






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=99   ,						_SCREAMING_SNAKE_CASE      :    List[str]=13   ,						_SCREAMING_SNAKE_CASE      :    Any=7   ,						_SCREAMING_SNAKE_CASE      :    List[str]=9   ,						_SCREAMING_SNAKE_CASE      :    List[str]=True   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=False   ,						_SCREAMING_SNAKE_CASE      :    Any=32   ,						_SCREAMING_SNAKE_CASE      :    List[str]=5   ,						_SCREAMING_SNAKE_CASE      :    Tuple=4   ,						_SCREAMING_SNAKE_CASE      :    Dict=37   ,						_SCREAMING_SNAKE_CASE      :    Any=8   ,						_SCREAMING_SNAKE_CASE      :    List[str]=0.1   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=0.0_0_2   ,						_SCREAMING_SNAKE_CASE      :    int=1   ,						_SCREAMING_SNAKE_CASE      :    str=0   ,						_SCREAMING_SNAKE_CASE      :    int=0   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						):






           """simple docstring"""




           UpperCamelCase					       =  parent
           UpperCamelCase					       =  batch_size
           UpperCamelCase					       =  encoder_seq_length
           UpperCamelCase					       =  decoder_seq_length
           # For common tests
           UpperCamelCase					       =  self.decoder_seq_length
           UpperCamelCase					       =  is_training
           UpperCamelCase					       =  use_attention_mask
           UpperCamelCase					       =  use_labels
           UpperCamelCase					       =  vocab_size
           UpperCamelCase					       =  hidden_size
           UpperCamelCase					       =  num_hidden_layers
           UpperCamelCase					       =  num_attention_heads
           UpperCamelCase					       =  d_ff
           UpperCamelCase					       =  relative_attention_num_buckets
           UpperCamelCase					       =  dropout_rate
           UpperCamelCase					       =  initializer_factor
           UpperCamelCase					       =  eos_token_id
           UpperCamelCase					       =  pad_token_id
           UpperCamelCase					       =  decoder_start_token_id
           UpperCamelCase					       =  None
           UpperCamelCase					       =  decoder_layers







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           return TaConfig.from_pretrained('google/umt5-base' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Any=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						_SCREAMING_SNAKE_CASE      :    int=None   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]=None   ,						):






           """simple docstring"""




           if attention_mask is None:
                 UpperCamelCase					       =  input_ids.ne(config.pad_token_id )
           if decoder_attention_mask is None:
                 UpperCamelCase					       =  decoder_input_ids.ne(config.pad_token_id )
           if head_mask is None:
                 UpperCamelCase					       =  torch.ones(config.num_hidden_layers   ,						config.num_attention_heads   ,						device=_SCREAMING_SNAKE_CASE )
           if decoder_head_mask is None:
                 UpperCamelCase					       =  torch.ones(config.num_decoder_layers   ,						config.num_attention_heads   ,						device=_SCREAMING_SNAKE_CASE )
           if cross_attn_head_mask is None:
                 UpperCamelCase					       =  torch.ones(
                     config.num_decoder_layers   ,						config.num_attention_heads   ,						device=_SCREAMING_SNAKE_CASE )
           return {
               "input_ids": input_ids,
               "decoder_input_ids": decoder_input_ids,
               "attention_mask": attention_mask,
               "decoder_attention_mask": decoder_attention_mask,
               "head_mask": head_mask,
               "decoder_head_mask": decoder_head_mask,
               "cross_attn_head_mask": cross_attn_head_mask,
           }







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  ids_tensor([self.batch_size, self.encoder_seq_length]   ,						self.vocab_size )
           UpperCamelCase					       =  ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

           # we need to clamp the input ids here to avoid having pad token in between
           # this is because for NllbMoe the position_ids are prepared such that
           # all pad tokens have pos id = 2 and rest are between 2..seq_length
           # and the seq_length here is seq_length - num_pad_tokens
           # but when using past, there is no way of knowing if the past input ids had
           # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
           # position_ids being off by num_pad_tokens in past input
           UpperCamelCase					       =  input_ids.clamp(self.pad_token_id + 1 )
           UpperCamelCase					       =  decoder_input_ids.clamp(self.pad_token_id + 1 )

