


import argparse
import logging
import os
import re

import tensorflow as tf

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


_snake_case            = logging.getLogger(__name__)

_snake_case            = tf.data.AUTOTUNE







def        lowerCAmelCase_			(  ):
      _A							:  Tuple    =		argparse.ArgumentParser(description="""Train a masked language model on TPU."""  )
      parser.add_argument(
          """--pretrained_model_config""",type=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=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=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=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=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=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=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=snake_case_,default=2**18,help="""Size of the shuffle buffer (in samples)""",)

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

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

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

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

      parser.add_argument(
          """--mlm_probability""",type=snake_case_,default=0.15,help="""Fraction of tokens to mask during training.""",)

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

      _A							:  Any    =		parser.parse_args()
      return args







def        lowerCAmelCase_			(  snake_case_  ):
      try:
            if args.tpu_name:
                  _A							:  List[str]    =		tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name,zone=args.tpu_zone,project=args.gcp_project  )
            else:
                  _A							:  Optional[int]    =		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(snake_case_  )
      tf.tpu.experimental.initialize_tpu_system(snake_case_  )

      return tpu







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		0
      for file in file_list:
            _A							:  Optional[Any]    =		file.split("""/"""  )[-1]
            _A							:  List[Any]    =		re.search(r"""-\d+-(\d+)\.tfrecord""",snake_case_  ).group(1  )
            _A							:  Dict    =		int(snake_case_  )
            num_samples += sample_count

      return num_samples







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_=None  ):
      _A							:  Tuple    =		count_samples(snake_case_  )
      _A							:  List[str]    =		tf.data.Dataset.from_tensor_slices(snake_case_  )
      if shuffle:
            _A							:  Union[str, Any]    =		dataset.shuffle(len(snake_case_  )  )
      _A							:  Dict    =		tf.data.TFRecordDataset(snake_case_,num_parallel_reads=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							:  Union[str, Any]    =		dataset.apply(tf.data.experimental.assert_cardinality(snake_case_  )  )
      _A							:  int    =		dataset.map(snake_case_,num_parallel_calls=snake_case_  )
      if shuffle:
            assert shuffle_buffer_size is not None
            _A							:  int    =		dataset.shuffle(args.shuffle_buffer_size  )
      _A							:  str    =		dataset.batch(snake_case_,drop_remainder=snake_case_  )
      _A							:  Tuple    =		dataset.map(snake_case_,num_parallel_calls=snake_case_  )
      _A							:  Any    =		dataset.prefetch(snake_case_  )
      return dataset







def        lowerCAmelCase_			(  snake_case_  ):
      if not args.no_tpu:
            _A							:  Dict    =		initialize_tpu(snake_case_  )
            _A							:  str    =		tf.distribute.TPUStrategy(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							:  List[Any]    =		AutoTokenizer.from_pretrained(args.tokenizer  )
      _A							:  int    =		AutoConfig.from_pretrained(args.pretrained_model_config  )
      _A							:  int    =		tokenizer.vocab_size

      _A							:  Tuple    =		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							:  Dict    =		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							:  Optional[Any]    =		count_samples(snake_case_  )

      _A							:  List[str]    =		num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      _A							:  Any    =		steps_per_epoch * args.num_epochs

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

      def decode_fn(snake_case_  ):
            _A							:  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(snake_case_,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=snake_case_,mlm_probability=args.mlm_probability,mlm=snake_case_,return_tensors="""tf"""  )

      def mask_with_collator(snake_case_  ):
            # TF really needs an isin() function
            _A							:  Tuple    =		(
                ~tf.cast(batch["""attention_mask"""],tf.bool  )
                | (batch["""input_ids"""] == tokenizer.cls_token_id)
                | (batch["""input_ids"""] == tokenizer.sep_token_id)
            )
            _A							:  str    =		data_collator.tf_mask_tokens(
                batch["""input_ids"""],vocab_size=len(snake_case_  ),mask_token_id=tokenizer.mask_token_id,special_tokens_mask=snake_case_,)
            return batch

      _A							:  List[str]    =		args.per_replica_batch_size * strategy.num_replicas_in_sync

      _A							:  Optional[int]    =		prepare_dataset(
          snake_case_,decode_fn=snake_case_,mask_fn=snake_case_,batch_size=snake_case_,shuffle=snake_case_,shuffle_buffer_size=args.shuffle_buffer_size,)

      _A							:  int    =		prepare_dataset(
          snake_case_,decode_fn=snake_case_,mask_fn=snake_case_,batch_size=snake_case_,shuffle=snake_case_,)

      _A							:  Optional[int]    =		[]
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir,hub_model_id=args.hub_model_id,tokenizer=snake_case_  )  )

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

      model.save_pretrained(args.output_dir  )


if __name__ == "__main__":
  _snake_case            = parse_args()
  main(args)







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

