
__a         =   [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






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		(				):


				'''simple docstring'''
				lowercase_	    =   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=0.1_5		,  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.'''   )

				lowercase_	    =   parser.parse_args()
				return args




def 							a		(				snake_case__:   int   ):


				'''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(snake_case__   )
				tf.tpu.experimental.initialize_tpu_system(snake_case__   )

				return tpu




def 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   0
				for file in file_list:
								lowercase_	    =   file.split('''/'''   )[-1]
								lowercase_	    =   re.search(r'''-\d+-(\d+)\.tfrecord'''		,  snake_case__   ).group(1   )
								lowercase_	    =   int(snake_case__   )
								num_samples += sample_count

				return num_samples




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   Union[str, Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Dict=None   ):


				'''simple docstring'''
				lowercase_	    =   count_samples(snake_case__   )
				lowercase_	    =   tf.data.Dataset.from_tensor_slices(snake_case__   )
				if shuffle:
								lowercase_	    =   dataset.shuffle(len(snake_case__   )   )
				lowercase_	    =   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
				lowercase_	    =   dataset.apply(tf.data.experimental.assert_cardinality(snake_case__   )   )
				lowercase_	    =   dataset.map(snake_case__		,  num_parallel_calls=snake_case__   )
				if shuffle:
								assert shuffle_buffer_size is not None
								lowercase_	    =   dataset.shuffle(args.shuffle_buffer_size   )
				lowercase_	    =   dataset.batch(snake_case__		,  drop_remainder=snake_case__   )
				lowercase_	    =   dataset.map(snake_case__		,  num_parallel_calls=snake_case__   )
				lowercase_	    =   dataset.prefetch(snake_case__   )
				return dataset




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				if not args.no_tpu:
								lowercase_	    =   initialize_tpu(snake_case__   )
								lowercase_	    =   tf.distribute.TPUStrategy(snake_case__   )
				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(snake_case__   )

				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(snake_case__   )
								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=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   ):
								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(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.
				lowercase_	    =   DataCollatorForLanguageModeling(
				    tokenizer=snake_case__		,  mlm_probability=args.mlm_probability		,  mlm=snake_case__		,  return_tensors='''tf'''   )

				def mask_with_collator(snake_case__:   str   ):
								# 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(snake_case__   )		,  mask_token_id=tokenizer.mask_token_id		,  special_tokens_mask=snake_case__		,  )
								return batch

				lowercase_	    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

				lowercase_	    =   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		,  )

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  )

				lowercase_	    =   []
				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)




