


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowerCamelCase		   =			1e-4

if is_torch_available():
 import torch

 from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
 from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class A    :






      def __init__(   self		:	Dict     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	Any=16     ,  lowercase_		:	Dict=13     ,  lowercase_		:	List[Any]=7     ,  lowercase_		:	str=14     ,  lowercase_		:	List[str]=10     ,  lowercase_		:	Any=19     ,  lowercase_		:	int=5     ,  lowercase_		:	List[Any]=4     ,  lowercase_		:	int=True     ,  lowercase_		:	Tuple=16     ,  lowercase_		:	Tuple=2     ,  lowercase_		:	Union[str, Any]=4     ,  lowercase_		:	Dict=4     ,  lowercase_		:	List[str]="gelu"     ,  lowercase_		:	List[str]=0.1     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	Optional[Any]=[1, 2, 3, 4, 5]     ,  lowercase_		:	Any=25     ,  lowercase_		:	Optional[int]=5     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =d_model
             _lowerCamelCase			:						Optional[Any]            =parent
             _lowerCamelCase			:						str            =batch_size
             _lowerCamelCase			:						List[Any]            =prediction_length
             _lowerCamelCase			:						Any            =context_length
             _lowerCamelCase			:						str            =cardinality
             _lowerCamelCase			:						int            =num_time_features
             _lowerCamelCase			:						List[Any]            =lags_sequence
             _lowerCamelCase			:						List[Any]            =embedding_dimension
             _lowerCamelCase			:						Union[str, Any]            =is_training
             _lowerCamelCase			:						str            =hidden_size
             _lowerCamelCase			:						Tuple            =num_hidden_layers
             _lowerCamelCase			:						int            =num_attention_heads
             _lowerCamelCase			:						Optional[int]            =intermediate_size
             _lowerCamelCase			:						Dict            =hidden_act
             _lowerCamelCase			:						Tuple            =hidden_dropout_prob
             _lowerCamelCase			:						Dict            =attention_probs_dropout_prob

             _lowerCamelCase			:						List[Any]            =context_length
             _lowerCamelCase			:						List[str]            =prediction_length + label_length
             _lowerCamelCase			:						Union[str, Any]            =label_length

             _lowerCamelCase			:						Union[str, Any]            =moving_average
             _lowerCamelCase			:						Tuple            =autocorrelation_factor






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		int:
             """simple docstring"""




             return AutoformerConfig(
                 d_model=self.d_model     ,  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     ,  prediction_length=self.prediction_length     ,  context_length=self.context_length     ,  label_length=self.label_length     ,  lags_sequence=self.lags_sequence     ,  num_time_features=self.num_time_features     ,  num_static_categorical_features=1     ,  cardinality=[self.cardinality]     ,  embedding_dimension=[self.embedding_dimension]     ,  moving_average=self.moving_average     ,  )






      def 			lowerCamelCase			(   self		:	Dict     ,  lowercase_		:	List[Any]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =config.context_length + max(config.lags_sequence					)

             _lowerCamelCase			:						List[str]            =ids_tensor([self.batch_size, 1]     ,  config.cardinality[0]					)
             _lowerCamelCase			:						int            =floats_tensor([self.batch_size, _past_length, config.num_time_features]					)
             _lowerCamelCase			:						Dict            =floats_tensor([self.batch_size, _past_length]					)
             _lowerCamelCase			:						Union[str, Any]            =floats_tensor([self.batch_size, _past_length]					) > 0.5

             # decoder inputs
             _lowerCamelCase			:						List[str]            =floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]					)
             _lowerCamelCase			:						Tuple            =floats_tensor([self.batch_size, config.prediction_length]					)

             _lowerCamelCase			:						Dict            ={
                 'past_values': past_values,
                 'static_categorical_features': static_categorical_features,
                 'past_time_features': past_time_features,
                 'past_observed_mask': past_observed_mask,
                 'future_time_features': future_time_features,
                 'future_values': future_values,
             }
             return inputs_dict






