


'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Optional[int] = None      ,       ):
 lowerCamelCase_  				=						{}

 if train_file is not None:
  lowerCamelCase_  				=						[train_file]
 if eval_file is not None:
  lowerCamelCase_  				=						[eval_file]
 if test_file is not None:
  lowerCamelCase_  				=						[test_file]

 lowerCamelCase_  				=						datasets.load_dataset("csv"      ,       data_files=lowerCAmelCase__	)
 lowerCamelCase_  				=						list(ds[list(files.keys()	)[0]].features.keys()	)
 lowerCamelCase_  				=						features_name.pop(lowerCAmelCase__	)
 lowerCamelCase_  				=						list(set(ds[list(files.keys()	)[0]][label_name]	)	)
 lowerCamelCase_  				=						{label: i for i, label in enumerate(lowerCAmelCase__	)}
 lowerCamelCase_  				=						tokenizer.model_input_names
 lowerCamelCase_  				=						{}

 if len(lowerCAmelCase__	) == 1:
  for k in files.keys():
   lowerCamelCase_  				=						ds[k].map(
       lambda UpperCAmelCase_	: tokenizer.batch_encode_plus(
           example[features_name[0]]      ,       truncation=lowerCAmelCase__      ,       max_length=lowerCAmelCase__      ,       padding="max_length"	)      ,       batched=lowerCAmelCase__      ,       )
 elif len(lowerCAmelCase__	) == 2:
  for k in files.keys():
   lowerCamelCase_  				=						ds[k].map(
       lambda UpperCAmelCase_	: tokenizer.batch_encode_plus(
           (example[features_name[0]], example[features_name[1]])      ,       truncation=lowerCAmelCase__      ,       max_length=lowerCAmelCase__      ,       padding="max_length"      ,       )      ,       batched=lowerCAmelCase__      ,       )

 def gen_train():
  for ex in transformed_ds[datasets.Split.TRAIN]:
   lowerCamelCase_  				=						{k: v for k, v in ex.items() if k in input_names}
   lowerCamelCase_  				=						labelaid[ex[label_name]]
   yield (d, label)

 def gen_val():
  for ex in transformed_ds[datasets.Split.VALIDATION]:
   lowerCamelCase_  				=						{k: v for k, v in ex.items() if k in input_names}
   lowerCamelCase_  				=						labelaid[ex[label_name]]
   yield (d, label)

 def gen_test():
  for ex in transformed_ds[datasets.Split.TEST]:
   lowerCamelCase_  				=						{k: v for k, v in ex.items() if k in input_names}
   lowerCamelCase_  				=						labelaid[ex[label_name]]
   yield (d, label)

 lowerCamelCase_  				=						(
     tf.data.Dataset.from_generator(
         lowerCAmelCase__      ,       ({k: tf.intaa for k in input_names}, tf.intaa)      ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))      ,       )
     if datasets.Split.TRAIN in transformed_ds
     else None
 )

 if train_ds is not None:
  lowerCamelCase_  				=						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

 lowerCamelCase_  				=						(
     tf.data.Dataset.from_generator(
         lowerCAmelCase__      ,       ({k: tf.intaa for k in input_names}, tf.intaa)      ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))      ,       )
     if datasets.Split.VALIDATION in transformed_ds
     else None
 )

 if val_ds is not None:
  lowerCamelCase_  				=						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

 lowerCamelCase_  				=						(
     tf.data.Dataset.from_generator(
         lowerCAmelCase__      ,       ({k: tf.intaa for k in input_names}, tf.intaa)      ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))      ,       )
     if datasets.Split.TEST in transformed_ds
     else None
 )

 if test_ds is not None:
  lowerCamelCase_  				=						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

 return train_ds, val_ds, test_ds, labelaid


a_						:       Union[str, Any]      			=				logging.getLogger(__name__)





@dataclass
class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						field(metadata={"help": "Which column contains the label"}    )
  _lowerCamelCase						=						field(default=__a    ,		metadata={"help": "The path of the training file"}    )
  _lowerCamelCase						=						field(default=__a    ,		metadata={"help": "The path of the development file"}    )
  _lowerCamelCase						=						field(default=__a    ,		metadata={"help": "The path of the test file"}    )
  _lowerCamelCase						=						field(
      default=1_28    ,		metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }    ,		)
  _lowerCamelCase						=						field(
      default=__a    ,		metadata={"help": "Overwrite the cached training and evaluation sets"}    )





@dataclass
class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
  _lowerCamelCase						=						field(
      default=__a    ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
  _lowerCamelCase						=						field(
      default=__a    ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
  _lowerCamelCase						=						field(default=__a    ,		metadata={"help": "Set this flag to use fast tokenization."}    )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  _lowerCamelCase						=						field(
      default=__a    ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,		)





def __snake_case      (   ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.
 lowerCamelCase_  				=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir	)
     and os.listdir(training_args.output_dir	)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
      " --overwrite_output_dir to overcome."	)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      ,       datefmt="%m/%d/%Y %H:%M:%S"      ,       level=logging.INFO      ,       )
 logger.info(
     F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '''
     F'''16-bits training: {training_args.fpaa}'''	)
 logger.info(F'''Training/evaluation parameters {training_args}'''	)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       )

 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						get_tfds(
     train_file=data_args.train_file      ,       eval_file=data_args.dev_file      ,       test_file=data_args.test_file      ,       tokenizer=lowerCAmelCase__      ,       label_column_id=data_args.label_column_id      ,       max_seq_length=data_args.max_seq_length      ,       )

 lowerCamelCase_  				=						AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,       num_labels=len(lowerCAmelCase__	)      ,       labelaid=lowerCAmelCase__      ,       idalabel={id: label for label, id in labelaid.items()}      ,       finetuning_task="text-classification"      ,       cache_dir=model_args.cache_dir      ,       )

 with training_args.strategy.scope():
  lowerCamelCase_  				=						TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path      ,       from_pt=bool(".bin" in model_args.model_name_or_path	)      ,       config=lowerCAmelCase__      ,       cache_dir=model_args.cache_dir      ,       )

 def compute_metrics(UpperCAmelCase_		:		int	) -> Dict:
  lowerCamelCase_  				=						np.argmax(p.predictions      ,       axis=1	)

  return {"acc": (preds == p.label_ids).mean()}

 # Initialize our Trainer
 lowerCamelCase_  				=						TFTrainer(
     model=lowerCAmelCase__      ,       args=lowerCAmelCase__      ,       train_dataset=lowerCAmelCase__      ,       eval_dataset=lowerCAmelCase__      ,       compute_metrics=lowerCAmelCase__      ,       )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir	)

 # Evaluation
 lowerCamelCase_  				=						{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"	)
  lowerCamelCase_  				=						trainer.evaluate()
  lowerCamelCase_  				=						os.path.join(training_args.output_dir      ,       "eval_results.txt"	)

  with open(lowerCAmelCase__      ,       "w"	) as writer:
   logger.info("***** Eval results *****"	)

   for key, value in result.items():
    logger.info(F'''  {key} = {value}'''	)
    writer.write(F'''{key} = {value}\n'''	)

   results.update(lowerCAmelCase__	)

 return results


if __name__ == "__main__":
     main()







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

 lowerCamelCase_  				=						nums[0]
 for i in range(1      ,       len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						nums[i]
  lowerCamelCase_  				=						max(UpperCAmelCase_      ,       ans + num      ,       UpperCAmelCase_	)

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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



