
"""simple docstring"""

import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class __lowerCAmelCase		(				tf.keras.layers.Layer      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      super().__init__()
      __UpperCamelCase      =	pad_token_id
      __UpperCamelCase      =	max_length
      __UpperCamelCase      =	vocab
      __UpperCamelCase      =	merges
      __UpperCamelCase      =	BytePairTokenizer(__UpperCAmelCase     ,					__UpperCAmelCase     ,					sequence_length=__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	[' '.join(__UpperCAmelCase				) for m in tokenizer.bpe_ranks.keys()]
      __UpperCamelCase      =	tokenizer.get_vocab()
      return cls(__UpperCAmelCase     ,					__UpperCAmelCase     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	GPTaTokenizer.from_pretrained(__UpperCAmelCase     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)
      return cls.from_tokenizer(__UpperCAmelCase     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return cls(**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return {
          "vocab": self.vocab,
          "merges": self.merges,
          "max_length": self.max_length,
          "pad_token_id": self.pad_token_id,
      }







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self.tf_tokenizer(__UpperCAmelCase				)
      __UpperCamelCase      =	tf.ones_like(__UpperCAmelCase				)

      if self.pad_token_id is not None:
       # pad the tokens up to max length
       __UpperCamelCase      =	max_length if max_length is not None else self.max_length

       if max_length is not None:
        __UpperCamelCase  ,     __UpperCamelCase      =	pad_model_inputs(
            __UpperCAmelCase     ,					max_seq_length=__UpperCAmelCase     ,					pad_value=self.pad_token_id				)

      return {"attention_mask": attention_mask, "input_ids": input_ids}





"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def 							A						(    )				->     Any:
 __UpperCamelCase      =	{
     'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
     'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
     'content': ['a ' * 2_0, 'a ' * 3_0, 'b ' * 7],
 }
 __UpperCamelCase      =	Dataset.from_dict(snake_case		)
 return dataset





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase      =	make_duplicate_clusters(__UpperCAmelCase     ,					0.8_5				)
      self.assertEqual(len(duplicate_clusters[0]				)     ,					2				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase  ,     __UpperCamelCase      =	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				)