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.get_config()
             _lowerCamelCase			:						Optional[int]            =self.prepare_autoformer_inputs_dict(lowercase_					)
             return config, inputs_dict






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self.prepare_config_and_inputs()
             return config, inputs_dict






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Dict     ,  lowercase_		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =AutoformerModel(config=lowercase_					).to(lowercase_					).eval()
             _lowerCamelCase			:						List[Any]            =model(**lowercase_					)

             _lowerCamelCase			:						List[Any]            =outputs.encoder_last_hidden_state
             _lowerCamelCase			:						int            =outputs.last_hidden_state

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						str            =model.get_encoder()
                    encoder.save_pretrained(lowercase_					)
                    _lowerCamelCase			:						Any            =AutoformerEncoder.from_pretrained(lowercase_					).to(lowercase_					)

             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =model.create_network_inputs(**lowercase_					)
             _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]					)

             _lowerCamelCase			:						Dict            =torch.cat(
                 (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])     ,  dim=-1     ,  )
             _lowerCamelCase			:						Optional[int]            =encoder(inputs_embeds=lowercase_					)[0]
             self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3					)

             _lowerCamelCase			:						Optional[int]            =(
                 torch.mean(transformer_inputs[:, : config.context_length, ...]     ,  dim=1					)
                 .unsqueeze(1					)
                 .repeat(1     ,  config.prediction_length     ,  1					)
             )
             _lowerCamelCase			:						Optional[int]            =torch.zeros(
                 [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]     ,  device=enc_input.device     ,  )

             _lowerCamelCase			:						List[Any]            =torch.cat(
                 (
                     torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)     ,  dim=1					),
                     feature[:, config.context_length - config.label_length :, ...],
                 )     ,  dim=-1     ,  )
             _lowerCamelCase			:						Tuple            =torch.cat(
                 (
                     torch.cat((trend_input[:, -config.label_length :, ...], mean)     ,  dim=1					),
                     feature[:, config.context_length - config.label_length :, ...],
                 )     ,  dim=-1     ,  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						Optional[int]            =model.get_decoder()
                    decoder.save_pretrained(lowercase_					)
                    _lowerCamelCase			:						Union[str, Any]            =AutoformerDecoder.from_pretrained(lowercase_					).to(lowercase_					)

             _lowerCamelCase			:						Tuple            =decoder(
                 trend=lowercase_     ,  inputs_embeds=lowercase_     ,  encoder_hidden_states=lowercase_     ,  )[0]

             self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3					)




@require_torch
class A    (  UpperCamelCase_       ,				UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Optional[Any] =(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      UpperCamelCase__				:    Optional[Any] =(AutoformerForPrediction,) if is_torch_available() else ()
      UpperCamelCase__				:    Optional[Any] ={'feature-extraction': AutoformerModel} if is_torch_available() else {}
      UpperCamelCase__				:    str =False
      UpperCamelCase__				:    Dict =False
      UpperCamelCase__				:    Optional[int] =False
      UpperCamelCase__				:    Union[str, Any] =False
      UpperCamelCase__				:    List[str] =False
      UpperCamelCase__				:    List[Any] =False






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =AutoformerModelTester(self					)
             _lowerCamelCase			:						List[Any]            =ConfigTester(self     ,  config_class=lowercase_     ,  has_text_modality=lowercase_					)






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




             self.config_tester.run_common_tests()






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    _lowerCamelCase			:						List[str]            =model_class(lowercase_					)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(lowercase_					)
                           _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =model_class.from_pretrained(lowercase_     ,  output_loading_info=lowercase_					)
                    self.assertEqual(info['missing_keys']     ,  []					)






      def 			lowerCamelCase			(   self		:	str					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_encoder_decoder_model_standalone(*lowercase_					)






      @unittest.skip(reason='Model has no tokens embeddings'					)
      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		int:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						str            =inspect.signature(getattr(lowercase_     ,  'forward'					)					)
             # The main input is the name of the argument after `self`
             _lowerCamelCase			:						str            =list(model_signature.parameters.keys()					)[1]
             self.assertEqual(AutoformerModel.main_input_name     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase			:						Any            =model_class(lowercase_					)
                    _lowerCamelCase			:						Dict            =inspect.signature(model.forward					)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    _lowerCamelCase			:						Tuple            =[*signature.parameters.keys()]

