'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  __snake_case  : Dict		       =    'platform'

  import jax
  import jax.numpy as jnp

  from transformers import AutoTokenizer
  from transformers.models.mbart.modeling_flax_mbart import (
      FlaxMBartForConditionalGeneration,
      FlaxMBartForQuestionAnswering,
      FlaxMBartForSequenceClassification,
      FlaxMBartModel,
      shift_tokens_right,
  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[int],  _UpperCamelCase       :      Union[str, Any],  _UpperCamelCase       :      Dict=None,  _UpperCamelCase       :      Any=None,  _UpperCamelCase       :      Dict=None,  _UpperCamelCase       :      Union[str, Any]=None,  _UpperCamelCase       :      Dict=None,  _UpperCamelCase       :      str=None,  )      -> Any:
    if attention_mask is None:
        A_      =  np.where(input_ids != config.pad_token_id,  1,  0  )
    if decoder_attention_mask is None:
        A_      =  np.where(decoder_input_ids != config.pad_token_id,  1,  0  )
    if head_mask is None:
        A_      =  np.ones((config.encoder_layers, config.encoder_attention_heads)  )
    if decoder_head_mask is None:
        A_      =  np.ones((config.decoder_layers, config.decoder_attention_heads)  )
    if cross_attn_head_mask is None:
        A_      =  np.ones((config.decoder_layers, config.decoder_attention_heads)  )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=13       ,	_SCREAMING_SNAKE_CASE=7       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE=99       ,	_SCREAMING_SNAKE_CASE=16       ,	_SCREAMING_SNAKE_CASE=2       ,	_SCREAMING_SNAKE_CASE=4       ,	_SCREAMING_SNAKE_CASE=4       ,	_SCREAMING_SNAKE_CASE="gelu"       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=32       ,	_SCREAMING_SNAKE_CASE=2       ,	_SCREAMING_SNAKE_CASE=1       ,	_SCREAMING_SNAKE_CASE=0       ,	_SCREAMING_SNAKE_CASE=2       ,	_SCREAMING_SNAKE_CASE=0.02       ,	)						->	List[Any]:
           A_      =  parent
           A_      =  batch_size
           A_      =  seq_length
           A_      =  is_training
           A_      =  use_labels
           A_      =  vocab_size
           A_      =  hidden_size
           A_      =  num_hidden_layers
           A_      =  num_attention_heads
           A_      =  intermediate_size
           A_      =  hidden_act
           A_      =  hidden_dropout_prob
           A_      =  attention_probs_dropout_prob
           A_      =  max_position_embeddings
           A_      =  eos_token_id
           A_      =  pad_token_id
           A_      =  bos_token_id
           A_      =  decoder_start_token_id
           A_      =  initializer_range

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size					)       ,	3       ,	self.vocab_size					)
           A_      =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,	dtype=np.intaa					))       ,	-1					)

           A_      =  shift_tokens_right(_SCREAMING_SNAKE_CASE       ,	1					)

           A_      =  MBartConfig(
               vocab_size=self.vocab_size       ,	d_model=self.hidden_size       ,	encoder_layers=self.num_hidden_layers       ,	decoder_layers=self.num_hidden_layers       ,	encoder_attention_heads=self.num_attention_heads       ,	decoder_attention_heads=self.num_attention_heads       ,	encoder_ffn_dim=self.intermediate_size       ,	decoder_ffn_dim=self.intermediate_size       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	eos_token_id=self.eos_token_id       ,	bos_token_id=self.bos_token_id       ,	pad_token_id=self.pad_token_id       ,	decoder_start_token_id=self.decoder_start_token_id       ,	initializer_range=self.initializer_range       ,	use_cache=_SCREAMING_SNAKE_CASE       ,	)
           A_      =  prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           return config, inputs_dict

       def    __A			( self					)						->	Tuple:
           A_  ,A_      =  self.prepare_config_and_inputs()
           return config, inputs_dict

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           A_      =  20
           A_      =  model_class_name(_SCREAMING_SNAKE_CASE					)

           A_      =  model.encode(inputs_dict['''input_ids''']					)

           A_  ,A_      =  (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           A_      =  model.init_cache(decoder_input_ids.shape[0]       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,	dtype='''i4'''					)

