



"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   _UpperCAmelCase  =      """pt"""
elif is_tf_available():
   _UpperCAmelCase  =      """tf"""
else:
   _UpperCAmelCase  =      """jax"""
class        a						(	UpperCamelCase_						,   unittest.TestCase							):
  UpperCamelCase						:					Optional[Any]      =       ByTaTokenizer
  UpperCamelCase						:					Tuple      =       False



  def 						lowerCamelCase__							(    self			:       List[str]		)  ->       Dict:


     '''simple docstring'''




     super().setUp()
     SCREAMING_SNAKE_CASE_:			int        =ByTaTokenizer()
     tokenizer.save_pretrained(self.tmpdirname		)



  @cached_property
  def 						lowerCamelCase__							(    self			:       Tuple		)  ->       Optional[int]:


     '''simple docstring'''




     return ByTaTokenizer.from_pretrained("""google/byt5-small"""		)



  def 						lowerCamelCase__							(    self			:       str							,		**lowerCAmelCase			:       Dict		)  ->       ByTaTokenizer:


     '''simple docstring'''




     return self.tokenizer_class.from_pretrained(self.tmpdirname							,		**lowercase_		)



  def 						lowerCamelCase__							(    self			:       str							,		lowerCAmelCase			:       List[str]							,		lowerCAmelCase			:       Any=False							,		lowerCAmelCase			:       List[str]=20							,		lowerCAmelCase			:       Union[str, Any]=5		)  ->       Tuple[str, list]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Union[str, Any]        =[]
     for i in range(len(lowercase_		)		):
        try:
           SCREAMING_SNAKE_CASE_:			Optional[Any]        =tokenizer.decode([i]							,		clean_up_tokenization_spaces=lowercase_		)
        except UnicodeDecodeError:
           pass
        toks.append((i, tok)		)

     SCREAMING_SNAKE_CASE_:			int        =list(filter(lambda lowerCAmelCase		: re.match(R"""^[ a-zA-Z]+$"""							,		t[1]		)							,		lowercase_		)		)
     SCREAMING_SNAKE_CASE_:			List[str]        =list(filter(lambda lowerCAmelCase		: [t[0]] == tokenizer.encode(t[1]							,		add_special_tokens=lowercase_		)							,		lowercase_		)		)
     if max_length is not None and len(lowercase_		) > max_length:
        SCREAMING_SNAKE_CASE_:			List[str]        =toks[:max_length]
     if min_length is not None and len(lowercase_		) < min_length and len(lowercase_		) > 0:
        while len(lowercase_		) < min_length:
           SCREAMING_SNAKE_CASE_:			Optional[int]        =toks + toks
        # toks_str = [t[1] for t in toks]
     SCREAMING_SNAKE_CASE_:			List[Any]        =[t[0] for t in toks]

     # Ensure consistency
     SCREAMING_SNAKE_CASE_:			Optional[int]        =tokenizer.decode(lowercase_							,		clean_up_tokenization_spaces=lowercase_		)
     if " " not in output_txt and len(lowercase_		) > 1:
        SCREAMING_SNAKE_CASE_:			Optional[Any]        =(
            tokenizer.decode([toks_ids[0]]							,		clean_up_tokenization_spaces=lowercase_		)
            + ' '
            + tokenizer.decode(toks_ids[1:]							,		clean_up_tokenization_spaces=lowercase_		)
        )
     if with_prefix_space:
        SCREAMING_SNAKE_CASE_:			Dict        =' ' + output_txt
     SCREAMING_SNAKE_CASE_:			Optional[Any]        =tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
     return output_txt, output_ids



  def 						lowerCamelCase__							(    self			:       int		)  ->       Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Optional[Any]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			List[Any]        =tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]		)
     SCREAMING_SNAKE_CASE_:			str        =tokenizer(["""hi""", """I went to the gym""", """"""]		)
     self.assertListEqual(batch_with_eos_added["""input_ids"""]							,		batch_without_eos_added["""input_ids"""]		)



  def 						lowerCamelCase__							(    self			:       Tuple		)  ->       Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Optional[int]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			Any        ='Unicode €.'
     SCREAMING_SNAKE_CASE_:			Optional[int]        =tokenizer(lowercase_		)
     SCREAMING_SNAKE_CASE_:			str        =[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
     self.assertEqual(encoded["""input_ids"""]							,		lowercase_		)

