



"""simple docstring"""
import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        a						(	UpperCamelCase_						,   unittest.TestCase							):
  UpperCamelCase						:					int      =       LxmertTokenizer
  UpperCamelCase						:					Dict      =       LxmertTokenizerFast
  UpperCamelCase						:					List[str]      =       True
  UpperCamelCase						:					Optional[int]      =       True



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


     '''simple docstring'''




     super().setUp()

     SCREAMING_SNAKE_CASE_:			Optional[Any]        =[
         '[UNK]',
         '[CLS]',
         '[SEP]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     SCREAMING_SNAKE_CASE_:			Dict        =os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]		)
     with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""		) as vocab_writer:
        vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)



  def 						lowerCamelCase__							(    self			:       Tuple							,		lowerCAmelCase			:       List[Any]		)  ->       Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			List[str]        ='UNwant\u00E9d,running'
     SCREAMING_SNAKE_CASE_:			Any        ='unwanted, running'
     return input_text, output_text



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Optional[int]        =self.tokenizer_class(self.vocab_file		)

     SCREAMING_SNAKE_CASE_:			str        =tokenizer.tokenize("""UNwant\u00E9d,running"""		)
     self.assertListEqual(lowercase_							,		["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_		)							,		[7, 4, 5, 10, 8, 9]		)





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


     '''simple docstring'''




     if not self.test_rust_tokenizer:
        return

     SCREAMING_SNAKE_CASE_:			Union[str, Any]        =self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:			Optional[Any]        =self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE_:			List[Any]        ='I was born in 92000, and this is falsé.'

     SCREAMING_SNAKE_CASE_:			List[Any]        =tokenizer.tokenize(lowercase_		)
     SCREAMING_SNAKE_CASE_:			Tuple        =rust_tokenizer.tokenize(lowercase_		)
     self.assertListEqual(lowercase_							,		lowercase_		)

     SCREAMING_SNAKE_CASE_:			Tuple        =tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
     SCREAMING_SNAKE_CASE_:			Any        =rust_tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_		)
     self.assertListEqual(lowercase_							,		lowercase_		)

     SCREAMING_SNAKE_CASE_:			List[Any]        =self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_:			List[Any]        =tokenizer.encode(lowercase_		)
     SCREAMING_SNAKE_CASE_:			str        =rust_tokenizer.encode(lowercase_		)
     self.assertListEqual(lowercase_							,		lowercase_		)







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()