           UpperCamelCase					       =  self.get_config()
           UpperCamelCase					       =  config.num_attention_heads
           UpperCamelCase					       =  self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           return config, input_dict







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  self.prepare_config_and_inputs()
           return config, inputs_dict







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           return TaConfig(
               vocab_size=166   ,						d_model=self.hidden_size   ,						d_ff=self.d_ff   ,						d_kv=self.hidden_size // self.num_attention_heads   ,						num_layers=self.num_hidden_layers   ,						num_decoder_layers=self.decoder_layers   ,						num_heads=self.num_attention_heads   ,						relative_attention_num_buckets=self.relative_attention_num_buckets   ,						dropout_rate=self.dropout_rate   ,						initializer_factor=self.initializer_factor   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.pad_token_id   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           return TaConfig(
               vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						d_ff=self.d_ff   ,						d_kv=self.hidden_size // self.num_attention_heads   ,						num_layers=self.num_hidden_layers   ,						num_decoder_layers=self.decoder_layers   ,						num_heads=self.num_attention_heads   ,						relative_attention_num_buckets=self.relative_attention_num_buckets   ,						dropout_rate=self.dropout_rate   ,						initializer_factor=self.initializer_factor   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.pad_token_id   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  UMTaModel(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           UpperCamelCase					       =  model(
               input_ids=_SCREAMING_SNAKE_CASE   ,						decoder_input_ids=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE   ,						)
           UpperCamelCase					       =  model(input_ids=_SCREAMING_SNAKE_CASE   ,						decoder_input_ids=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  result.last_hidden_state
           UpperCamelCase					       =  result.past_key_values
           UpperCamelCase					       =  result.encoder_last_hidden_state

           self.parent.assertEqual(encoder_output.size()   ,						(self.batch_size, self.encoder_seq_length, self.hidden_size) )
           self.parent.assertEqual(decoder_output.size()   ,						(self.batch_size, self.decoder_seq_length, self.hidden_size) )
           # There should be `num_layers` key value embeddings stored in decoder_past
           self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						config.num_layers )
           # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
           self.parent.assertEqual(len(decoder_past[0] )   ,						4 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						):






           """simple docstring"""




           UpperCamelCase					       =  UMTaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
           # first forward pass
           UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE   ,						use_cache=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE   ,						use_cache=_SCREAMING_SNAKE_CASE )

           self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) )
           self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) + 1 )

           UpperCamelCase       ,  UpperCamelCase					       =  outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           UpperCamelCase					       =  ids_tensor((self.batch_size, 1)   ,						config.vocab_size )

           # append to next input_ids and
           UpperCamelCase					       =  torch.cat([input_ids, next_tokens]   ,						dim=-1 )

           UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE )['last_hidden_state']
           UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE   ,						past_key_values=_SCREAMING_SNAKE_CASE )['last_hidden_state']

           # select random slice
           UpperCamelCase					       =  ids_tensor((1,)   ,						output_from_past.shape[-1] ).item()
           UpperCamelCase					       =  output_from_no_past[:, -1, random_slice_idx].detach()
           UpperCamelCase					       =  output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-3 ) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  UMTaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).half().eval()
           UpperCamelCase					       =  model(**_SCREAMING_SNAKE_CASE )['last_hidden_state']
           self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE ).any().item() )





@require_torch
class 					A__     (     __snake_case   ,    __snake_case   ,    __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     snake_case__					=					(UMTaForConditionalGeneration,) if is_torch_available() else ()
     snake_case__					=					(
         {
             """conversational""": UMTaForConditionalGeneration,
             """feature-extraction""": UMTaModel,
             """summarization""": UMTaForConditionalGeneration,
             """text2text-generation""": UMTaForConditionalGeneration,
             """translation""": UMTaForConditionalGeneration,
             """question-answering""": UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     snake_case__					=					True
     snake_case__					=					False
     snake_case__					=					False
     snake_case__					=					True
     snake_case__					=					True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     snake_case__					=					[0.8, 0.9]







