code: 
  
  
  
  
  
  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[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)
  
       UpperCAmelCase			:			Any	  = parser.parse_known_args()
  
       # overwrite batch size until we have tf_glue.py
       UpperCAmelCase			:			Optional[Any]	  = 16
       UpperCAmelCase			:			int	  = 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[Any]	  = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       UpperCAmelCase			:			Dict	  = AutoTokenizer.from_pretrained(args.model_name_or_path)
  
       # Load dataset
       UpperCAmelCase			:			List[str]	  = load_dataset("""imdb""", split=["""train""", """test"""])
       UpperCAmelCase			:			int	  = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
       UpperCAmelCase			:			Union[str, Any]	  = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
  
       # Preprocess train dataset
       UpperCAmelCase			:			Any	  = 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[str]	  = {
           x: train_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((train_features, train_dataset["""label"""])).batch(
           args.per_device_train_batch_size
       )
  
       # Preprocess test dataset
       UpperCAmelCase			:			Optional[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			:			Any	  = {
           x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
           for x in ['input_ids', 'attention_mask']
       }
       UpperCAmelCase			:			str	  = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
           args.per_device_eval_batch_size
       )
  
       # fine optimizer and loss
       UpperCAmelCase			:			str	  = tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       UpperCAmelCase			:			str	  = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       UpperCAmelCase			:			str	  = [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
       UpperCAmelCase			:			Union[str, Any]	  = time.time()
       UpperCAmelCase			:			Any	  = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       UpperCAmelCase			:			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}""")
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  UpperCAmelCase			:			int	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      str
        _lowercase       :      str
        _lowercase       :      Optional[str]        =    None
        _lowercase       :      Optional[str]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)	,   indent=2							) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        __lowerCAmelCase :
        _lowercase       :      List[int]
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[Union[int, float]]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)							) + "\n"
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          if len(self.get_labels()							) > 1:
            a__			:      Dict            =self.get_labels()[int(example.label							)]
          return example
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          with open(lowerCAmelCase__	,   "r"	,   encoding="utf-8-sig"							) as f:
            return list(csv.reader(lowerCAmelCase__	,   delimiter="\t"	,   quotechar=lowerCAmelCase__							)							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =[] if labels is None else labels
          a__			:      List[Any]            =[] if examples is None else examples
          a__			:      List[str]            =mode
          a__			:      Tuple            =verbose
  
  
  
  
  
  
  
        def __len__(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.examples							)
  
  
  
  
  
  
  
        def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
            return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]							)
          return self.examples[idx]
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =cls(**lowerCAmelCase__							)
          processor.add_examples_from_csv(
              lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
          return processor
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =cls(**lowerCAmelCase__							)
          processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
          return processor
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =self._read_tsv(lowerCAmelCase__							)
          if skip_first_row:
            a__			:      Tuple            =lines[1:]
          a__			:      List[Any]            =[]
          a__			:      int            =[]
          a__			:      str            =[]
          for i, line in enumerate(lowerCAmelCase__							):
            texts.append(line[column_text]							)
            labels.append(line[column_label]							)
            if column_id is not None:
              ids.append(line[column_id]							)
            else:
              a__			:      List[str]            =F'''{split_name}-{i}''' if split_name else str(lowerCAmelCase__							)
              ids.append(lowerCAmelCase__							)
  
          return self.add_examples(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if labels is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(
                F'''Text and labels have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(F'''Text and ids have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is None:
            a__			:      Dict            =[None] * len(lowerCAmelCase__							)
          if labels is None:
            a__			:      str            =[None] * len(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =[]
          a__			:      Optional[Any]            =set()
          for text_or_text_and_label, label, guid in zip(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							):
            if isinstance(lowerCAmelCase__	,   (tuple, list)							) and label is None:
              a__      ,							a__			:      int            =text_or_text_and_label
            else:
              a__			:      List[str]            =text_or_text_and_label
            added_labels.add(lowerCAmelCase__							)
            examples.append(InputExample(guid=lowerCAmelCase__	,   text_a=lowerCAmelCase__	,   text_b=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          # Update examples
          if overwrite_examples:
            a__			:      Dict            =examples
          else:
            self.examples.extend(lowerCAmelCase__							)
  
          # Update labels
          if overwrite_labels:
            a__			:      Any            =list(lowerCAmelCase__							)
          else:
            a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)
  
          return self.examples
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if max_length is None:
            a__			:      int            =tokenizer.max_len
  
          a__			:      int            ={label: i for i, label in enumerate(self.labels							)}
  
          a__			:      Union[str, Any]            =[]
          for ex_index, example in enumerate(self.examples							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Tokenizing example {ex_index}'''							)
  
            a__			:      Dict            =tokenizer.encode(
                example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
            all_input_ids.append(lowerCAmelCase__							)
  
          a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)
  
          a__			:      Dict            =[]
          for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Writing example {ex_index}/{len(self.examples							)}'''							)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            a__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)
  
            # Zero-pad up to the sequence length.
            a__			:      List[str]            =batch_length - len(lowerCAmelCase__							)
            if pad_on_left:
              a__			:      Tuple            =([pad_token] * padding_length) + input_ids
              a__			:      Dict            =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
              a__			:      List[str]            =input_ids + ([pad_token] * padding_length)
              a__			:      Any            =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
  
            if self.mode == "classification":
              a__			:      Dict            =label_map[example.label]
            elif self.mode == "regression":
              a__			:      Any            =float(example.label							)
            else:
              raise ValueError(self.mode							)
  
            if ex_index < 5 and self.verbose:
              logger.info("*** Example ***"							)
              logger.info(F'''guid: {example.guid}'''							)
              logger.info(F'''input_ids: {" ".join([str(lowerCAmelCase__							) for x in input_ids]							)}'''							)
              logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
              logger.info(F'''label: {example.label} (id = {label})'''							)
  
            features.append(InputFeatures(input_ids=lowerCAmelCase__	,   attention_mask=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          if return_tensors is None:
            return features
          elif return_tensors == "tf":
            if not is_tf_available():
              raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
            import tensorflow as tf
  
            def gen():
              for ex in features:
                yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
            a__			:      Any            =tf.data.Dataset.from_generator(
                lowerCAmelCase__	,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,   ({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,   )
            return dataset
          elif return_tensors == "pt":
            if not is_torch_available():
              raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
            import torch
            from torch.utils.data import TensorDataset
  
            a__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
            a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
            if self.mode == "classification":
              a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
            elif self.mode == "regression":
              a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)
  
            a__			:      List[str]            =TensorDataset(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
            return dataset
          else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)
  
  
  
  
  
  
  