                    _lowerCamelCase			:						Union[str, Any]            =[
                        'past_values',
                        'past_time_features',
                        'past_observed_mask',
                        'static_categorical_features',
                        'static_real_features',
                        'future_values',
                        'future_time_features',
                    ]

                    if model.__class__.__name__ in ["AutoformerForPrediction"]:
                           expected_arg_names.append('future_observed_mask'					)

                    expected_arg_names.extend(
                        [
                            'decoder_attention_mask',
                            'head_mask',
                            'decoder_head_mask',
                            'cross_attn_head_mask',
                            'encoder_outputs',
                            'past_key_values',
                            'output_hidden_states',
                            'output_attentions',
                            'use_cache',
                            'return_dict',
                        ]					)

                    self.assertListEqual(arg_names[: len(lowercase_					)]     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						int            =self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase			:						List[str]            =True

             _lowerCamelCase			:						Dict            =getattr(self.model_tester     ,  'seq_length'     ,  lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =getattr(self.model_tester     ,  'decoder_seq_length'     ,  lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =getattr(self.model_tester     ,  'encoder_seq_length'     ,  lowercase_					)
             _lowerCamelCase			:						Optional[int]            =getattr(self.model_tester     ,  'd_model'     ,  lowercase_					)
             _lowerCamelCase			:						Dict            =getattr(self.model_tester     ,  'num_attention_heads'     ,  lowercase_					)
             _lowerCamelCase			:						str            =d_model // num_attention_heads

             for model_class in self.all_model_classes:
                    _lowerCamelCase			:						str            =True
                    _lowerCamelCase			:						Tuple            =False
                    _lowerCamelCase			:						Dict            =True
                    _lowerCamelCase			:						Optional[Any]            =model_class(lowercase_					)
                    model.to(lowercase_					)
                    model.eval()
                    with torch.no_grad():
                           _lowerCamelCase			:						Union[str, Any]            =model(**self._prepare_for_class(lowercase_     ,  lowercase_					)					)
                    _lowerCamelCase			:						int            =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                    self.assertEqual(len(lowercase_					)     ,  self.model_tester.num_hidden_layers					)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    _lowerCamelCase			:						int            =True
                    _lowerCamelCase			:						int            =model_class(lowercase_					)
                    model.to(lowercase_					)
                    model.eval()
                    with torch.no_grad():
                           _lowerCamelCase			:						int            =model(**self._prepare_for_class(lowercase_     ,  lowercase_					)					)
                    _lowerCamelCase			:						int            =outputs.encoder_attentions
                    self.assertEqual(len(lowercase_					)     ,  self.model_tester.num_hidden_layers					)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]					)     ,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]     ,  )
                    _lowerCamelCase			:						Any            =len(lowercase_					)

                    _lowerCamelCase			:						Any            =7

                    if "last_hidden_state" in outputs:
                           correct_outlen += 1

                    if "trend" in outputs:
                           correct_outlen += 1

                    if "past_key_values" in outputs:
                           correct_outlen += 1  # past_key_values have been returned

                    if "loss" in outputs:
                           correct_outlen += 1

                    if "params" in outputs:
                           correct_outlen += 1

                    self.assertEqual(lowercase_     ,  lowercase_					)

                    # decoder attentions
                    _lowerCamelCase			:						List[Any]            =outputs.decoder_attentions
                    self.assertIsInstance(lowercase_     ,  (list, tuple)					)
                    self.assertEqual(len(lowercase_					)     ,  self.model_tester.num_hidden_layers					)
                    self.assertListEqual(
                        list(decoder_attentions[0].shape[-3:]					)     ,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]     ,  )