           A_      =  jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,	)
           A_      =  model.decode(
               decoder_input_ids[:, :-1]       ,	_SCREAMING_SNAKE_CASE       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE       ,	past_key_values=_SCREAMING_SNAKE_CASE       ,	decoder_position_ids=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,	dtype='''i4'''					)
           A_      =  model.decode(
               decoder_input_ids[:, -1:]       ,	_SCREAMING_SNAKE_CASE       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE       ,	past_key_values=outputs_cache.past_key_values       ,	decoder_position_ids=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  model.decode(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           A_      =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1E-3       ,	msg=F'''Max diff is {diff}'''					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  20
           A_      =  model_class_name(_SCREAMING_SNAKE_CASE					)

           A_      =  model.encode(inputs_dict['''input_ids''']					)

           A_  ,A_      =  (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           A_      =  jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
               ]       ,	axis=-1       ,	)

           A_      =  model.init_cache(decoder_input_ids.shape[0]       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,	)

           A_      =  model.decode(
               decoder_input_ids[:, :-1]       ,	_SCREAMING_SNAKE_CASE       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE       ,	past_key_values=_SCREAMING_SNAKE_CASE       ,	decoder_position_ids=_SCREAMING_SNAKE_CASE       ,	)
           A_      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,	dtype='''i4'''					)
           A_      =  model.decode(
               decoder_input_ids[:, -1:]       ,	_SCREAMING_SNAKE_CASE       ,	past_key_values=outputs_cache.past_key_values       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE       ,	decoder_position_ids=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  model.decode(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE					)

           A_      =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
           self.parent.assertTrue(diff < 1E-3       ,	msg=F'''Max diff is {diff}'''					)




@require_flax
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   Any  					=							99

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]       ,	dtype=np.intaa       ,	)

           A_      =  input_ids.shape[0]
           A_      =  MBartConfig(
               vocab_size=self.vocab_size       ,	d_model=24       ,	encoder_layers=2       ,	decoder_layers=2       ,	encoder_attention_heads=2       ,	decoder_attention_heads=2       ,	encoder_ffn_dim=32       ,	decoder_ffn_dim=32       ,	max_position_embeddings=48       ,	eos_token_id=2       ,	pad_token_id=1       ,	bos_token_id=0       ,	)
           return config, input_ids, batch_size

       def    __A			( self					)						->	List[Any]:
           A_  ,A_  ,A_      =  self._get_config_and_data()
           A_      =  FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE					)
           A_      =  model(input_ids=_SCREAMING_SNAKE_CASE       ,	decoder_input_ids=_SCREAMING_SNAKE_CASE					)
           A_      =  (batch_size, config.num_labels)
           self.assertEqual(outputs['''logits'''].shape       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[Any]:
           A_  ,A_  ,A_      =  self._get_config_and_data()
           A_      =  FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE					)
           A_      =  model(input_ids=_SCREAMING_SNAKE_CASE					)

           self.assertEqual(outputs['''start_logits'''].shape       ,	input_ids.shape					)
           self.assertEqual(outputs['''end_logits'''].shape       ,	input_ids.shape					)

       def    __A			( self					)						->	List[Any]:
           A_  ,A_  ,A_      =  self._get_config_and_data()
           A_      =  FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE					)
           A_      =  lm_model(input_ids=_SCREAMING_SNAKE_CASE					)
           A_      =  (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  MBartConfig(
               vocab_size=self.vocab_size       ,	d_model=14       ,	encoder_layers=2       ,	decoder_layers=2       ,	encoder_attention_heads=2       ,	decoder_attention_heads=2       ,	encoder_ffn_dim=8       ,	decoder_ffn_dim=8       ,	max_position_embeddings=48       ,	)
           A_      =  FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE					)
           A_      =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,	dtype=np.intaa					)
           A_      =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,	dtype=np.intaa					)
           A_      =  lm_model(input_ids=_SCREAMING_SNAKE_CASE       ,	decoder_input_ids=_SCREAMING_SNAKE_CASE					)
           A_      =  (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[Any]:
           A_      =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,	dtype=np.intaa					)
           A_      =  shift_tokens_right(_SCREAMING_SNAKE_CASE       ,	1					)
           A_      =  np.equal(_SCREAMING_SNAKE_CASE       ,	1					).astype(np.floataa					).sum()
           A_      =  np.equal(_SCREAMING_SNAKE_CASE       ,	1					).astype(np.floataa					).sum()
           self.assertEqual(shifted.shape       ,	input_ids.shape					)
           self.assertEqual(_SCREAMING_SNAKE_CASE       ,	n_pad_before - 1					)
           self.assertTrue(np.equal(shifted[:, 0]       ,	2					).all()					)




