import argparse
import logging
import os
import re

import tensorflow as tf

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


lowerCamelCase       :	Dict									=     logging.getLogger(__name__)

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

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					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=5_12			,							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.'  )

      lowerCamelCase_	  =					parser.parse_args()
      return args

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      try:
            if args.tpu_name:
                  lowerCamelCase_	  =					tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name			,							zone=args.tpu_zone			,							project=args.gcp_project  )
            else:
                  lowerCamelCase_	  =					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     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for file in file_list:
            lowerCamelCase_	  =					file.split('/'  )[-1]
            lowerCamelCase_	  =					re.search(r'-\d+-(\d+)\.tfrecord'			,							_UpperCamelCase  ).group(1  )
            lowerCamelCase_	  =					int(_UpperCamelCase  )
            num_samples += sample_count

      return num_samples

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: int			,							lowercase			: Dict			,							lowercase			: Any			,							lowercase			: Tuple			,							lowercase			: int=None  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      if not args.no_tpu:
            lowerCamelCase_	  =					initialize_tpu(_UpperCamelCase  )
            lowerCamelCase_	  =					tf.distribute.TPUStrategy(_UpperCamelCase  )
      else:
            lowerCamelCase_	  =					tf.distribute.OneDeviceStrategy(device='/gpu:0'  )

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

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

      lowerCamelCase_	  =					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}."""  )
      lowerCamelCase_	  =					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}."""  )

      lowerCamelCase_	  =					count_samples(_UpperCamelCase  )

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

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

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

      lowerCamelCase_	  =					args.per_replica_batch_size * strategy.num_replicas_in_sync

      lowerCamelCase_	  =					prepare_dataset(
          _UpperCamelCase			,							decode_fn=_UpperCamelCase			,							mask_fn=_UpperCamelCase			,							batch_size=_UpperCamelCase			,							shuffle=_UpperCamelCase			,							shuffle_buffer_size=args.shuffle_buffer_size			,							)

      lowerCamelCase_	  =					prepare_dataset(
          _UpperCamelCase			,							decode_fn=_UpperCamelCase			,							mask_fn=_UpperCamelCase			,							batch_size=_UpperCamelCase			,							shuffle=_UpperCamelCase			,							)

      lowerCamelCase_	  =					[]
      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__":
 lowerCamelCase       :	Optional[Any]									=     parse_args()
 main(args)









from __future__ import annotations

from fractions import Fraction

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: int  ):


      '''simple docstring'''



      return (
          num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
      )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					[]
      lowerCamelCase_	  =					11
      lowerCamelCase_	  =					int('1' + '0' * digit_len  )
      for num in range(lowercase			,							lowercase  ):
            while den <= 99:
                  if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                        if is_digit_cancelling(lowercase			,							lowercase  ):
                              solutions.append(f"""{num}/{den}"""  )
                  den += 1
            num += 1
            lowerCamelCase_	  =					10
      return solutions

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int = 2  ):


      '''simple docstring'''



      lowerCamelCase_	  =					1.0
      for fraction in fraction_list(lowercase  ):
            lowerCamelCase_	  =					Fraction(lowercase  )
            result *= frac.denominator / frac.numerator
      return int(lowercase  )


if __name__ == "__main__":
 print(solution())