                    # cross attentions
                    _lowerCamelCase			:						Optional[int]            =outputs.cross_attentions
                    self.assertIsInstance(lowercase_     ,  (list, tuple)					)
                    self.assertEqual(len(lowercase_					)     ,  self.model_tester.num_hidden_layers					)
                    self.assertListEqual(
                        list(cross_attentions[0].shape[-3:]					)     ,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]     ,  )

             # Check attention is always last and order is fine
             _lowerCamelCase			:						Tuple            =True
             _lowerCamelCase			:						Any            =True
             _lowerCamelCase			:						List[str]            =model_class(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             with torch.no_grad():
                    _lowerCamelCase			:						Any            =model(**self._prepare_for_class(lowercase_     ,  lowercase_					)					)

             self.assertEqual(out_len + 2     ,  len(lowercase_					)					)

             _lowerCamelCase			:						Any            =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

             self.assertEqual(len(lowercase_					)     ,  self.model_tester.num_hidden_layers					)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]					)     ,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]     ,  )






      @is_flaky()
      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             super().test_retain_grad_hidden_states_attentions()




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[int]="train-batch.pt"	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'						,     filename=SCREAMING_SNAKE_CASE__						,     repo_type='dataset'	)
       _lowerCamelCase			:						Tuple            =torch.load(SCREAMING_SNAKE_CASE__						,     map_location=SCREAMING_SNAKE_CASE__	)
       return batch




@require_torch
@slow
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(lowercase_					)
             _lowerCamelCase			:						Dict            =prepare_batch()

             with torch.no_grad():
                    _lowerCamelCase			:						Dict            =model(
                        past_values=batch['past_values']     ,  past_time_features=batch['past_time_features']     ,  past_observed_mask=batch['past_observed_mask']     ,  static_categorical_features=batch['static_categorical_features']     ,  future_values=batch['future_values']     ,  future_time_features=batch['future_time_features']     ,  )[0]

             _lowerCamelCase			:						Tuple            =torch.Size(
                 (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)					)
             self.assertEqual(output.shape     ,  lowercase_					)

             _lowerCamelCase			:						int            =torch.tensor(
                 [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]     ,  device=lowercase_					)
             self.assertTrue(torch.allclose(output[0, :3, :3]     ,  lowercase_     ,  atol=lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(lowercase_					)
             _lowerCamelCase			:						int            =prepare_batch('val-batch.pt'					)
             with torch.no_grad():
                    _lowerCamelCase			:						Optional[Any]            =model(
                        past_values=batch['past_values']     ,  past_time_features=batch['past_time_features']     ,  past_observed_mask=batch['past_observed_mask']     ,  static_categorical_features=batch['static_categorical_features']     ,  ).encoder_last_hidden_state
             _lowerCamelCase			:						Optional[int]            =torch.Size((64, model.config.context_length, model.config.d_model)					)
             self.assertEqual(output.shape     ,  lowercase_					)

             _lowerCamelCase			:						List[Any]            =torch.tensor(
                 [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]     ,  device=lowercase_					)
             self.assertTrue(torch.allclose(output[0, :3, :3]     ,  lowercase_     ,  atol=lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(lowercase_					)
             _lowerCamelCase			:						Any            =prepare_batch('val-batch.pt'					)
             with torch.no_grad():
                    _lowerCamelCase			:						str            =model.generate(
                        static_categorical_features=batch['static_categorical_features']     ,  past_time_features=batch['past_time_features']     ,  past_values=batch['past_values']     ,  future_time_features=batch['future_time_features']     ,  past_observed_mask=batch['past_observed_mask']     ,  )
             _lowerCamelCase			:						List[str]            =torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)					)
             self.assertEqual(outputs.sequences.shape     ,  lowercase_					)

             _lowerCamelCase			:						Optional[int]            =torch.tensor([3130.6763, 4056.5293, 7053.0786]     ,  device=lowercase_					)
             _lowerCamelCase			:						List[Any]            =outputs.sequences.mean(dim=1					)
             self.assertTrue(torch.allclose(mean_prediction[0, -3:]     ,  lowercase_     ,  rtol=1E-1					)					)






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()