@require_flax
class       __UpperCAmelCase							(							_UpperCamelCase		, unittest.TestCase		, _UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							True
       __lowercase   :   Any  					=							(
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       __lowercase   :   List[str]  					=							(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

       def    __A			( self					)						->	Dict:
           A_      =  FlaxMBartModelTester(self					)

       def    __A			( self					)						->	Tuple:
           A_  ,A_      =  self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Any:
           A_  ,A_      =  self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           A_  ,A_      =  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__					):
                   A_      =  self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  model_class(_SCREAMING_SNAKE_CASE					)

                   @jax.jit
                   def encode_jitted(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					):
                       return model.encode(input_ids=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE					)

                   with self.subTest('''JIT Enabled'''					):
                       A_      =  encode_jitted(**_SCREAMING_SNAKE_CASE					).to_tuple()

                   with self.subTest('''JIT Disabled'''					):
                       with jax.disable_jit():
                           A_      =  encode_jitted(**_SCREAMING_SNAKE_CASE					).to_tuple()

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,	len(_SCREAMING_SNAKE_CASE					)					)
                   for jitted_output, output in zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                       self.assertEqual(jitted_output.shape       ,	output.shape					)

       def    __A			( self					)						->	int:
           A_  ,A_      =  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__					):
                   A_      =  model_class(_SCREAMING_SNAKE_CASE					)
                   A_      =  model.encode(inputs_dict['''input_ids''']       ,	inputs_dict['''attention_mask''']					)

                   A_      =  {
                       '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                       '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                       '''encoder_outputs''': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                       return model.decode(
                           decoder_input_ids=_SCREAMING_SNAKE_CASE       ,	decoder_attention_mask=_SCREAMING_SNAKE_CASE       ,	encoder_outputs=_SCREAMING_SNAKE_CASE       ,	)

                   with self.subTest('''JIT Enabled'''					):
                       A_      =  decode_jitted(**_SCREAMING_SNAKE_CASE					).to_tuple()

                   with self.subTest('''JIT Disabled'''					):
                       with jax.disable_jit():
                           A_      =  decode_jitted(**_SCREAMING_SNAKE_CASE					).to_tuple()

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,	len(_SCREAMING_SNAKE_CASE					)					)
                   for jitted_output, output in zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                       self.assertEqual(jitted_output.shape       ,	output.shape					)

       @slow
       def    __A			( self					)						->	str:
           for model_class_name in self.all_model_classes:
               A_      =  model_class_name.from_pretrained('''facebook/mbart-large-cc25'''       ,	from_pt=_SCREAMING_SNAKE_CASE					)
               # FlaxMBartForSequenceClassification expects eos token in input_ids
               A_      =  np.ones((1, 1)					) * model.config.eos_token_id
               A_      =  model(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)




@require_flax
@require_sentencepiece
@require_tokenizers
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   Any  					=							[
           ' UN Chief Says There Is No Military Solution in Syria',
       ]
       __lowercase   :   Optional[int]  					=							[
           'Şeful ONU declară că nu există o soluţie militară în Siria',
       ]
       __lowercase   :   Union[str, Any]  					=							'facebook/mbart-large-en-ro'

       @cached_property
       def    __A			( self					)						->	Dict:
           return AutoTokenizer.from_pretrained(self.model_name					)

       @cached_property
       def    __A			( self					)						->	Any:
           A_      =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       ,	from_pt=_SCREAMING_SNAKE_CASE					)
           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  self.translate_src_text(**_SCREAMING_SNAKE_CASE					)
           self.assertListEqual(self.expected_text       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Union[str, Any]:
           A_      =  self.tokenizer(self.src_text       ,	**_SCREAMING_SNAKE_CASE       ,	return_tensors='''np'''					)
           A_      =  self.model.generate(
               model_inputs.input_ids       ,	attention_mask=model_inputs.attention_mask       ,	decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']       ,	early_stopping=_SCREAMING_SNAKE_CASE       ,	num_beams=2       ,	).sequences
           A_      =  self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE       ,	skip_special_tokens=_SCREAMING_SNAKE_CASE					)
           return generated_words

       @slow
       def    __A			( self					)						->	Dict:
           self._assert_generated_batch_equal_expected()







'''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__":
  __snake_case  : 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  : Dict		       =    parser.parse_known_args()

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

  # Set up logging
  __snake_case  : Union[str, 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
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

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

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

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

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






