






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A        =			5_0000
A        =			5000

A ,  A        =			os.path.split(__file__)
A        =			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def 					_lowerCamelCase(			lowerCAmelCase__				:		datasets.Dataset    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				for i in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   dataset[i]



@get_duration
def 					_lowerCamelCase(			lowerCAmelCase__				:		datasets.Dataset    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				for i in range(0    ,    len(lowerCAmelCase__      )    ,    lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	str													=   dataset[i : i + batch_size]



@get_duration
def 					_lowerCamelCase(			lowerCAmelCase__				:		datasets.Dataset    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Tuple      ):





				'''simple docstring'''







				with dataset.formatted_as(type=lowerCAmelCase__      ):
								for i in range(lowerCAmelCase__      ):
												SCREAMING_SNAKE_CASE_   :	Tuple													=   dataset[i]



@get_duration
def 					_lowerCamelCase(			lowerCAmelCase__				:		datasets.Dataset    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				with dataset.formatted_as(type=lowerCAmelCase__      ):
								for i in range(0    ,    lowerCAmelCase__    ,    lowerCAmelCase__      ):
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   dataset[i : i + batch_size]



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   {'num examples': SPEED_TEST_N_EXAMPLES}
				SCREAMING_SNAKE_CASE_   :	Any													=   [
				    (read, {'length': SMALL_TEST}),
				    (read, {'length': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
				    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
				    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
				    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
				    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
				]

				SCREAMING_SNAKE_CASE_   :	str													=   [
				    (read, {'length': SMALL_TEST}),
				    (read, {'length': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
				    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
				    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
				    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print('generating dataset'      )
								SCREAMING_SNAKE_CASE_   :	str													=   datasets.Features(
								    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   generate_example_dataset(
								    os.path.join(lowerCAmelCase__    ,    'dataset.arrow'      )    ,    lowerCAmelCase__    ,    num_examples=lowerCAmelCase__    ,    seq_shapes={'list': (100,)}    ,    )
								print('first set of iterations'      )
								for func, kwargs in functions:
												print(func.__name__    ,    str(lowerCAmelCase__      )      )
												SCREAMING_SNAKE_CASE_   :	Tuple													=   func(lowerCAmelCase__    ,    **lowerCAmelCase__      )

								print('shuffling dataset'      )
								SCREAMING_SNAKE_CASE_   :	str													=   dataset.shuffle()
								print('Second set of iterations (after shuffling'      )
								for func, kwargs in functions_shuffled:
												print('shuffled '    ,    func.__name__    ,    str(lowerCAmelCase__      )      )
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   func(
												    lowerCAmelCase__    ,    **lowerCAmelCase__      )

				with open(lowerCAmelCase__    ,    'wb'      ) as f:
								f.write(json.dumps(lowerCAmelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_iterating()







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 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   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=512    ,    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.'      )

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				try:
								if args.tpu_name:
												SCREAMING_SNAKE_CASE_   :	Dict													=   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,    zone=args.tpu_zone    ,    project=args.gcp_project      )
								else:
												SCREAMING_SNAKE_CASE_   :	Any													=   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 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0
				for file in file_list:
								SCREAMING_SNAKE_CASE_   :	Dict													=   file.split('/'      )[-1]
								SCREAMING_SNAKE_CASE_   :	Any													=   re.search(R'-\d+-(\d+)\.tfrecord'    ,    lowerCAmelCase__      ).group(1      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   int(lowerCAmelCase__      )
								num_samples += sample_count

				return num_samples



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]=None      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







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

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

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

				SCREAMING_SNAKE_CASE_   :	str													=   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_   :	str													=   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_   :	Optional[Any]													=   count_samples(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	int													=   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   steps_per_epoch * args.num_epochs

				with strategy.scope():
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   TFAutoModelForMaskedLM.from_config(lowerCAmelCase__      )
								model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	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__				:		Optional[int]      ):
								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(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.
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   DataCollatorForLanguageModeling(
				    tokenizer=lowerCAmelCase__    ,    mlm_probability=args.mlm_probability    ,    mlm=lowerCAmelCase__    ,    return_tensors='tf'      )

				def mask_with_collator(lowerCAmelCase__				:		Optional[Any]      ):
								# TF really needs an isin() function
								SCREAMING_SNAKE_CASE_   :	Tuple													=   (
								    ~tf.cast(batch['attention_mask']    ,    tf.bool      )
								    | (batch['input_ids'] == tokenizer.cls_token_id)
								    | (batch['input_ids'] == tokenizer.sep_token_id)
								)
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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

				SCREAMING_SNAKE_CASE_   :	Any													=   args.per_replica_batch_size * strategy.num_replicas_in_sync

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

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    )

				SCREAMING_SNAKE_CASE_   :	str													=   []
				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__":
				A        =			parse_args()
				main(args)