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

lowerCAmelCase__  :  List[str]						     =	tf.data.AUTOTUNE



def   a_						(					):
				UpperCAmelCase__             =							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**1_8					,			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=5_1_2					,			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.'       )

				UpperCAmelCase__             =							parser.parse_args()
				return args



def   a_						(					lowerCamelCase       ):
				try:
								if args.tpu_name:
												UpperCAmelCase__             =							tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name					,			zone=args.tpu_zone					,			project=args.gcp_project       )
								else:
												UpperCAmelCase__             =							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   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							0
				for file in file_list:
								UpperCAmelCase__             =							file.split('/'       )[-1]
								UpperCAmelCase__             =							re.search(r'-\d+-(\d+)\.tfrecord'					,			lowerCAmelCase_       ).group(1       )
								UpperCAmelCase__             =							int(lowerCAmelCase_       )
								num_samples += sample_count

				return num_samples



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



def   a_						(					lowerCamelCase       ):
				if not args.no_tpu:
								UpperCAmelCase__             =							initialize_tpu(lowerCAmelCase_       )
								UpperCAmelCase__             =							tf.distribute.TPUStrategy(lowerCAmelCase_       )
				else:
								UpperCAmelCase__             =							tf.distribute.OneDeviceStrategy(device='/gpu:0'       )

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

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

				UpperCAmelCase__             =							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}.'''       )
				UpperCAmelCase__             =							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}.'''       )

				UpperCAmelCase__             =							count_samples(lowerCAmelCase_       )

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

				with strategy.scope():
								UpperCAmelCase__             =							TFAutoModelForMaskedLM.from_config(lowerCAmelCase_       )
								model(model.dummy_inputs       )  # Pass some dummy inputs through the model to ensure all the weights are built
								UpperCAmelCase__      ,	UpperCAmelCase__             =							create_optimizer(
								    num_train_steps=lowerCAmelCase_					,			num_warmup_steps=total_train_steps // 2_0					,			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       ):
								UpperCAmelCase__             =							{
								    '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.
				UpperCAmelCase__             =							DataCollatorForLanguageModeling(
				    tokenizer=lowerCAmelCase_					,			mlm_probability=args.mlm_probability					,			mlm=lowerCAmelCase_					,			return_tensors='tf'       )

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

				UpperCAmelCase__             =							args.per_replica_batch_size * strategy.num_replicas_in_sync

				UpperCAmelCase__             =							prepare_dataset(
				    lowerCAmelCase_					,			decode_fn=lowerCAmelCase_					,			mask_fn=lowerCAmelCase_					,			batch_size=lowerCAmelCase_					,			shuffle=lowerCAmelCase_					,			shuffle_buffer_size=args.shuffle_buffer_size					,			)

				UpperCAmelCase__             =							prepare_dataset(
				    lowerCAmelCase_					,			decode_fn=lowerCAmelCase_					,			mask_fn=lowerCAmelCase_					,			batch_size=lowerCAmelCase_					,			shuffle=lowerCAmelCase_					,			)

				UpperCAmelCase__             =							[]
				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__":
						lowerCAmelCase__  :  Union[str, Any]						     =	parse_args()
						main(args)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

