import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCAmelCase					      =							logging.getLogger(__name__)

UpperCAmelCase					      =							tf.data.AUTOTUNE






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

      parser.add_argument(
          "--gcp_project"			,						type=lowerCAmelCase_			,						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=lowerCAmelCase_			,						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=lowerCAmelCase_			,						default=2**1_8			,						help="Size of the shuffle buffer (in samples)"			,						)

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

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

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

      parser.add_argument(
          "--max_length"			,						type=lowerCAmelCase_			,						default=5_1_2			,						help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"			,						)

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

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

      snake_case_  :      List[Any]  	=							parser.parse_args()
      return args






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Optional[int]  ):
      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(lowerCAmelCase_  )
      tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase_  )

      return tpu






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

      return num_samples






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






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			str  ):
      if not args.no_tpu:
            snake_case_  :      Optional[int]  	=							initialize_tpu(lowerCAmelCase_  )
            snake_case_  :      str  	=							tf.distribute.TPUStrategy(lowerCAmelCase_  )
      else:
            snake_case_  :      Optional[Any]  	=							tf.distribute.OneDeviceStrategy(device="/gpu:0"  )

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

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

      snake_case_  :      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_  :      int  	=							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_  :      Tuple  	=							count_samples(lowerCAmelCase_  )

      snake_case_  :      Any  	=							num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      snake_case_  :      Any  	=							steps_per_epoch * args.num_epochs

      with strategy.scope():
            snake_case_  :      Optional[Any]  	=							TFAutoModelForMaskedLM.from_config(lowerCAmelCase_  )
            model(model.dummy_inputs  )  # Pass some dummy inputs through the model to ensure all the weights are built
            snake_case_  :      Dict  	=							create_optimizer(
                num_train_steps=lowerCAmelCase_			,						num_warmup_steps=total_train_steps // 2_0			,						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=lowerCAmelCase_			,						metrics=["accuracy"]  )

      def decode_fn(lowerCAmelCase_:			str  ):
            snake_case_  :      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(lowerCAmelCase_			,						lowerCAmelCase_  )

      # 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[Any]  	=							DataCollatorForLanguageModeling(
          tokenizer=lowerCAmelCase_			,						mlm_probability=args.mlm_probability			,						mlm=lowerCAmelCase_			,						return_tensors="tf"  )

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

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

      snake_case_  :      Dict  	=							prepare_dataset(
          lowerCAmelCase_			,						decode_fn=lowerCAmelCase_			,						mask_fn=lowerCAmelCase_			,						batch_size=lowerCAmelCase_			,						shuffle=lowerCAmelCase_			,						shuffle_buffer_size=args.shuffle_buffer_size			,						)

      snake_case_  :      List[str]  	=							prepare_dataset(
          lowerCAmelCase_			,						decode_fn=lowerCAmelCase_			,						mask_fn=lowerCAmelCase_			,						batch_size=lowerCAmelCase_			,						shuffle=lowerCAmelCase_			,						)

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

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

      model.save_pretrained(args.output_dir  )


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









from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




