

'''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 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@slow
				@require_torch
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,				'''prajjwal1/bert-tiny'''      )
										lowercase   :       str								=BertTokenizer.from_pretrained('''bert-base-uncased'''      )

										lowercase   :       List[str]								=bertabert.config.encoder.vocab_size
										lowercase   :       Tuple								=tokenizer.sep_token_id
										lowercase   :       Tuple								=tokenizer.cls_token_id
										lowercase   :       Optional[int]								=128

										lowercase   :       List[str]								=datasets.load_dataset('''cnn_dailymail'''		,				'''3.0.0'''		,				split='''train[:1%]'''      )
										lowercase   :       List[Any]								=datasets.load_dataset('''cnn_dailymail'''		,				'''3.0.0'''		,				split='''validation[:1%]'''      )

										lowercase   :       List[str]								=train_dataset.select(range(32      )      )
										lowercase   :       Optional[Any]								=val_dataset.select(range(16      )      )

										lowercase   :       Optional[int]								=4

										def _map_to_encoder_decoder_inputs(UpperCAmelCase__    :		str      ):
																# Tokenizer will automatically set [BOS] <text> [EOS]
																lowercase   :       Tuple								=tokenizer(batch['''article''']		,				padding='''max_length'''		,				truncation=UpperCAmelCase__		,				max_length=512      )
																lowercase   :       Union[str, Any]								=tokenizer(batch['''highlights''']		,				padding='''max_length'''		,				truncation=UpperCAmelCase__		,				max_length=128      )
																lowercase   :       Optional[int]								=inputs.input_ids
																lowercase   :       List[Any]								=inputs.attention_mask

																lowercase   :       List[Any]								=outputs.input_ids
																lowercase   :       Optional[int]								=outputs.input_ids.copy()
																lowercase   :       List[Any]								=[
																    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																]
																lowercase   :       Dict								=outputs.attention_mask

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

																return batch

										def _compute_metrics(UpperCAmelCase__    :		Optional[int]      ):
																lowercase   :       Optional[Any]								=pred.label_ids
																lowercase   :       Union[str, Any]								=pred.predictions

																# all unnecessary tokens are removed
																lowercase   :       Union[str, Any]								=tokenizer.batch_decode(UpperCAmelCase__		,				skip_special_tokens=UpperCAmelCase__      )
																lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__		,				skip_special_tokens=UpperCAmelCase__      )

																lowercase   :       str								=sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase__      )      )]      ) / len(UpperCAmelCase__      )

																return {"accuracy": accuracy}

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

										lowercase   :       str								=self.get_auto_remove_tmp_dir()

										lowercase   :       int								=SeqaSeqTrainingArguments(
										    output_dir=UpperCAmelCase__		,				per_device_train_batch_size=UpperCAmelCase__		,				per_device_eval_batch_size=UpperCAmelCase__		,				predict_with_generate=UpperCAmelCase__		,				evaluation_strategy='''steps'''		,				do_train=UpperCAmelCase__		,				do_eval=UpperCAmelCase__		,				warmup_steps=0		,				eval_steps=2		,				logging_steps=2		,				)

										# instantiate trainer
										lowercase   :       Union[str, Any]								=SeqaSeqTrainer(
										    model=UpperCAmelCase__		,				args=UpperCAmelCase__		,				compute_metrics=_compute_metrics		,				train_dataset=UpperCAmelCase__		,				eval_dataset=UpperCAmelCase__		,				tokenizer=UpperCAmelCase__		,				)

										# start training
										trainer.train()








'''simple docstring'''





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BioGptTokenizer
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase   :       List[str]								=[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''w</w>''',
										    '''r</w>''',
										    '''t</w>''',
										    '''lo''',
										    '''low''',
										    '''er</w>''',
										    '''low</w>''',
										    '''lowest</w>''',
										    '''newer</w>''',
										    '''wider</w>''',
										    '''<unk>''',
										]
										lowercase   :       Any								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )
										lowercase   :       Union[str, Any]								=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

										lowercase   :       Optional[int]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
										lowercase   :       Union[str, Any]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''merges_file''']      )
										with open(self.vocab_file		,				'''w'''      ) as fp:
																fp.write(json.dumps(UpperCAmelCase__      )      )
										with open(self.merges_file		,				'''w'''      ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								='''lower newer'''
										lowercase   :       str								='''lower newer'''
										return input_text, output_text







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptTokenizer(self.vocab_file		,				self.merges_file      )

										lowercase   :       Any								='''lower'''
										lowercase   :       int								=['''low''', '''er</w>''']
										lowercase   :       Optional[Any]								=tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[int]								=tokens + ['''<unk>''']
										lowercase   :       Any								=[14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )		,				UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )

										lowercase   :       List[str]								=tokenizer.encode('''sequence builders'''		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Optional[int]								=tokenizer.encode('''multi-sequence build'''		,				add_special_tokens=UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__		,				UpperCAmelCase__      )

										self.assertTrue(encoded_sentence == [2] + text      )
										self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )





