

"""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  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @slow
 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny"     )
    __lowercase								=						BertTokenizer.from_pretrained("bert-base-uncased"     )

    __lowercase								=						bertabert.config.encoder.vocab_size
    __lowercase								=						tokenizer.sep_token_id
    __lowercase								=						tokenizer.cls_token_id
    __lowercase								=						1_2_8

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

    __lowercase								=						train_dataset.select(range(3_2     )     )
    __lowercase								=						val_dataset.select(range(1_6     )     )

    __lowercase								=						4

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

       __lowercase								=						outputs.input_ids
       __lowercase								=						outputs.input_ids.copy()
       __lowercase								=						[
           [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
       ]
       __lowercase								=						outputs.attention_mask

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

       return batch

    def _compute_metrics(UpperCAmelCase__							:      List[Any]     ):
       __lowercase								=						pred.label_ids
       __lowercase								=						pred.predictions

       # all unnecessary tokens are removed
       __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__, skip_special_tokens=UpperCAmelCase__     )
       __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__, skip_special_tokens=UpperCAmelCase__     )

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

       return {"accuracy": accuracy}

    # map train dataset
    __lowercase								=						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								=						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								=						self.get_auto_remove_tmp_dir()

    __lowercase								=						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								=						SeqaSeqTrainer(
        model=UpperCAmelCase__, args=UpperCAmelCase__, compute_metrics=_compute_metrics, train_dataset=UpperCAmelCase__, eval_dataset=UpperCAmelCase__, tokenizer=UpperCAmelCase__, )

    # start training
    trainer.train()







"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters




def   _A  (  )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   __lowercase								=						Dataset.from_dict(UpperCamelCase_)
   return dataset




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      int     ):
    __lowercase								=						get_dataset()
    __lowercase								=						make_duplicate_clusters(UpperCAmelCase__, 0.85     )
    self.assertEqual(len(duplicate_clusters[0]     ), 2     )



 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						get_dataset()
    __lowercase		,__lowercase								=						deduplicate_dataset(UpperCAmelCase__     )
    self.assertEqual(len(UpperCAmelCase__     ), 2     )
    print(UpperCAmelCase__     )
    self.assertEqual(duplicate_clusters[0][0]["copies"], 2     )
    self.assertEqual(duplicate_clusters[0][0]["is_extreme"], UpperCAmelCase__     )




