'''simple docstring'''
import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a=	get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class       __lowercase		(    _lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    ErnieMTokenizer
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    True
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self     ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         __UpperCamelCase						:						int		     =	ErnieMTokenizer(_lowerCamelCase  ,      unk_token='<unk>'  ,      pad_token='<pad>'     )
         tokenizer.save_pretrained(self.tmpdirname     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[str]		     =	'this is a test'
         __UpperCamelCase						:						Optional[Any]		     =	'this is a test'
         return input_text, output_text







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	'<pad>'
         __UpperCamelCase						:						Dict		     =	0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase     )  ,      _lowerCamelCase     )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase     )  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	list(self.get_tokenizer().get_vocab().keys()     )

         self.assertEqual(vocab_keys[0]  ,      '<pad>'     )
         self.assertEqual(vocab_keys[1]  ,      '<unk>'     )
         self.assertEqual(vocab_keys[-1]  ,      '▁eloquent'     )
         self.assertEqual(len(_lowerCamelCase     )  ,      3_0_0_0_0     )







     def lowerCAmelCase  (		self     ):
         self.assertEqual(self.get_tokenizer().vocab_size  ,      3_0_0_0_0     )







     def lowerCAmelCase  (		self     ):
         if not self.test_rust_tokenizer:
             return

         __UpperCamelCase						:						str		     =	self.get_tokenizer()
         __UpperCamelCase						:						Union[str, Any]		     =	self.get_rust_tokenizer()

         __UpperCamelCase						:						Optional[Any]		     =	'I was born in 92000, and this is falsé.'

         __UpperCamelCase						:						Union[str, Any]		     =	tokenizer.tokenize(_lowerCamelCase     )
         __UpperCamelCase						:						str		     =	rust_tokenizer.tokenize(_lowerCamelCase     )
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase     )
         __UpperCamelCase						:						Tuple		     =	rust_tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase     )
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase     )

         __UpperCamelCase						:						int		     =	self.get_rust_tokenizer()
         __UpperCamelCase						:						int		     =	tokenizer.encode(_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	rust_tokenizer.encode(_lowerCamelCase     )
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	ErnieMTokenizer(_lowerCamelCase  ,      do_lower_case=_lowerCamelCase  ,      unk_token='<unk>'  ,      pad_token='<pad>'     )

         __UpperCamelCase						:						List[Any]		     =	tokenizer.tokenize('This is a test'     )
         self.assertListEqual(_lowerCamelCase  ,      ['▁this', '▁is', '▁a', '▁test']     )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase     )  ,      [4_8, 2_5, 2_1, 1_2_8_9]     )

         __UpperCamelCase						:						Any		     =	tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
         self.assertListEqual(
             _lowerCamelCase  ,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']     )
         __UpperCamelCase						:						Tuple		     =	tokenizer.convert_tokens_to_ids(_lowerCamelCase     )
         self.assertListEqual(_lowerCamelCase  ,      [3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]     )

         __UpperCamelCase						:						Any		     =	tokenizer.convert_ids_to_tokens(_lowerCamelCase     )
         self.assertListEqual(
             _lowerCamelCase  ,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']  ,      )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	ErnieMTokenizer(_lowerCamelCase  ,      unk_token='<unk>'  ,      pad_token='<pad>'     )

         __UpperCamelCase						:						int		     =	tokenizer.encode('sequence builders'     )
         __UpperCamelCase						:						List[str]		     =	tokenizer.encode('multi-sequence build'     )

         __UpperCamelCase						:						Any		     =	tokenizer.build_inputs_with_special_tokens(_lowerCamelCase     )
         __UpperCamelCase						:						Optional[Any]		     =	tokenizer.build_inputs_with_special_tokens(_lowerCamelCase  ,      _lowerCamelCase     )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
             tokenizer.sep_token_id
         ] + text_a + [tokenizer.sep_token_id]







     @slow
     def lowerCAmelCase  (		self     ):
         # fmt: off
         __UpperCamelCase						:						List[Any]		     =	{'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase  ,      model_name='susnato/ernie-m-base_pytorch'  ,      sequences=[
                 'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
                 'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
                 'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
                 'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
                 'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
                 'conditioning on both left and right context in all layers.',
                 'The quick brown fox jumps over the lazy dog.',
             ]  ,      )




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







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



