



'''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 							a__( _a     ):






			@slow
			@require_torch
			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'					,   'prajjwal1/bert-tiny'						)
					snake_case__	     =BertTokenizer.from_pretrained('bert-base-uncased'						)

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

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

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

					snake_case__	     =4

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

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

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

							return batch

					def _compute_metrics(_UpperCAmelCase						):
							snake_case__	     =pred.label_ids
							snake_case__	     =pred.predictions

							# all unnecessary tokens are removed
							snake_case__	     =tokenizer.batch_decode(A_					,   skip_special_tokens=A_						)
							snake_case__	     =tokenizer.batch_decode(A_					,   skip_special_tokens=A_						)

							snake_case__	     =sum([int(pred_str[i] == label_str[i]						) for i in range(len(A_						)						)]						) / len(A_						)

							return {"accuracy": accuracy}

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

					snake_case__	     =self.get_auto_remove_tmp_dir()

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

					# instantiate trainer
					snake_case__	     =SeqaSeqTrainer(
					    model=A_					,   args=A_					,   compute_metrics=_compute_metrics					,   train_dataset=A_					,   eval_dataset=A_					,   tokenizer=A_					,   )

					# start training
					trainer.train()








__snake_case   							=						{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__snake_case   							=						{value: key for key, value in encode_dict.items()}






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			''
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception('encode() accepts only letters of the alphabet and spaces' )
     return encoded






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     if set(_lowercase ) - {"A", "B", " "} != set():
          raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
     __UpperCamelCase =			''
     for word in coded.split():
          while len(_lowercase ) != 0:
               decoded += decode_dict[word[:5]]
               __UpperCamelCase =			word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


