'''simple docstring'''

import argparse
import logging
import os
import re

import tensorflow as tf

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


__a: Optional[int]          =   logging.getLogger(__name__)

__a: Dict          =   tf.data.AUTOTUNE
def 			__UpperCamelCase       (   ):
 lowercase__				:    Dict  =					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.'''     )

 lowercase__				:    Optional[int]  =					parser.parse_args()
 return args
def 			__UpperCamelCase       (   UpperCAmelCase     ):

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

 return tpu
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Dict  =					0
 for file in file_list:
  lowercase__				:    Union[str, Any]  =					file.split('''/'''     )[-1]
  lowercase__				:    Tuple  =					re.search(r'''-\d+-(\d+)\.tfrecord'''		,     __A     ).group(1     )
  lowercase__				:    List[str]  =					int(__A     )
  num_samples += sample_count

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

 if not args.no_tpu:
  lowercase__				:    Dict  =					initialize_tpu(__A     )
  lowercase__				:    int  =					tf.distribute.TPUStrategy(__A     )
 else:
  lowercase__				:    int  =					tf.distribute.OneDeviceStrategy(device='''/gpu:0'''     )

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

 lowercase__				:    Tuple  =					AutoTokenizer.from_pretrained(args.tokenizer     )
 lowercase__				:    List[str]  =					AutoConfig.from_pretrained(args.pretrained_model_config     )
 lowercase__				:    Any  =					tokenizer.vocab_size

 lowercase__				:    List[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}."""     )
 lowercase__				:    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}."""     )

 lowercase__				:    List[Any]  =					count_samples(__A     )

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

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

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

 lowercase__				:    Optional[Any]  =					args.per_replica_batch_size * strategy.num_replicas_in_sync

 lowercase__				:    Dict  =					prepare_dataset(
     __A		,     decode_fn=__A		,     mask_fn=__A		,     batch_size=__A		,     shuffle=__A		,     shuffle_buffer_size=args.shuffle_buffer_size		,     )

 lowercase__				:    Any  =					prepare_dataset(
     __A		,     decode_fn=__A		,     mask_fn=__A		,     batch_size=__A		,     shuffle=__A		,     )

 lowercase__				:    int  =					[]
 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__":
  __a: List[Any]          =   parse_args()
  main(args)





from __future__ import annotations

UpperCAmelCase_      : Tuple  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int  ,    __A :			int				)	->		bool:

       """simple docstring"""

       for i in range(len(__A				)				):
              if board[row][i] == 1:
                     return False
       for i in range(len(__A				)				):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    -1  ,    -1				)				):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    len(__A				)				)				):
              if board[i][j] == 1:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int				)	->		bool:

       """simple docstring"""

       if row >= len(__A				):
              solution.append(__A				)
              printboard(__A				)
              print()
              return True
       for i in range(len(__A				)				):
              if is_safe(__A  ,    __A  ,    __A				):
                     a_					:		Any							=  1
                     solve(__A  ,    row + 1				)
                     a_					:		Tuple							=  0
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]				)	->		None:

       """simple docstring"""

       for i in range(len(__A				)				):
              for j in range(len(__A				)				):
                     if board[i][j] == 1:
                            print('Q'  ,    end=' '				)
                     else:
                            print('.'  ,    end=' '				)
              print()


# n=int(input("The no. of queens"))
UpperCAmelCase_      : List[str]  					=       8
UpperCAmelCase_      : str  					=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


