


"""simple docstring"""
import argparse
import logging
import os
import re

import tensorflow as tf

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


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

__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      tf.data.AUTOTUNE







def   _a			( )      ->			Optional[int]:
							snake_case_          =						argparse.ArgumentParser(description="""Train a masked language model on TPU."""			)
							parser.add_argument(
							    """--pretrained_model_config"""			,	type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE			,	default=2**18			,	help="""Size of the shuffle buffer (in samples)"""			,	)

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

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

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

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

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

							snake_case_          =						parser.parse_args()
							return args







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[str]:
							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(_SCREAMING_SNAKE_CASE			)
							tf.tpu.experimental.initialize_tpu_system(_SCREAMING_SNAKE_CASE			)

							return tpu







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						0
							for file in file_list:
														snake_case_          =						file.split("""/"""			)[-1]
														snake_case_          =						re.search(r"""-\d+-(\d+)\.tfrecord"""			,	_SCREAMING_SNAKE_CASE			).group(1			)
														snake_case_          =						int(_SCREAMING_SNAKE_CASE			)
														num_samples += sample_count

							return num_samples







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=None			)      ->			Union[str, Any]:
							snake_case_          =						count_samples(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						tf.data.Dataset.from_tensor_slices(_SCREAMING_SNAKE_CASE			)
							if shuffle:
														snake_case_          =						dataset.shuffle(len(_SCREAMING_SNAKE_CASE			)			)
							snake_case_          =						tf.data.TFRecordDataset(_SCREAMING_SNAKE_CASE			,	num_parallel_reads=_SCREAMING_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(_SCREAMING_SNAKE_CASE			)			)
							snake_case_          =						dataset.map(_SCREAMING_SNAKE_CASE			,	num_parallel_calls=_SCREAMING_SNAKE_CASE			)
							if shuffle:
														assert shuffle_buffer_size is not None
														snake_case_          =						dataset.shuffle(args.shuffle_buffer_size			)
							snake_case_          =						dataset.batch(_SCREAMING_SNAKE_CASE			,	drop_remainder=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						dataset.map(_SCREAMING_SNAKE_CASE			,	num_parallel_calls=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						dataset.prefetch(_SCREAMING_SNAKE_CASE			)
							return dataset







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Any:
							if not args.no_tpu:
														snake_case_          =						initialize_tpu(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						tf.distribute.TPUStrategy(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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=_SCREAMING_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=_SCREAMING_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(_SCREAMING_SNAKE_CASE			,	_SCREAMING_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=_SCREAMING_SNAKE_CASE			,	mlm_probability=args.mlm_probability			,	mlm=_SCREAMING_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(_SCREAMING_SNAKE_CASE			)			,	mask_token_id=tokenizer.mask_token_id			,	special_tokens_mask=_SCREAMING_SNAKE_CASE			,	)
														return batch

							snake_case_          =						args.per_replica_batch_size * strategy.num_replicas_in_sync

							snake_case_          =						prepare_dataset(
							    _SCREAMING_SNAKE_CASE			,	decode_fn=_SCREAMING_SNAKE_CASE			,	mask_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			,	shuffle=_SCREAMING_SNAKE_CASE			,	shuffle_buffer_size=args.shuffle_buffer_size			,	)

							snake_case_          =						prepare_dataset(
							    _SCREAMING_SNAKE_CASE			,	decode_fn=_SCREAMING_SNAKE_CASE			,	mask_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			,	shuffle=_SCREAMING_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=_SCREAMING_SNAKE_CASE			)			)

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

							model.save_pretrained(args.output_dir			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Tuple						=      parse_args()
				main(args)







"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """mobilenet_v1"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: List[Any]=224						,    UpperCAmelCase_							: List[Any]=1.0						,    UpperCAmelCase_							: Any=8						,    UpperCAmelCase_							: int="relu6"						,    UpperCAmelCase_							: str=True						,    UpperCAmelCase_							: List[str]=0.999						,    UpperCAmelCase_							: Optional[Any]=0.02						,    UpperCAmelCase_							: List[Any]=0.001						,    **UpperCAmelCase_							: Any						,    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														if depth_multiplier <= 0:
																					raise ValueError("""depth_multiplier must be greater than zero."""    )

														snake_case_          =						num_channels
														snake_case_          =						image_size
														snake_case_          =						depth_multiplier
														snake_case_          =						min_depth
														snake_case_          =						hidden_act
														snake_case_          =						tf_padding
														snake_case_          =						classifier_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						layer_norm_eps





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						int							 =      version.parse("""1.11""")



							@property
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														return OrderedDict([("""pixel_values""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task == "image-classification":
																					return OrderedDict([("""logits""", {0: """batch"""})]    )
														else:
																					return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->float:



														"""simple docstring"""



														return 1E-4



