


from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase     :					List[str]       =logging.get_logger(__name__)







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""input_ids""", """attention_mask"""]




						def __init__(			self				,  __lowercase="[PAD]"				,  __lowercase="[BOS]"				,  __lowercase="[EOS]"				,  __lowercase="[MASK]"				,  __lowercase="[CLS]"				,  __lowercase="[SEP]"				,  __lowercase=2_0_4_8				,  **__lowercase				,  )   ->      None:
										lowerCAmelCase_	: Optional[int]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else pad_token
										lowerCAmelCase_	: str      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else bos_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else eos_token
										lowerCAmelCase_	: List[str]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else mask_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else cls_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else sep_token

										super().__init__(
										    pad_token=__lowercase				,  bos_token=__lowercase				,  eos_token=__lowercase				,  mask_token=__lowercase				,  cls_token=__lowercase				,  sep_token=__lowercase				,  model_max_length=__lowercase				,  **__lowercase				,  )

										lowerCAmelCase_	: Dict      			=			2**8  # utf is 8 bits

										# define special tokens dict
										lowerCAmelCase_	: Dict[str, int]      			=			{
										    self.pad_token: 0,
										    self.bos_token: 1,
										    self.eos_token: 2,
										    self.mask_token: 3,
										    self.cls_token: 4,
										    self.sep_token: 5,
										}
										lowerCAmelCase_	: List[Any]      			=			len(self.special_tokens_encoder							)
										lowerCAmelCase_	: Dict[int, str]      			=			{v: k for k, v in self.special_tokens_encoder.items()}




						def   lowercase_	(			self							)   ->      Dict[str, int]:
										lowerCAmelCase_	: int      			=			self.special_tokens_encoder.copy()
										vocab.update(self.added_tokens_encoder							)
										for i in range(self._utf_vocab_size							):
														lowerCAmelCase_	: int      			=			chr(__lowercase							)
														lowerCAmelCase_	: Optional[Any]      			=			i + len(self.special_tokens_encoder							)
										return vocab




						@property
						def   lowercase_	(			self							)   ->      str:
										return self._utf_vocab_size + self._num_special_tokens




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None				,  __lowercase = False							)   ->      List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__lowercase				,  token_ids_a=__lowercase				,  already_has_special_tokens=__lowercase							)

										# normal case: some special tokens
										if token_ids_a is None:
														return [1] + [0] * len(__lowercase							) + [1]
										return [1] + ([0] * len(__lowercase							)) + [1] + ([0] * len(__lowercase							)) + [1]




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      List[int]:
										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Optional[int]      			=			[chr(__lowercase							) for i in text.encode('''utf-8'''							)]
										return tokens




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										if token in self.special_tokens_encoder:
														lowerCAmelCase_	: Optional[int]      			=			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
														lowerCAmelCase_	: Any      			=			self.added_tokens_encoder[token]
										elif len(__lowercase							) != 1:
														lowerCAmelCase_	: Any      			=			self.unk_token_id
										else:
														lowerCAmelCase_	: List[Any]      			=			ord(__lowercase							) + self._num_special_tokens
										return token_id




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										if index in self.special_tokens_decoder:
														lowerCAmelCase_	: List[Any]      			=			self.special_tokens_decoder[index]
										elif index in self.added_tokens_decoder:
														lowerCAmelCase_	: List[str]      			=			self.added_tokens_decoder[index]
										else:
														lowerCAmelCase_	: str      			=			chr(index - self._num_special_tokens							)
										return token




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: Tuple      			=			B''''''
										for token in tokens:
														if token in self.special_tokens_decoder:
																		lowerCAmelCase_	: int      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.added_tokens_decoder:
																		lowerCAmelCase_	: Tuple      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.special_tokens_encoder:
																		lowerCAmelCase_	: Any      			=			token.encode('''utf-8'''							)
														elif token in self.added_tokens_encoder:
																		lowerCAmelCase_	: Tuple      			=			token.encode('''utf-8'''							)
														else:
																		lowerCAmelCase_	: str      			=			bytes([ord(__lowercase							)]							)
														bstring += tok_string
										lowerCAmelCase_	: str      			=			bstring.decode('''utf-8'''				,  errors='''replace'''							)
										return string




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      Tuple[str]:
										return ()



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__":
						_UpperCAmelCase     :					Optional[int]       =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)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =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
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =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"""])

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

						# Preprocess test dataset
						_UpperCAmelCase     :					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"""])

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

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

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =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}""")