'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

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


_lowercase		:				Dict	       =		logging.getLogger(__name__)

_lowercase		:				int	       =		tf.data.AUTOTUNE




def 							lowerCamelCase			(   )    ->   List[str]:
							lowercase_      :    Union[str, Any]       		= argparse.ArgumentParser(description="""Train a masked language model on TPU."""				)
							parser.add_argument(
							    """--pretrained_model_config"""   ,   type=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""   ,   )

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

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

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

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

							parser.add_argument(
							    """--max_length"""   ,   type=UpperCAmelCase__   ,   default=512   ,   help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""   ,   )

							parser.add_argument(
							    """--mlm_probability"""   ,   type=UpperCAmelCase__   ,   default=0.15   ,   help="""Fraction of tokens to mask during training."""   ,   )

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

							lowercase_      :    List[str]       		= parser.parse_args()
							return args




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   str:
							try:
														if args.tpu_name:
																					lowercase_      :    Tuple       		= tf.distribute.cluster_resolver.TPUClusterResolver(
																					    args.tpu_name   ,   zone=args.tpu_zone   ,   project=args.gcp_project				)
														else:
																					lowercase_      :    Union[str, 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(UpperCAmelCase__				)
							tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase__				)

							return tpu




def 							lowerCamelCase			(   UpperCAmelCase__ :      int				)    ->   Optional[int]:
							lowercase_      :    Dict       		= 0
							for file in file_list:
														lowercase_      :    List[Any]       		= file.split("""/"""				)[-1]
														lowercase_      :    Union[str, Any]       		= re.search(R"""-\d+-(\d+)\.tfrecord"""   ,   UpperCAmelCase__				).group(1				)
														lowercase_      :    Optional[int]       		= int(UpperCAmelCase__				)
														num_samples += sample_count

							return num_samples




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




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]				)    ->   Optional[int]:
							if not args.no_tpu:
														lowercase_      :    Optional[Any]       		= initialize_tpu(UpperCAmelCase__				)
														lowercase_      :    Optional[int]       		= tf.distribute.TPUStrategy(UpperCAmelCase__				)
							else:
														lowercase_      :    Optional[int]       		= tf.distribute.OneDeviceStrategy(device="""/gpu:0"""				)

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

							lowercase_      :    Union[str, Any]       		= AutoTokenizer.from_pretrained(args.tokenizer				)
							lowercase_      :    int       		= AutoConfig.from_pretrained(args.pretrained_model_config				)
							lowercase_      :    int       		= tokenizer.vocab_size

							lowercase_      :    Any       		= 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_      :    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}.'''				)

							lowercase_      :    Tuple       		= count_samples(UpperCAmelCase__				)

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

							with strategy.scope():
														lowercase_      :    Any       		= TFAutoModelForMaskedLM.from_config(UpperCAmelCase__				)
														model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
														lowercase_		,		lowercase_      :    Any       		= create_optimizer(
														    num_train_steps=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   metrics=["""accuracy"""]				)

							def decode_fn(UpperCAmelCase__ :      Union[str, Any]				):
														lowercase_      :    List[str]       		= {
														    """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(UpperCAmelCase__   ,   UpperCAmelCase__				)

							# 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_      :    int       		= DataCollatorForLanguageModeling(
							    tokenizer=UpperCAmelCase__   ,   mlm_probability=args.mlm_probability   ,   mlm=UpperCAmelCase__   ,   return_tensors="""tf"""				)

							def mask_with_collator(UpperCAmelCase__ :      List[str]				):
														# TF really needs an isin() function
														lowercase_      :    Dict       		= (
														    ~tf.cast(batch["""attention_mask"""]   ,   tf.bool				)
														    | (batch["""input_ids"""] == tokenizer.cls_token_id)
														    | (batch["""input_ids"""] == tokenizer.sep_token_id)
														)
														lowercase_		,		lowercase_      :    Tuple       		= data_collator.tf_mask_tokens(
														    batch["""input_ids"""]   ,   vocab_size=len(UpperCAmelCase__				)   ,   mask_token_id=tokenizer.mask_token_id   ,   special_tokens_mask=UpperCAmelCase__   ,   )
														return batch

							lowercase_      :    Any       		= args.per_replica_batch_size * strategy.num_replicas_in_sync

							lowercase_      :    Optional[Any]       		= prepare_dataset(
							    UpperCAmelCase__   ,   decode_fn=UpperCAmelCase__   ,   mask_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__   ,   shuffle=UpperCAmelCase__   ,   shuffle_buffer_size=args.shuffle_buffer_size   ,   )

							lowercase_      :    Optional[Any]       		= prepare_dataset(
							    UpperCAmelCase__   ,   decode_fn=UpperCAmelCase__   ,   mask_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__   ,   shuffle=UpperCAmelCase__   ,   )

							lowercase_      :    Dict       		= []
							if args.hub_model_id:
														callbacks.append(
														    PushToHubCallback(output_dir=args.output_dir   ,   hub_model_id=args.hub_model_id   ,   tokenizer=UpperCAmelCase__				)				)

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

							model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
						_lowercase		:				List[Any]	       =		parse_args()
						main(args)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





