




'''simple docstring'''







import numpy as np






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_=10  ):



		'''simple docstring'''







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

				_snake_case     	=	[]
				_snake_case     	=	[]

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

				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 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					lowercase__   =			EsmTokenizer






					def     __SCREAMING_SNAKE_CASE     (   self			):
							super().setUp()
							_snake_case     	=	tempfile.mkdtemp()
							# fmt: off
							_snake_case     	=	["<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
							_snake_case     	=	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     __SCREAMING_SNAKE_CASE     (   self,  **__UpperCamelCase			):
							return [self.get_tokenizer(**__UpperCamelCase			)]






					def     __SCREAMING_SNAKE_CASE     (   self,  **__UpperCamelCase			):
							return self.tokenizer_class.from_pretrained(self.tmpdirname,  **__UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.tokenizer_class(self.vocab_file			)

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






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.tokenizer_class(self.vocab_file			)

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






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.tokenizer_class(self.vocab_file			)

							_snake_case     	=	["LAGVS", "WCB"]
							_snake_case     	=	tokenizer(__UpperCamelCase,  padding=__UpperCamelCase			)["input_ids"]

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






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.tokenizer_class(self.vocab_file			)

							_snake_case     	=	["LAGVS", "WCB"]
							_snake_case     	=	tokenizer(__UpperCamelCase,  padding=__UpperCamelCase			)["input_ids"]

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






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

											_snake_case     	=	tokenizer.tokenize(__UpperCamelCase			)
											_snake_case     	=	tokenizer.tokenize(__UpperCamelCase			)

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