     # decoding
     SCREAMING_SNAKE_CASE_:			List[Any]        =tokenizer.decode(lowercase_		)
     self.assertEqual(lowercase_							,		"""Unicode €.</s>"""		)

     SCREAMING_SNAKE_CASE_:			Any        =tokenizer("""e è é ê ë"""		)
     SCREAMING_SNAKE_CASE_:			int        =[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
     self.assertEqual(encoded["""input_ids"""]							,		lowercase_		)
     # decoding
     SCREAMING_SNAKE_CASE_:			Optional[int]        =tokenizer.decode(lowercase_		)
     self.assertEqual(lowercase_							,		"""e è é ê ë</s>"""		)

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		)							,		"""e è é ê ë</s>"""		)



  def 						lowerCamelCase__							(    self			:       Optional[int]		)  ->       str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[Any]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			Union[str, Any]        =['A long paragraph for summarization.', 'Another paragraph for summarization.']
     # fmt: off
     SCREAMING_SNAKE_CASE_:			Optional[Any]        =[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
     # fmt: on
     SCREAMING_SNAKE_CASE_:			Any        =tokenizer(lowercase_							,		padding=lowercase_							,		return_tensors=lowercase_		)
     self.assertIsInstance(lowercase_							,		lowercase_		)

     if FRAMEWORK != "jax":
        SCREAMING_SNAKE_CASE_:			List[Any]        =list(batch.input_ids.numpy()[0]		)
     else:
        SCREAMING_SNAKE_CASE_:			Optional[int]        =list(batch.input_ids.tolist()[0]		)

     self.assertListEqual(lowercase_							,		lowercase_		)

     self.assertEqual((2, 37)							,		batch.input_ids.shape		)
     self.assertEqual((2, 37)							,		batch.attention_mask.shape		)



  def 						lowerCamelCase__							(    self			:       str		)  ->       str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[str]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			Optional[Any]        =['A long paragraph for summarization.', 'Another paragraph for summarization.']
     SCREAMING_SNAKE_CASE_:			Union[str, Any]        =tokenizer(lowercase_							,		padding=lowercase_							,		return_tensors=lowercase_		)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn("""input_ids"""							,		lowercase_		)
     self.assertIn("""attention_mask"""							,		lowercase_		)
     self.assertNotIn("""decoder_input_ids"""							,		lowercase_		)
     self.assertNotIn("""decoder_attention_mask"""							,		lowercase_		)



  def 						lowerCamelCase__							(    self			:       str		)  ->       Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[str]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			int        =[
         'Summary of the text.',
         'Another summary.',
     ]
     SCREAMING_SNAKE_CASE_:			Any        =tokenizer(
         text_target=lowercase_							,		max_length=32							,		padding="""max_length"""							,		truncation=lowercase_							,		return_tensors=lowercase_		)
     self.assertEqual(32							,		targets["""input_ids"""].shape[1]		)



  def 						lowerCamelCase__							(    self			:       Any		)  ->       List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[str]        =self.ta_base_tokenizer
     SCREAMING_SNAKE_CASE_:			str        =['A long paragraph for summarization. </s>']
     SCREAMING_SNAKE_CASE_:			Tuple        =['Summary of the text. </s>']
     # fmt: off
     SCREAMING_SNAKE_CASE_:			Any        =[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
     SCREAMING_SNAKE_CASE_:			Optional[int]        =[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
     # fmt: on

     SCREAMING_SNAKE_CASE_:			int        =tokenizer(lowercase_							,		text_target=lowercase_		)

     self.assertEqual(lowercase_							,		batch["""input_ids"""][0]		)
     self.assertEqual(lowercase_							,		batch["""labels"""][0]		)



  def 						lowerCamelCase__							(    self			:       Tuple		)  ->       Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Optional[int]        =self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           self.assertNotEqual(tokenizer.model_max_length							,		42		)

