





import argparse
import logging
import os
import re

import tensorflow as tf

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


_lowercase		:  List[str]			       =logging.getLogger(__name__)

_lowercase		:  Optional[int]			       =tf.data.AUTOTUNE


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

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

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

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

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

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

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

		lowerCamelCase_	:     int 						=      parser.parse_args()
		return args


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		try:
				if args.tpu_name:
						lowerCamelCase_	:     str 						=      tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name							,zone=args.tpu_zone							,project=args.gcp_project						)
				else:
						lowerCamelCase_	:     Dict 						=      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(lowerCAmelCase__						)
		tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase__						)

		return tpu


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     List[str] 						=      0
		for file in file_list:
				lowerCamelCase_	:     Dict 						=      file.split('/'						)[-1]
				lowerCamelCase_	:     List[str] 						=      re.search(r'-\d+-(\d+)\.tfrecord'							,lowerCAmelCase__						).group(1						)
				lowerCamelCase_	:     int 						=      int(lowerCAmelCase__						)
				num_samples += sample_count

		return num_samples


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=None						):
		lowerCamelCase_	:     List[Any] 						=      count_samples(lowerCAmelCase__						)
		lowerCamelCase_	:     List[Any] 						=      tf.data.Dataset.from_tensor_slices(lowerCAmelCase__						)
		if shuffle:
				lowerCamelCase_	:     Tuple 						=      dataset.shuffle(len(lowerCAmelCase__						)						)
		lowerCamelCase_	:     List[str] 						=      tf.data.TFRecordDataset(lowerCAmelCase__							,num_parallel_reads=lowerCAmelCase__						)
		# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
		lowerCamelCase_	:     Dict 						=      dataset.apply(tf.data.experimental.assert_cardinality(lowerCAmelCase__						)						)
		lowerCamelCase_	:     List[str] 						=      dataset.map(lowerCAmelCase__							,num_parallel_calls=lowerCAmelCase__						)
		if shuffle:
				assert shuffle_buffer_size is not None
				lowerCamelCase_	:     Dict 						=      dataset.shuffle(args.shuffle_buffer_size						)
		lowerCamelCase_	:     Optional[Any] 						=      dataset.batch(lowerCAmelCase__							,drop_remainder=lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[Any] 						=      dataset.map(lowerCAmelCase__							,num_parallel_calls=lowerCAmelCase__						)
		lowerCamelCase_	:     Union[str, Any] 						=      dataset.prefetch(lowerCAmelCase__						)
		return dataset


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		if not args.no_tpu:
				lowerCamelCase_	:     List[str] 						=      initialize_tpu(lowerCAmelCase__						)
				lowerCamelCase_	:     Optional[Any] 						=      tf.distribute.TPUStrategy(lowerCAmelCase__						)
		else:
				lowerCamelCase_	:     Tuple 						=      tf.distribute.OneDeviceStrategy(device='/gpu:0'						)

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

		lowerCamelCase_	:     Optional[Any] 						=      AutoTokenizer.from_pretrained(args.tokenizer						)
		lowerCamelCase_	:     str 						=      AutoConfig.from_pretrained(args.pretrained_model_config						)
		lowerCamelCase_	:     int 						=      tokenizer.vocab_size

		lowerCamelCase_	:     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}."						)
		lowerCamelCase_	:     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}."						)

		lowerCamelCase_	:     Optional[int] 						=      count_samples(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		lowerCamelCase_	:     int 						=      steps_per_epoch * args.num_epochs

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

		def decode_fn(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      {
				    '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(lowerCAmelCase__							,lowerCAmelCase__						)

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

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

		lowerCamelCase_	:     Tuple 						=      args.per_replica_batch_size * strategy.num_replicas_in_sync

		lowerCamelCase_	:     Optional[int] 						=      prepare_dataset(
		    lowerCAmelCase__							,decode_fn=lowerCAmelCase__							,mask_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__							,shuffle=lowerCAmelCase__							,shuffle_buffer_size=args.shuffle_buffer_size							,)

		lowerCamelCase_	:     Optional[int] 						=      prepare_dataset(
		    lowerCAmelCase__							,decode_fn=lowerCAmelCase__							,mask_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__							,shuffle=lowerCAmelCase__							,)

		lowerCamelCase_	:     Any 						=      []
		if args.hub_model_id:
				callbacks.append(
				    PushToHubCallback(output_dir=args.output_dir							,hub_model_id=args.hub_model_id							,tokenizer=lowerCAmelCase__						)						)

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

		model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
		_lowercase		:  Optional[int]			       =parse_args()
		main(args)







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
