


"""simple docstring"""






import numpy as np



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      str	)							->	Any:

	'''simple docstring'''


	snake_case__  :						List[str]		=      n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	snake_case__  :						Union[str, Any]		=      np.floor(__lowerCamelCase	)
	# we add samples according to how much "left over" probability
	# they had, until we arrive at n_samples
	snake_case__  :						Optional[int]		=      int(n_draws - floored.sum()	)
	if need_to_add > 0:
		snake_case__  :						str		=      continuous - floored
		snake_case__  :						Dict		=      np.sort(np.unique(__lowerCamelCase	)	)[::-1]
		# add according to remainder, but break ties
		# randomly to avoid biases
		for value in values:
			((snake_case__     ),					     )  :						List[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
			snake_case__  :						List[Any]		=      min(len(__lowerCamelCase	)				,    __lowerCamelCase	)
			snake_case__  :						Optional[int]		=      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__		(					_UpperCAmelCase    :      int				,    _UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      Tuple				,    _UpperCAmelCase    :      Tuple				,    _UpperCAmelCase    :      int=10	)							->	Dict:

	'''simple docstring'''


	snake_case__,					snake_case__  :						Any		=      np.unique(__lowerCamelCase				,    return_inverse=__lowerCamelCase	)
	snake_case__  :						int		=      classes.shape[0]
	snake_case__  :						str		=      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)	)
	snake_case__  :						Tuple		=      np.split(np.argsort(__lowerCamelCase				,    kind="mergesort"	)				,    np.cumsum(__lowerCamelCase	)[:-1]	)
	for _ in range(__lowerCamelCase	):
		snake_case__  :						Tuple		=      approximate_mode(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase	)
		snake_case__  :						Tuple		=      class_counts - n_i
		snake_case__  :						List[str]		=      approximate_mode(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase	)

		snake_case__  :						List[str]		=      []
		snake_case__  :						str		=      []

		for i in range(__lowerCamelCase	):
			snake_case__  :						List[Any]		=      rng.permutation(class_counts[i]	)
			snake_case__  :						List[Any]		=      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]]	)
		snake_case__  :						str		=      rng.permutation(__lowerCamelCase	)
		snake_case__  :						int		=      rng.permutation(__lowerCamelCase	)

		yield train, test













"""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     A_				(   unittest.TestCase	):
				UpperCAmelCase__										=					EsmTokenizer







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										super().setUp()
										__magic_name__      =  tempfile.mkdtemp()
										# fmt: off
										__magic_name__      =  ["<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
										__magic_name__      =  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      _snake_case       (				self				:    Optional[Any]    ,     **__lowerCamelCase				:    str						)					->       List[PreTrainedTokenizerBase]:
										return [self.get_tokenizer(**__lowerCamelCase						)]







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    int						)					->       PreTrainedTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

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







				def      _snake_case       (				self				:    int						)					->       Dict:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

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







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizers(fast=__lowerCamelCase						)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																						__magic_name__      =  "<unk>"
																						__magic_name__      =  "<mask>"

																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)
																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)

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



