






import argparse
import logging
import os
import re

import tensorflow as tf

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


__lowerCamelCase	:		Any										=   logging.getLogger(__name__)

__lowerCamelCase	:		Optional[int]										=   tf.data.AUTOTUNE







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Any							       =argparse.ArgumentParser(description="""Train a masked language model on TPU."""	)
 parser.add_argument(
     """--pretrained_model_config"""		,  type=_a		,  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=_a		,  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=_a		,  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=_a		,  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=_a		,  help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""		,  )

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

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

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

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

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

 parser.add_argument(
     """--mlm_probability"""		,  type=_a		,  default=0.1_5		,  help="""Fraction of tokens to mask during training."""		,  )

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

 snake_case__  :    Optional[int]							       =parser.parse_args()
 return args







def 			A__							(      _a  :					Optional[int]	):





 '''simple docstring'''







 try:
  if args.tpu_name:
   snake_case__  :    List[str]							       =tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name		,  zone=args.tpu_zone		,  project=args.gcp_project	)
  else:
   snake_case__  :    List[str]							       =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(_a	)
 tf.tpu.experimental.initialize_tpu_system(_a	)

 return tpu







def 			A__							(      _a  :					List[str]	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =0
 for file in file_list:
  snake_case__  :    Tuple							       =file.split("""/"""	)[-1]
  snake_case__  :    Any							       =re.search(R"""-\d+-(\d+)\.tfrecord"""		,  _a	).group(1	)
  snake_case__  :    str							       =int(_a	)
  num_samples += sample_count

 return num_samples







def 			A__							(      _a  :					List[str]		,  _a  :					str		,  _a  :					str		,  _a  :					Dict		,  _a  :					List[str]		,  _a  :					List[Any]=None	):





 '''simple docstring'''







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







def 			A__							(      _a  :					Union[str, Any]	):





 '''simple docstring'''







 if not args.no_tpu:
  snake_case__  :    List[str]							       =initialize_tpu(_a	)
  snake_case__  :    str							       =tf.distribute.TPUStrategy(_a	)
 else:
  snake_case__  :    List[Any]							       =tf.distribute.OneDeviceStrategy(device="""/gpu:0"""	)

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

 snake_case__  :    Optional[int]							       =AutoTokenizer.from_pretrained(args.tokenizer	)
 snake_case__  :    List[str]							       =AutoConfig.from_pretrained(args.pretrained_model_config	)
 snake_case__  :    str							       =tokenizer.vocab_size

 snake_case__  :    List[str]							       =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__  :    Union[str, 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}."	)

 snake_case__  :    List[str]							       =count_samples(_a	)

 snake_case__  :    Union[str, Any]							       =num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
 snake_case__  :    str							       =steps_per_epoch * args.num_epochs

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

 def decode_fn(_a  :					Union[str, Any]	):
  snake_case__  :    Union[str, Any]							       ={
      """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(_a		,  _a	)

 # 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__  :    Optional[int]							       =DataCollatorForLanguageModeling(
     tokenizer=_a		,  mlm_probability=args.mlm_probability		,  mlm=_a		,  return_tensors="""tf"""	)

 def mask_with_collator(_a  :					Optional[int]	):
  # TF really needs an isin() function
  snake_case__  :    List[Any]							       =(
      ~tf.cast(batch["""attention_mask"""]		,  tf.bool	)
      | (batch["""input_ids"""] == tokenizer.cls_token_id)
      | (batch["""input_ids"""] == tokenizer.sep_token_id)
  )
  snake_case__  :    int							       =data_collator.tf_mask_tokens(
      batch["""input_ids"""]		,  vocab_size=len(_a	)		,  mask_token_id=tokenizer.mask_token_id		,  special_tokens_mask=_a		,  )
  return batch

 snake_case__  :    int							       =args.per_replica_batch_size * strategy.num_replicas_in_sync

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

 snake_case__  :    Any							       =prepare_dataset(
     _a		,  decode_fn=_a		,  mask_fn=_a		,  batch_size=_a		,  shuffle=_a		,  )

 snake_case__  :    List[Any]							       =[]
 if args.hub_model_id:
  callbacks.append(
      PushToHubCallback(output_dir=args.output_dir		,  hub_model_id=args.hub_model_id		,  tokenizer=_a	)	)

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

 model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
       __lowerCamelCase	:		Dict										=   parse_args()
       main(args)













import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )







class  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=3_2				,			a=5				,			a=4				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=4				,			):
       snake_case__  :    Any							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    List[Any]							       =seq_length
       snake_case__  :    str							       =is_training
       snake_case__  :    Union[str, Any]							       =use_attention_mask
       snake_case__  :    str							       =use_token_type_ids
       snake_case__  :    int							       =use_labels
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    List[str]							       =hidden_size
       snake_case__  :    Dict							       =num_hidden_layers
       snake_case__  :    Optional[Any]							       =num_attention_heads
       snake_case__  :    List[str]							       =intermediate_size
       snake_case__  :    str							       =hidden_act
       snake_case__  :    Union[str, Any]							       =hidden_dropout_prob
       snake_case__  :    Tuple							       =attention_probs_dropout_prob
       snake_case__  :    Tuple							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Optional[Any]							       =type_sequence_label_size
       snake_case__  :    str							       =initializer_range
       snake_case__  :    List[Any]							       =num_choices
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Tuple							       =None
       if self.use_attention_mask:
        snake_case__  :    Union[str, Any]							       =random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__  :    Union[str, Any]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Tuple							       =RoFormerConfig(
           vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			is_decoder=a				,			initializer_range=self.initializer_range				,			)

       return config, input_ids, token_type_ids, attention_mask



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Optional[Any]							       =config_and_inputs
       snake_case__  :    Dict							       ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_flax
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						str										=							True

      _a							:						Optional[Any]										=							(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =FlaxRoFormerModelTester(self   )



      @slow
      def 						lowercase__					(     self   ):
       for model_class_name in self.all_model_classes:
        snake_case__  :    Tuple							       =model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,			from_pt=a   )
        snake_case__  :    List[Any]							       =model(np.ones((1, 1)   )   )
        self.assertIsNotNone(a   )







@require_flax
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
       snake_case__  :    Tuple							       =jnp.array([[0, 1, 2, 3, 4, 5]]   )
       snake_case__  :    str							       =model(a   )[0]

       snake_case__  :    List[str]							       =5_0_0_0_0

       snake_case__  :    str							       =(1, 6, vocab_size)
       self.assertEqual(output.shape				,			a   )

       snake_case__  :    Optional[int]							       =jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,			a				,			atol=1e-4   )   )