        # Now let's start the test
     SCREAMING_SNAKE_CASE_:			Tuple        =self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           # Isolate this from the other tests because we save additional tokens/etc
           SCREAMING_SNAKE_CASE_:			Optional[Any]        =tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE_:			Dict        =' He is very happy, UNwant\u00E9d,running'
           SCREAMING_SNAKE_CASE_:			Any        =tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
           tokenizer.save_pretrained(lowercase_		)

           SCREAMING_SNAKE_CASE_:			List[str]        =tokenizer.__class__.from_pretrained(lowercase_		)
           SCREAMING_SNAKE_CASE_:			Tuple        =after_tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
           self.assertListEqual(lowercase_							,		lowercase_		)

           shutil.rmtree(lowercase_		)

     SCREAMING_SNAKE_CASE_:			Optional[Any]        =self.get_tokenizers(model_max_length=42		)
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           # Isolate this from the other tests because we save additional tokens/etc
           SCREAMING_SNAKE_CASE_:			Optional[int]        =tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE_:			Dict        =' He is very happy, UNwant\u00E9d,running'
           tokenizer.add_tokens(["""bim""", """bambam"""]		)
           SCREAMING_SNAKE_CASE_:			int        =tokenizer.additional_special_tokens
           additional_special_tokens.append("""new_additional_special_token"""		)
           tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
           SCREAMING_SNAKE_CASE_:			Tuple        =tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
           tokenizer.save_pretrained(lowercase_		)

           SCREAMING_SNAKE_CASE_:			int        =tokenizer.__class__.from_pretrained(lowercase_		)
           SCREAMING_SNAKE_CASE_:			Optional[Any]        =after_tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
           self.assertListEqual(lowercase_							,		lowercase_		)
           self.assertIn("""new_additional_special_token"""							,		after_tokenizer.additional_special_tokens		)
           self.assertEqual(after_tokenizer.model_max_length							,		42		)

           SCREAMING_SNAKE_CASE_:			Union[str, Any]        =tokenizer.__class__.from_pretrained(lowercase_							,		model_max_length=43		)
           self.assertEqual(tokenizer.model_max_length							,		43		)

           shutil.rmtree(lowercase_		)



  def 						lowerCamelCase__							(    self			:       Optional[int]		)  ->       Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			str        =[]
     if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

     if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(lowercase_		)

           with open(os.path.join(lowercase_							,		"""special_tokens_map.json"""		)							,		encoding="""utf-8"""		) as json_file:
              SCREAMING_SNAKE_CASE_:			Dict        =json.load(lowercase_		)

           with open(os.path.join(lowercase_							,		"""tokenizer_config.json"""		)							,		encoding="""utf-8"""		) as json_file:
              SCREAMING_SNAKE_CASE_:			int        =json.load(lowercase_		)

           SCREAMING_SNAKE_CASE_:			List[str]        =[f'''<extra_id_{i}>''' for i in range(125		)]

           SCREAMING_SNAKE_CASE_:			List[str]        =added_tokens_extra_ids + [
               'an_additional_special_token'
           ]
           SCREAMING_SNAKE_CASE_:			Any        =added_tokens_extra_ids + [
               'an_additional_special_token'
           ]

           with open(os.path.join(lowercase_							,		"""special_tokens_map.json"""		)							,		"""w"""							,		encoding="""utf-8"""		) as outfile:
              json.dump(lowercase_							,		lowercase_		)
           with open(os.path.join(lowercase_							,		"""tokenizer_config.json"""		)							,		"""w"""							,		encoding="""utf-8"""		) as outfile:
              json.dump(lowercase_							,		lowercase_		)

           # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
           # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
           # "special_tokens_map.json" files
           SCREAMING_SNAKE_CASE_:			str        =tokenizer_class.from_pretrained(
               lowercase_							,		)
           self.assertIn(
               """an_additional_special_token"""							,		tokenizer_without_change_in_init.additional_special_tokens		)
           # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
           self.assertEqual(
               ["""an_additional_special_token"""]							,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                   tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]		)		)							,		)

