



'''simple docstring'''
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						None
     SCREAMING_SNAKE_CASE__		   =						None







     @property
     def lowerCAmelCase     (		self    ):
         return self.feat_extract_tester.prepare_feat_extract_dict()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'feature_size'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'sampling_rate'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'padding_value'    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	List[str]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Tuple		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Optional[Any]		    =  BatchFeature({input_name: speech_inputs}    )

         self.assertTrue(all(len(_lowerCamelCase    ) == len(_lowerCamelCase    ) for x, y in zip(_lowerCamelCase     ,			processed_features[input_name]    )    )    )

         __UpperCamelCase	:	List[str]		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='np'    )

         __UpperCamelCase	:	Dict		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Optional[int]		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	str		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='pt'    )

         __UpperCamelCase	:	List[str]		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Tuple		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     @require_tf
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	List[str]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	int		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='tf'    )

         __UpperCamelCase	:	Dict		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Optional[int]		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False    ):
         def _inputs_have_equal_length(_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[int]		    =  len(input[0]    )
             for input_slice in input[1:]:
                 if len(_lowerCamelCase    ) != length:
                     return False
             return True

         def _inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    ):
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                 return False

             for input_slice_a, input_slice_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
                 if not np.allclose(np.asarray(_lowerCamelCase    )     ,			np.asarray(_lowerCamelCase    )     ,			atol=1E-3    ):
                     return False
             return True

         __UpperCamelCase	:	Dict		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Dict		    =  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Any		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Any		    =  self.feat_extract_tester.seq_length_diff
         __UpperCamelCase	:	List[Any]		    =  self.feat_extract_tester.max_seq_length + pad_diff
         __UpperCamelCase	:	Any		    =  self.feat_extract_tester.min_seq_length
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.batch_size
         __UpperCamelCase	:	str		    =  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         __UpperCamelCase	:	str		    =  feat_extract.pad(_lowerCamelCase     ,			padding=_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'    )
         __UpperCamelCase	:	List[str]		    =  input_a[input_name]

         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[-1]    )    )
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )
         __UpperCamelCase	:	int		    =  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='max_length'    )[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=_lowerCamelCase     ,			return_tensors='np'    )
         __UpperCamelCase	:	Dict		    =  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )
         self.assertTrue(len(input_a[0]    ) == pad_min_length    )
         self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

         if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         __UpperCamelCase	:	str		    =  feat_extract.pad(_lowerCamelCase     ,			pad_to_multiple_of=1_0    )
         __UpperCamelCase	:	Any		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			pad_to_multiple_of=1_0    )
         __UpperCamelCase	:	str		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			pad_to_multiple_of=1_0     ,			max_length=_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			pad_to_multiple_of=1_0     ,			max_length=_lowerCamelCase     ,			return_tensors='np'     ,			)
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         self.assertTrue(all(len(_lowerCamelCase    ) % 1_0 == 0 for x in input_a    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )

         __UpperCamelCase	:	List[str]		    =  pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
         self.assertTrue(all(len(_lowerCamelCase    ) == expected_mult_pad_length for x in input_a    )    )
         self.assertEqual(input_a.shape[:2]     ,			(batch_size, expected_mult_pad_length)    )

         if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size    )

         # Check padding value is correct
         __UpperCamelCase	:	List[str]		    =  (np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
             < 1E-3    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False    ):
         def _inputs_have_equal_length(_lowerCamelCase    ):
             __UpperCamelCase	:	Any		    =  len(input[0]    )
             for input_slice in input[1:]:
                 if len(_lowerCamelCase    ) != length:
                     return False
             return True

         def _inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    ):
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                 return False

             for input_slice_a, input_slice_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
                 if not np.allclose(np.asarray(_lowerCamelCase    )     ,			np.asarray(_lowerCamelCase    )     ,			atol=1E-3    ):
                     return False
             return True

         __UpperCamelCase	:	Optional[Any]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	int		    =  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Tuple		    =  BatchFeature({input_name: speech_inputs}    )

