





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

lowercase__       : int         =       tf.data.AUTOTUNE






def 			_lowerCAmelCase     (    )	->	Tuple:
     __A		:					Any						=			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=5_12					,				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.'     )

     __A		:					Union[str, Any]						=			parser.parse_args()
     return args






def 			_lowerCAmelCase     (    __snake_case    :					Dict     )	->	str:
     try:
          if args.tpu_name:
               __A		:					Tuple						=			tf.distribute.cluster_resolver.TPUClusterResolver(
                   args.tpu_name					,				zone=args.tpu_zone					,				project=args.gcp_project     )
          else:
               __A		:					Optional[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(SCREAMING_SNAKE_CASE__     )
     tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE__     )

     return tpu






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]     )	->	Optional[Any]:
     __A		:					int						=			0
     for file in file_list:
          __A		:					Union[str, Any]						=			file.split('/'     )[-1]
          __A		:					Optional[int]						=			re.search(r'-\d+-(\d+)\.tfrecord'					,				SCREAMING_SNAKE_CASE__     ).group(1     )
          __A		:					List[str]						=			int(SCREAMING_SNAKE_CASE__     )
          num_samples += sample_count

     return num_samples






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int					,				__snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[str]=None     )	->	int:
     __A		:					Union[str, Any]						=			count_samples(SCREAMING_SNAKE_CASE__     )
     __A		:					List[str]						=			tf.data.Dataset.from_tensor_slices(SCREAMING_SNAKE_CASE__     )
     if shuffle:
          __A		:					Union[str, Any]						=			dataset.shuffle(len(SCREAMING_SNAKE_CASE__     )     )
     __A		:					List[Any]						=			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
     __A		:					Optional[int]						=			dataset.apply(tf.data.experimental.assert_cardinality(SCREAMING_SNAKE_CASE__     )     )
     __A		:					Optional[Any]						=			dataset.map(SCREAMING_SNAKE_CASE__					,				num_parallel_calls=SCREAMING_SNAKE_CASE__     )
     if shuffle:
          assert shuffle_buffer_size is not None
          __A		:					Tuple						=			dataset.shuffle(args.shuffle_buffer_size     )
     __A		:					Dict						=			dataset.batch(SCREAMING_SNAKE_CASE__					,				drop_remainder=SCREAMING_SNAKE_CASE__     )
     __A		:					int						=			dataset.map(SCREAMING_SNAKE_CASE__					,				num_parallel_calls=SCREAMING_SNAKE_CASE__     )
     __A		:					str						=			dataset.prefetch(SCREAMING_SNAKE_CASE__     )
     return dataset






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[int]:
     if not args.no_tpu:
          __A		:					int						=			initialize_tpu(SCREAMING_SNAKE_CASE__     )
          __A		:					List[Any]						=			tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE__     )
     else:
          __A		:					Optional[int]						=			tf.distribute.OneDeviceStrategy(device='/gpu:0'     )

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

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

     __A		:					Union[str, 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}.'     )
     __A		:					Any						=			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}.'     )

     __A		:					List[str]						=			count_samples(SCREAMING_SNAKE_CASE__     )

     __A		:					int						=			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
     __A		:					str						=			steps_per_epoch * args.num_epochs

     with strategy.scope():
          __A		:					List[Any]						=			TFAutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE__     )
          model(model.dummy_inputs     )  # Pass some dummy inputs through the model to ensure all the weights are built
          __A					,__A		:					List[str]						=			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(__snake_case    :					List[str]     ):
          __A		:					Optional[int]						=			{
              '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.
     __A		:					List[Any]						=			DataCollatorForLanguageModeling(
         tokenizer=SCREAMING_SNAKE_CASE__					,				mlm_probability=args.mlm_probability					,				mlm=SCREAMING_SNAKE_CASE__					,				return_tensors='tf'     )

     def mask_with_collator(__snake_case    :					List[Any]     ):
          # TF really needs an isin() function
          __A		:					Any						=			(
              ~tf.cast(batch['attention_mask']					,				tf.bool     )
              | (batch['input_ids'] == tokenizer.cls_token_id)
              | (batch['input_ids'] == tokenizer.sep_token_id)
          )
          __A					,__A		:					Union[str, Any]						=			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

     __A		:					int						=			args.per_replica_batch_size * strategy.num_replicas_in_sync

     __A		:					Dict						=			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					,				)

     __A		:					Dict						=			prepare_dataset(
         SCREAMING_SNAKE_CASE__					,				decode_fn=SCREAMING_SNAKE_CASE__					,				mask_fn=SCREAMING_SNAKE_CASE__					,				batch_size=SCREAMING_SNAKE_CASE__					,				shuffle=SCREAMING_SNAKE_CASE__					,				)

     __A		:					List[str]						=			[]
     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__":
  lowercase__       : Optional[int]         =       parse_args()
  main(args)







'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)