




'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


A					       =datasets.utils.logging.get_logger(__name__)


@dataclass
class      _a    (      datasets.BuilderConfig					):
       __a							:	Optional[datasets.Features]  							=   None






class      _a    (      datasets.ArrowBasedBuilder					):
       __a							:	Union[str, Any]  							=   ArrowConfig
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           return datasets.DatasetInfo(features=self.config.features							)
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           if not self.config.data_files:
               raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"							)
           UpperCAmelCase     			=						dl_manager.download_and_extract(self.config.data_files							)
           if isinstance(lowercase  ,							(str, list, tuple)							):
               UpperCAmelCase     			=						data_files
               if isinstance(lowercase  ,							lowercase							):
                   UpperCAmelCase     			=						[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               UpperCAmelCase     			=						[dl_manager.iter_files(lowercase							) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,							gen_kwargs={'''files''': files}							)]
           UpperCAmelCase     			=						[]
           for split_name, files in data_files.items():
               if isinstance(lowercase  ,							lowercase							):
                   UpperCAmelCase     			=						[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               UpperCAmelCase     			=						[dl_manager.iter_files(lowercase							) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                   for file in itertools.chain.from_iterable(lowercase							):
                       with open(lowercase  ,							'''rb'''							) as f:
                           UpperCAmelCase     			=						datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowercase							).schema							)
                       break
               splits.append(datasets.SplitGenerator(name=lowercase  ,							gen_kwargs={'''files''': files}							)							)
           return splits
       def        A (      self		:      Tuple  ,							lowercase		:      pa.Table							):



           '''simple docstring'''

           if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               UpperCAmelCase     			=						table_cast(lowercase  ,							self.info.features.arrow_schema							)
           return pa_table







       def        A (      self		:      Dict  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase							)							):
               with open(lowercase  ,							'''rb'''							) as f:
                   try:
                       for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowercase							)							):
                           UpperCAmelCase     			=						pa.Table.from_batches([record_batch]							)
                           # Uncomment for debugging (will print the Arrow table size and elements)
                           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                           yield f"{file_idx}_{batch_idx}", self._cast_table(lowercase							)
                   except ValueError as e:
                       logger.error(f"Failed to read file '{file}' with error {type(lowercase							)}: {e}"							)
                       raise










'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   A					       =argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument('--epochs', type=int, default=1)
   parser.add_argument('--per_device_train_batch_size', type=int, default=16)
   parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
   parser.add_argument('--model_name_or_path', type=str)
   parser.add_argument('--learning_rate', type=str, default=5E-5)
   parser.add_argument('--do_train', type=bool, default=True)
   parser.add_argument('--do_eval', type=bool, default=True)
   parser.add_argument('--output_dir', type=str)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # Set up logging
   A					       =logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName('INFO'),
       handlers=[logging.StreamHandler(sys.stdout)],
       format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
   )

   # Load model and tokenizer
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A					       =train_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   A					       =test_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
       args.per_device_eval_batch_size
   )

   # fine optimizer and loss
   A					       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   A					       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   A					       =[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =time.time() - start_train_time

   logger.info('*** Train ***')
   logger.info(f"""train_runtime = {end_train_time}""")
   for key, value in train_results.history.items():
      logger.info(f"""  {key} = {value}""")




