snake_case							,    shuffle_buffer_size=args.shuffle_buffer_size							,    )

      _A     =  prepare_dataset(
          _snake_case							,    decode_fn=_snake_case							,    mask_fn=_snake_case							,    batch_size=_snake_case							,    shuffle=_snake_case							,    )

      _A     =  []
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir							,    hub_model_id=args.hub_model_id							,    tokenizer=_snake_case				)				)

      model.fit(
          _snake_case							,    validation_data=_snake_case							,    epochs=args.num_epochs							,    callbacks=_snake_case							,    )

      model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
       a							      =     parse_args()
       main(args)







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int				)       ->   int:






      '''simple docstring'''


      return int((input_a, input_a).count(1				) != 0				)






def 		_snake_case	(   )       ->   None:






      '''simple docstring'''


      assert or_gate(0							,    0				) == 0
      assert or_gate(0							,    1				) == 1
      assert or_gate(1							,    0				) == 1
      assert or_gate(1							,    1				) == 1


if __name__ == "__main__":
       print(or_gate(0, 1))
       print(or_gate(1, 0))
       print(or_gate(0, 0))
       print(or_gate(1, 1))