         # truncate to smallest
         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			truncation=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )    )
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )

         # truncate to smallest with np
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			return_tensors='np'     ,			truncation=_lowerCamelCase     ,			)
         __UpperCamelCase	:	Optional[int]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			return_tensors='np'    )
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )

         # truncate to middle
         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			truncation=_lowerCamelCase     ,			return_tensors='np'     ,			)
         __UpperCamelCase	:	Dict		    =  input_a[input_name]

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			truncation=_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			return_tensors='np'    )
         __UpperCamelCase	:	Tuple		    =  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			truncation=_lowerCamelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			truncation=_lowerCamelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			truncation=_lowerCamelCase    )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			truncation=_lowerCamelCase    )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         __UpperCamelCase	:	Union[str, Any]		    =  1_2
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			pad_to_multiple_of=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			)
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			pad_to_multiple_of=_lowerCamelCase     ,			)
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         __UpperCamelCase	:	Optional[int]		    =  len(speech_inputs[0]    )
         if expected_length % pad_to_multiple_of != 0:
             __UpperCamelCase	:	Tuple		    =  ((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]    ) == expected_length    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         self._check_padding(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_padding(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_truncation(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_truncation(numpify=_lowerCamelCase    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	List[str]		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	Any		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Optional[Any]		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )[input_name]
         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='pt'    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )







     @require_tf
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	Any		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Dict		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Dict		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )[input_name]
         __UpperCamelCase	:	int		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='tf'    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.feat_extract_dict
         __UpperCamelCase	:	Optional[int]		    =  True
         __UpperCamelCase	:	Dict		    =  self.feature_extraction_class(**_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	List[str]		    =  [len(_lowerCamelCase    ) for x in speech_inputs]
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Dict		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )
         self.assertIn('attention_mask'     ,			_lowerCamelCase    )
         self.assertListEqual(list(processed.attention_mask.shape    )     ,			list(processed[input_name].shape[:2]    )    )
         self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feat_extract_dict
         __UpperCamelCase	:	Tuple		    =  True
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	str		    =  [len(_lowerCamelCase    ) for x in speech_inputs]
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	List[str]		    =  BatchFeature({input_name: speech_inputs}    )
         __UpperCamelCase	:	Any		    =  min(_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			return_tensors='np'    )
         self.assertIn('attention_mask'     ,			_lowerCamelCase    )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape    )     ,			[processed_pad[input_name].shape[0], max_length]    )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()     ,			[max_length for x in speech_inputs]    )




'''simple docstring'''
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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

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

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

         def _map_to_encoder_decoder_inputs(_lowerCamelCase    ):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(batch['article']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=5_1_2    )
             __UpperCamelCase	:	Optional[int]		    =  tokenizer(batch['highlights']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=1_2_8    )
             __UpperCamelCase	:	Any		    =  inputs.input_ids
             __UpperCamelCase	:	Optional[Any]		    =  inputs.attention_mask

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  outputs.attention_mask

             assert all(len(_lowerCamelCase    ) == 5_1_2 for x in inputs.input_ids    )
             assert all(len(_lowerCamelCase    ) == 1_2_8 for x in outputs.input_ids    )

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

             __UpperCamelCase	:	Dict		    =  sum([int(pred_str[i] == label_str[i]    ) for i in range(len(_lowerCamelCase    )    )]    ) / len(_lowerCamelCase    )

             return {"accuracy": accuracy}

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

         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()

         __UpperCamelCase	:	List[Any]		    =  SeqaSeqTrainingArguments(
             output_dir=_lowerCamelCase     ,			per_device_train_batch_size=_lowerCamelCase     ,			per_device_eval_batch_size=_lowerCamelCase     ,			predict_with_generate=_lowerCamelCase     ,			evaluation_strategy='steps'     ,			do_train=_lowerCamelCase     ,			do_eval=_lowerCamelCase     ,			warmup_steps=0     ,			eval_steps=2     ,			logging_steps=2     ,			)

         # instantiate trainer
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()



