


'''simple docstring'''

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 			UpperCAmelCase			(							unittest.TestCase):
 _lowerCamelCase			:       Optional[int]            =       EsmTokenizer

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    super().setUp()
    UpperCamelCase__			    =    tempfile.mkdtemp()
    # fmt: off
    UpperCamelCase__			    =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
    # fmt: on
    UpperCamelCase__			    =    os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES["vocab_file"]       )
    with open(self.vocab_file,       "w",       encoding="utf-8"       ) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]       )       )

 def 		lowercase_    (			self			:						Optional[int],       **a_			:						Dict       ):





    """simple docstring"""



    return [self.get_tokenizer(**a_       )]

 def 		lowercase_    (			self			:						List[Any],       **a_			:						Dict       ):





    """simple docstring"""



    return self.tokenizer_class.from_pretrained(self.tmpdirname,       **a_       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class(self.vocab_file       )

    UpperCamelCase__			    =    tokenizer.tokenize("LAGVS"       )
    self.assertListEqual(a_,       ["L", "A", "G", "V", "S"]       )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(a_       ),       [4, 5, 6, 7, 8]       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class(self.vocab_file       )

    UpperCamelCase__			    =    "LAGVS"
    self.assertListEqual(tokenizer.encode(a_       ),       [0, 4, 5, 6, 7, 8, 2]       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class(self.vocab_file       )

    UpperCamelCase__			    =    ["LAGVS", "WCB"]
    UpperCamelCase__			    =    tokenizer(a_,       padding=a_       )["input_ids"]

    self.assertListEqual(a_,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class(self.vocab_file       )

    UpperCamelCase__			    =    ["LAGVS", "WCB"]
    UpperCamelCase__			    =    tokenizer(a_,       padding=a_       )["input_ids"]

    self.assertListEqual(a_,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.get_tokenizers(fast=a_       )
    for tokenizer in tokenizers:
       with self.subTest(f'{tokenizer.__class__.__name__}'       ):
          UpperCamelCase__			    =    "<unk>"
          UpperCamelCase__			    =    "<mask>"

          UpperCamelCase__			    =    tokenizer.tokenize(a_       )
          UpperCamelCase__			    =    tokenizer.tokenize(a_       )

          self.assertEqual(len(a_       ),       1       )
          self.assertEqual(len(a_       ),       1       )
          self.assertEqual(token_a[0],       a_       )
          self.assertEqual(token_a[0],       a_       )







import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     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							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 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							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						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							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
