
'''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					=			logging.getLogger(__name__)

__lowerCAmelCase					=			tf.data.AUTOTUNE






def    __SCREAMING_SNAKE_CASE (    ):
							_snake_case  =    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.""" )

							_snake_case  =    parser.parse_args()
							return args






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):


							try:
														if args.tpu_name:
																					_snake_case  =    tf.distribute.cluster_resolver.TPUClusterResolver(
																					    args.tpu_name				,   zone=args.tpu_zone				,   project=args.gcp_project )
														else:
																					_snake_case  =    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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    0
							for file in file_list:
														_snake_case  =    file.split("""/""" )[-1]
														_snake_case  =    re.search(R"""-\d+-(\d+)\.tfrecord"""				,   _snake_case ).group(1 )
														_snake_case  =    int(_snake_case )
														num_samples += sample_count

							return num_samples






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=None ):
							_snake_case  =    count_samples(_snake_case )
							_snake_case  =    tf.data.Dataset.from_tensor_slices(_snake_case )
							if shuffle:
														_snake_case  =    dataset.shuffle(len(_snake_case ) )
							_snake_case  =    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
							_snake_case  =    dataset.apply(tf.data.experimental.assert_cardinality(_snake_case ) )
							_snake_case  =    dataset.map(_snake_case				,   num_parallel_calls=_snake_case )
							if shuffle:
														assert shuffle_buffer_size is not None
														_snake_case  =    dataset.shuffle(args.shuffle_buffer_size )
							_snake_case  =    dataset.batch(_snake_case				,   drop_remainder=_snake_case )
							_snake_case  =    dataset.map(_snake_case				,   num_parallel_calls=_snake_case )
							_snake_case  =    dataset.prefetch(_snake_case )
							return dataset






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):


							if not args.no_tpu:
														_snake_case  =    initialize_tpu(_snake_case )
														_snake_case  =    tf.distribute.TPUStrategy(_snake_case )
							else:
														_snake_case  =    tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )

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

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

							_snake_case  =    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}.""" )
							_snake_case  =    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}.""" )

							_snake_case  =    count_samples(_snake_case )

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

							with strategy.scope():
														_snake_case  =    TFAutoModelForMaskedLM.from_config(_snake_case )
														model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
														_snake_case,				_snake_case  =    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(_SCREAMING_SNAKE_CASE ):
														_snake_case  =    {
														    """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.
							_snake_case  =    DataCollatorForLanguageModeling(
							    tokenizer=_snake_case				,   mlm_probability=args.mlm_probability				,   mlm=_snake_case				,   return_tensors="""tf""" )

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

							_snake_case  =    args.per_replica_batch_size * strategy.num_replicas_in_sync

							_snake_case  =    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				,   )

							_snake_case  =    prepare_dataset(
							    _snake_case				,   decode_fn=_snake_case				,   mask_fn=_snake_case				,   batch_size=_snake_case				,   shuffle=_snake_case				,   )

							_snake_case  =    []
							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__":
				__lowerCAmelCase					=			parse_args()
				main(args)





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