           # Now we test that we can change the value of additional_special_tokens in the from_pretrained
           SCREAMING_SNAKE_CASE_:			Tuple        =added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""							,		lstrip=lowercase_		)]
           SCREAMING_SNAKE_CASE_:			Union[str, Any]        =tokenizer_class.from_pretrained(
               lowercase_							,		additional_special_tokens=lowercase_							,		)

           self.assertIn("""a_new_additional_special_token"""							,		tokenizer.additional_special_tokens		)
           self.assertEqual(
               ["""a_new_additional_special_token"""]							,		tokenizer.convert_ids_to_tokens(
                   tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]		)		)							,		)



  def 						lowerCamelCase__							(    self			:       int		)  ->       Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Optional[Any]        =[]
     if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

     if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(lowercase_		)

           SCREAMING_SNAKE_CASE_:			Union[str, Any]        =tokenizer_class.from_pretrained(lowercase_		)

           self.assertTrue(tokenizer.decode([255]		) == """"""		)



  def 						lowerCamelCase__							(    self			:       Optional[int]		)  ->       List[Any]:


     '''simple docstring'''




     pass



  def 						lowerCamelCase__							(    self			:       Union[str, Any]		)  ->       Union[str, Any]:


     '''simple docstring'''




     pass



  def 						lowerCamelCase__							(    self			:       str		)  ->       Optional[int]:


     '''simple docstring'''




     pass



  def 						lowerCamelCase__							(    self			:       Any		)  ->       Tuple:


     '''simple docstring'''




     pass



  def 						lowerCamelCase__							(    self			:       str		)  ->       Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Any        =self.get_tokenizers(fast=lowercase_							,		do_lower_case=lowercase_		)
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           SCREAMING_SNAKE_CASE_:			Optional[Any]        =['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
           SCREAMING_SNAKE_CASE_:			Dict        =tokenizer.convert_tokens_to_string(lowercase_		)

           self.assertIsInstance(lowercase_							,		lowercase_		)





  def 						lowerCamelCase__							(    self			:       int		)  ->       int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[Any]        =self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           SCREAMING_SNAKE_CASE_:			Union[str, Any]        =[
               'bos_token',
               'eos_token',
               'unk_token',
               'sep_token',
               'pad_token',
               'cls_token',
               'mask_token',
           ]

           SCREAMING_SNAKE_CASE_:			List[Any]        =0
           SCREAMING_SNAKE_CASE_:			int        =tokenizer.convert_ids_to_tokens(
               lowercase_							,		skip_special_tokens=lowercase_		)

           for attr in attributes_list:
              setattr(lowercase_							,		attr + """_id"""							,		lowercase_		)
              self.assertEqual(getattr(lowercase_							,		lowercase_		)							,		lowercase_		)
              self.assertEqual(getattr(lowercase_							,		attr + """_id"""		)							,		lowercase_		)

              setattr(lowercase_							,		attr + """_id"""							,		lowercase_		)
              self.assertEqual(getattr(lowercase_							,		lowercase_		)							,		lowercase_		)
              self.assertEqual(getattr(lowercase_							,		attr + """_id"""		)							,		lowercase_		)

           setattr(lowercase_							,		"""additional_special_tokens_ids"""							,		[]		)
           self.assertListEqual(getattr(lowercase_							,		"""additional_special_tokens"""		)							,		[]		)
           self.assertListEqual(getattr(lowercase_							,		"""additional_special_tokens_ids"""		)							,		[]		)

           setattr(lowercase_							,		"""additional_special_tokens_ids"""							,		[token_id_to_test_setters]		)
           self.assertListEqual(getattr(lowercase_							,		"""additional_special_tokens"""		)							,		[token_to_test_setters]		)
           self.assertListEqual(getattr(lowercase_							,		"""additional_special_tokens_ids"""		)							,		[token_id_to_test_setters]		)







from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets




class A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

                    assert all(len(lowercase_					) == 512 for x in inputs.input_ids					)
                    assert all(len(lowercase_					) == 128 for x in outputs.input_ids					)

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

             # map train dataset
             _lowerCamelCase			:						Tuple            =train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             train_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             # same for validation dataset
             _lowerCamelCase			:						Tuple            =val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             val_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


