

'''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    A_  ( ):
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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=512      ,       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." )

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					parser.parse_args()
		return args





def    A_  ( snake_case ):
		try:
				if args.tpu_name:
						SCREAMING_SNAKE_CASE:List[str]						      =					tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name      ,       zone=args.tpu_zone      ,       project=args.gcp_project )
				else:
						SCREAMING_SNAKE_CASE:int						      =					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    A_  ( snake_case ):
		SCREAMING_SNAKE_CASE:Dict						      =					0
		for file in file_list:
				SCREAMING_SNAKE_CASE:Optional[int]						      =					file.split("/" )[-1]
				SCREAMING_SNAKE_CASE:List[str]						      =					re.search(r"-\d+-(\d+)\.tfrecord"      ,       snake_case ).group(1 )
				SCREAMING_SNAKE_CASE:Dict						      =					int(snake_case )
				num_samples += sample_count

		return num_samples





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case=None ):
		SCREAMING_SNAKE_CASE:str						      =					count_samples(snake_case )
		SCREAMING_SNAKE_CASE:List[Any]						      =					tf.data.Dataset.from_tensor_slices(snake_case )
		if shuffle:
				SCREAMING_SNAKE_CASE:Optional[int]						      =					dataset.shuffle(len(snake_case ) )
		SCREAMING_SNAKE_CASE:str						      =					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
		SCREAMING_SNAKE_CASE:str						      =					dataset.apply(tf.data.experimental.assert_cardinality(snake_case ) )
		SCREAMING_SNAKE_CASE:List[str]						      =					dataset.map(snake_case      ,       num_parallel_calls=snake_case )
		if shuffle:
				assert shuffle_buffer_size is not None
				SCREAMING_SNAKE_CASE:int						      =					dataset.shuffle(args.shuffle_buffer_size )
		SCREAMING_SNAKE_CASE:str						      =					dataset.batch(snake_case      ,       drop_remainder=snake_case )
		SCREAMING_SNAKE_CASE:int						      =					dataset.map(snake_case      ,       num_parallel_calls=snake_case )
		SCREAMING_SNAKE_CASE:Any						      =					dataset.prefetch(snake_case )
		return dataset





def    A_  ( snake_case ):
		if not args.no_tpu:
				SCREAMING_SNAKE_CASE:Optional[int]						      =					initialize_tpu(snake_case )
				SCREAMING_SNAKE_CASE:List[str]						      =					tf.distribute.TPUStrategy(snake_case )
		else:
				SCREAMING_SNAKE_CASE:Any						      =					tf.distribute.OneDeviceStrategy(device="/gpu:0" )

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

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					AutoTokenizer.from_pretrained(args.tokenizer )
		SCREAMING_SNAKE_CASE:List[str]						      =					AutoConfig.from_pretrained(args.pretrained_model_config )
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer.vocab_size

		SCREAMING_SNAKE_CASE:Dict						      =					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}.''' )
		SCREAMING_SNAKE_CASE:Optional[int]						      =					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}.''' )

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					count_samples(snake_case )

		SCREAMING_SNAKE_CASE:Any						      =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					steps_per_epoch * args.num_epochs

		with strategy.scope():
				SCREAMING_SNAKE_CASE:Any						      =					TFAutoModelForMaskedLM.from_config(snake_case )
				model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
				SCREAMING_SNAKE_CASE:List[str]						      =					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 ):
				SCREAMING_SNAKE_CASE:int						      =					{
				    "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.
		SCREAMING_SNAKE_CASE:int						      =					DataCollatorForLanguageModeling(
		    tokenizer=snake_case      ,       mlm_probability=args.mlm_probability      ,       mlm=snake_case      ,       return_tensors="tf" )

		def mask_with_collator(snake_case ):
				# TF really needs an isin() function
				SCREAMING_SNAKE_CASE:List[Any]						      =					(
				    ~tf.cast(batch["attention_mask"]      ,       tf.bool )
				    | (batch["input_ids"] == tokenizer.cls_token_id)
				    | (batch["input_ids"] == tokenizer.sep_token_id)
				)
				SCREAMING_SNAKE_CASE:List[str]						      =					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

		SCREAMING_SNAKE_CASE:List[Any]						      =					args.per_replica_batch_size * strategy.num_replicas_in_sync

		SCREAMING_SNAKE_CASE:int						      =					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      ,       )

		SCREAMING_SNAKE_CASE:Tuple						      =					prepare_dataset(
		    snake_case      ,       decode_fn=snake_case      ,       mask_fn=snake_case      ,       batch_size=snake_case      ,       shuffle=snake_case      ,       )

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
		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    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






