





'''simple docstring'''

import numpy as np


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

				__UpperCAmelCase     :				Optional[Any]					   =			[]
				__UpperCAmelCase     :				Optional[int]					   =			[]

				for i in range(_UpperCAmelCase   ):
						__UpperCAmelCase     :				Union[str, Any]					   =			rng.permutation(class_counts[i]   )
						__UpperCAmelCase     :				List[str]					   =			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]]   )
				__UpperCAmelCase     :				Any					   =			rng.permutation(_UpperCAmelCase   )
				__UpperCAmelCase     :				Tuple					   =			rng.permutation(_UpperCAmelCase   )

				yield train, test










'''simple docstring'''


import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image

class 			A    (		unittest.TestCase     ,_a    ):







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Optional[int]:


														"""simple docstring"""


														_a	     =				load_tool('''image-question-answering'''					)
														self.tool.setup()
														_a	     =				load_tool('''image-question-answering'''							,			remote=lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Optional[int]:


														"""simple docstring"""


														_a	     =				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''					)					) / '''000000039769.png'''					)
														_a	     =				self.tool(lowerCAmelCase_							,			'''How many cats are sleeping on the couch?'''					)
														self.assertEqual(lowerCAmelCase_							,			'''2'''					)







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''					)					) / '''000000039769.png'''					)
														_a	     =				self.remote_tool(lowerCAmelCase_							,			'''How many cats are sleeping on the couch?'''					)
														self.assertEqual(lowerCAmelCase_							,			'''2'''					)







							def    __lowerCAmelCase			(  self       :  Dict					)			->							Tuple:


														"""simple docstring"""


														_a	     =				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''					)					) / '''000000039769.png'''					)
														_a	     =				self.tool(image=lowerCAmelCase_							,			question='''How many cats are sleeping on the couch?'''					)
														self.assertEqual(lowerCAmelCase_							,			'''2'''					)







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Dict:


														"""simple docstring"""


														_a	     =				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''					)					) / '''000000039769.png'''					)
														_a	     =				self.remote_tool(image=lowerCAmelCase_							,			question='''How many cats are sleeping on the couch?'''					)
														self.assertEqual(lowerCAmelCase_							,			'''2'''					)