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  UMTaModelTester(self )







     @unittest.skip('Test has a segmentation fault on torch 1.8.0' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.model_tester.prepare_config_and_inputs()
           UpperCamelCase					       =  UMTaModel(config_and_inputs[0] ).to(_SCREAMING_SNAKE_CASE )
           with tempfile.TemporaryDirectory() as tmpdirname:
                 torch.onnx.export(
                     _SCREAMING_SNAKE_CASE   ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,						f'{tmpdirname}/t5_test.onnx'   ,						export_params=_SCREAMING_SNAKE_CASE   ,						opset_version=9   ,						input_names=['input_ids', 'decoder_input_ids']   ,						)







     @unittest.skipIf(torch_device == 'cpu'   ,						'Cant do half precision' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
           UpperCamelCase					       =  self.model_tester.prepare_config_and_inputs()
           UpperCamelCase					       =  config_and_inputs[0]
           UpperCamelCase					       =  UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval()
           model.to(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  {
               'head_mask': torch.zeros(config.num_layers   ,						config.num_heads   ,						device=_SCREAMING_SNAKE_CASE ),
               'decoder_head_mask': torch.zeros(config.num_decoder_layers   ,						config.num_heads   ,						device=_SCREAMING_SNAKE_CASE ),
               'cross_attn_head_mask': torch.zeros(config.num_decoder_layers   ,						config.num_heads   ,						device=_SCREAMING_SNAKE_CASE ),
           }

           for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE   ,						head_masking.items() ):
                 UpperCamelCase					       =  {name: mask}
                 # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                 if name == "head_mask":
                       UpperCamelCase					       =  torch.ones(
                           config.num_decoder_layers   ,						config.num_heads   ,						device=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  model.generate(
                     config_and_inputs[1]['input_ids']   ,						num_beams=1   ,						max_length=3   ,						output_attentions=_SCREAMING_SNAKE_CASE   ,						return_dict_in_generate=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)
                 # We check the state of decoder_attentions and cross_attentions just from the last step
                 UpperCamelCase					       =  out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                 self.assertEqual(sum([w.sum().item() for w in attn_weights] )   ,						0.0 )







     @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     @slow
     @unittest.skip(
         'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  UMTaForConditionalGeneration.from_pretrained('google/umt5-small'   ,						return_dict=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  AutoTokenizer.from_pretrained('google/umt5-small'   ,						use_fast=_SCREAMING_SNAKE_CASE   ,						legacy=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  [
               'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
               'No se como puedo <extra_id_0>.',
               'This is the reason why we <extra_id_0> them.',
               'The <extra_id_0> walks in <extra_id_1>, seats',
               'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
           ]
           UpperCamelCase					       =  tokenizer(_SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						padding=_SCREAMING_SNAKE_CASE ).input_ids
           # fmt: off
           UpperCamelCase					       =  torch.tensor(
               [
                   [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                   [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
               ] )
           # fmt: on
           torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model.generate(input_ids.to(_SCREAMING_SNAKE_CASE ) )
           UpperCamelCase					       =  [
               '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
               '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
               '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
               '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
               '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
           ]
           UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
           self.assertEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )











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__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,						'prajjwal1/bert-tiny' )
           UpperCamelCase					       =  BertTokenizer.from_pretrained('bert-base-uncased' )

           UpperCamelCase					       =  bertabert.config.encoder.vocab_size
           UpperCamelCase					       =  tokenizer.sep_token_id
           UpperCamelCase					       =  tokenizer.cls_token_id
           UpperCamelCase					       =  128

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

           UpperCamelCase					       =  train_dataset.select(range(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

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

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

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

           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()

           UpperCamelCase					       =  SeqaSeqTrainingArguments(
               output_dir=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



