






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__":
					snake_case_   :	List[Any]							      =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)

					snake_case_      ,	snake_case_   :	Tuple							      =parser.parse_known_args()

					# overwrite batch size until we have tf_glue.py
					snake_case_   :	Optional[int]							      =16
					snake_case_   :	Any							      =16

					# Set up logging
					snake_case_   :	int							      =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
					snake_case_   :	Dict							      =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					snake_case_   :	Dict							      =AutoTokenizer.from_pretrained(args.model_name_or_path)

					# Load dataset
					snake_case_      ,	snake_case_   :	str							      =load_dataset('''imdb''', split=['''train''', '''test'''])
					snake_case_   :	Union[str, Any]							      =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
					snake_case_   :	List[Any]							      =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

					# Preprocess train dataset
					snake_case_   :	Optional[int]							      =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'''])

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

					# Preprocess test dataset
					snake_case_   :	List[Any]							      =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'''])

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

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

					snake_case_   :	List[Any]							      =time.time()
					snake_case_   :	Union[str, Any]							      =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					snake_case_   :	List[str]							      =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}""")









'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class        a__     (      TensorFormatter[Mapping, np.ndarray, Mapping]    ):
     '''simple docstring'''







     def __init__(self     :						Tuple					,					lowercase__     :						Dict=None					,					**lowercase__     :						List[Any]       )	->							str:
           super().__init__(features=lowercase__       )
           __A		 =   np_array_kwargs






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						Tuple       )	->							List[Any]:
           if isinstance(lowercase__					,					lowercase__       ):
                 if column and all(
                     isinstance(lowercase__					,					np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
                       return np.stack(lowercase__       )
                 else:
                       # don't use np.array(column, dtype=object)
                       # since it fails in certain cases
                       # see https://stackoverflow.com/q/51005699
                       __A		 =   np.empty(len(lowercase__       )					,					dtype=lowercase__       )
                       __A		 =   column
                       return out
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						Optional[int]       )	->							Tuple:
           if isinstance(lowercase__					,					(str, bytes, type(lowercase__       ))       ):
                 return value
           elif isinstance(lowercase__					,					(np.character, np.ndarray)       ) and np.issubdtype(value.dtype					,					np.character       ):
                 return value
           elif isinstance(lowercase__					,					np.number       ):
                 return value

           __A		 =   {}

           if isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.integer       ):
                 __A		 =   {"dtype": np.intaa}
           elif isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.floating       ):
                 __A		 =   {"dtype": np.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(lowercase__					,					PIL.Image.Image       ):
                       return np.asarray(lowercase__					,					**self.np_array_kwargs       )

           return np.asarray(lowercase__					,					**{**default_dtype, **self.np_array_kwargs}       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						str       )	->							Optional[Any]:
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(lowercase__					,					torch.Tensor       ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
           if hasattr(lowercase__					,					"__array__"       ) and not isinstance(lowercase__					,					(np.ndarray, np.character, np.number)       ):
                 __A		 =   data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(lowercase__					,					np.ndarray       ):
                 if data_struct.dtype == object:
                       return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           if isinstance(lowercase__					,					(list, tuple)       ):
                 return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           return self._tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						dict       )	->							Dict:
           return map_nested(self._recursive_tensorize					,					lowercase__					,					map_list=lowercase__       )






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_row(lowercase__       )
           __A		 =   self.python_features_decoder.decode_row(lowercase__       )
           return self.recursive_tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						pa.Table       )	->							np.ndarray:
           __A		 =   self.numpy_arrow_extractor().extract_column(lowercase__       )
           __A		 =   self.python_features_decoder.decode_column(lowercase__					,					pa_table.column_names[0]       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           __A		 =   self._consolidate(lowercase__       )
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_batch(lowercase__       )
           __A		 =   self.python_features_decoder.decode_batch(lowercase__       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           for column_name in batch:
                 __A		 =   self._consolidate(batch[column_name]       )
           return batch


