


'''simple docstring'''

import argparse
import logging
import os
import re

import tensorflow as tf

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


lowerCamelCase				:  int    		=      logging.getLogger(__name__)

lowerCamelCase				:  Union[str, Any]    		=      tf.data.AUTOTUNE

def _SCREAMING_SNAKE_CASE ()			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					argparse.ArgumentParser(description='''Train a masked language model on TPU.'''			)
       parser.add_argument(
           '''--pretrained_model_config'''    ,					type=A    ,					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=A    ,					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=A    ,					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=A    ,					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=A    ,					help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''    ,					)

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

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

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

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

       parser.add_argument(
           '''--max_length'''    ,					type=A    ,					default=512    ,					help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''    ,					)

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

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

       lowercase__      =					parser.parse_args()
       return args

def _SCREAMING_SNAKE_CASE (A			)			->			Optional[Any]:






       """simple docstring"""







       try:
              if args.tpu_name:
                     lowercase__      =					tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name    ,					zone=args.tpu_zone    ,					project=args.gcp_project			)
              else:
                     lowercase__      =					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(A			)
       tf.tpu.experimental.initialize_tpu_system(A			)

       return tpu

def _SCREAMING_SNAKE_CASE (A			)			->			List[str]:






       """simple docstring"""







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

       return num_samples

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A    ,					A=None			)			->			Union[str, Any]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







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

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

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

       lowercase__      =					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}."			)
       lowercase__      =					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}."			)

       lowercase__      =					count_samples(A			)

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

       with strategy.scope():
              lowercase__      =					TFAutoModelForMaskedLM.from_config(A			)
              model(model.dummy_inputs			)  # Pass some dummy inputs through the model to ensure all the weights are built
              lowercase__     ,lowercase__      =					create_optimizer(
                  num_train_steps=A    ,					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=A    ,					metrics=['''accuracy''']			)

       def decode_fn(A			):
              lowercase__      =					{
                  '''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(A    ,					A			)

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

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

       lowercase__      =					args.per_replica_batch_size * strategy.num_replicas_in_sync

       lowercase__      =					prepare_dataset(
           A    ,					decode_fn=A    ,					mask_fn=A    ,					batch_size=A    ,					shuffle=A    ,					shuffle_buffer_size=args.shuffle_buffer_size    ,					)

       lowercase__      =					prepare_dataset(
           A    ,					decode_fn=A    ,					mask_fn=A    ,					batch_size=A    ,					shuffle=A    ,					)

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

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

       model.save_pretrained(args.output_dir			)


if __name__ == "__main__":
 lowerCamelCase				:  Any    		=      parse_args()
 main(args)








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




