






import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def __A       ( __lowerCAmelCase      )->    Any:





						"""simple docstring"""



						_UpperCAmelCase							   =     {}
						for name in unet.attn_processors.keys():
												_UpperCAmelCase							   =     None if name.endswith('attn1.processor'      ) else unet.config.cross_attention_dim
												if name.startswith('mid_block'      ):
																		_UpperCAmelCase							   =     unet.config.block_out_channels[-1]
												elif name.startswith('up_blocks'      ):
																		_UpperCAmelCase							   =     int(name[len('up_blocks.'      )]      )
																		_UpperCAmelCase							   =     list(reversed(unet.config.block_out_channels      )      )[block_id]
												elif name.startswith('down_blocks'      ):
																		_UpperCAmelCase							   =     int(name[len('down_blocks.'      )]      )
																		_UpperCAmelCase							   =     unet.config.block_out_channels[block_id]
												_UpperCAmelCase							   =     (
												    LoRAAttnProcessor2_0 if hasattr(__lowerCAmelCase					,						'scaled_dot_product_attention'      ) else LoRAAttnProcessor
												)
												_UpperCAmelCase							   =     lora_attn_processor_class(
												    hidden_size=__lowerCAmelCase					,						cross_attention_dim=__lowerCAmelCase      )
						_UpperCAmelCase							   =     AttnProcsLayers(__lowerCAmelCase      )
						return lora_attn_procs, unet_lora_layers


def __A       ( __lowerCAmelCase      )->    Any:





						"""simple docstring"""



						_UpperCAmelCase							   =     {}
						_UpperCAmelCase							   =     (
						    LoRAAttnProcessor2_0 if hasattr(__lowerCAmelCase					,						'scaled_dot_product_attention'      ) else LoRAAttnProcessor
						)
						for name, module in text_encoder.named_modules():
												if name.endswith(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     lora_attn_processor_class(
																		    hidden_size=module.out_proj.out_features					,						cross_attention_dim=__lowerCAmelCase      )
						return text_lora_attn_procs


def __A       ( __lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     create_text_encoder_lora_attn_procs(__lowerCAmelCase      )
						_UpperCAmelCase							   =     AttnProcsLayers(__lowerCAmelCase      )
						return text_encoder_lora_layers


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase=False      )->    Dict:





						"""simple docstring"""



						for _, attn_proc in text_lora_attn_procs.items():
												# set up.weights
												for layer_name, layer_module in attn_proc.named_modules():
																		if layer_name.endswith('_lora'      ):
																								_UpperCAmelCase							   =     (
																								    torch.randn_like(layer_module.up.weight      )
																								    if randn_weight
																								    else torch.zeros_like(layer_module.up.weight      )
																								)
																								_UpperCAmelCase							   =     torch.nn.Parameter(__lowerCAmelCase      )





class __lowerCamelCase					( unittest.TestCase):





	"""simple docstring"""






	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							torch.manual_seed(0       )
							_UpperCAmelCase							   =     UNetaDConditionModel(
							    block_out_channels=(32, 64)			,       layers_per_block=2			,       sample_size=32			,       in_channels=4			,       out_channels=4			,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,       cross_attention_dim=32			,       )
							_UpperCAmelCase							   =     DDIMScheduler(
							    beta_start=0.0_00_85			,       beta_end=0.0_12			,       beta_schedule='scaled_linear'			,       clip_sample=UpperCAmelCase			,       set_alpha_to_one=UpperCAmelCase			,       steps_offset=1			,       )
							torch.manual_seed(0       )
							_UpperCAmelCase							   =     AutoencoderKL(
							    block_out_channels=[32, 64]			,       in_channels=3			,       out_channels=3			,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,       latent_channels=4			,       )
							_UpperCAmelCase							   =     CLIPTextConfig(
							    bos_token_id=0			,       eos_token_id=2			,       hidden_size=32			,       intermediate_size=37			,       layer_norm_eps=1e-05			,       num_attention_heads=4			,       num_hidden_layers=5			,       pad_token_id=1			,       vocab_size=1000			,       )
							_UpperCAmelCase							   =     CLIPTextModel(UpperCAmelCase       )
							_UpperCAmelCase							   =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

							_UpperCAmelCase   ,					_UpperCAmelCase							   =     create_unet_lora_layers(UpperCAmelCase       )
							_UpperCAmelCase							   =     create_text_encoder_lora_layers(UpperCAmelCase       )

							_UpperCAmelCase							   =     {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							_UpperCAmelCase							   =     {
							    'unet_lora_layers': unet_lora_layers,
							    'text_encoder_lora_layers': text_encoder_lora_layers,
							    'unet_lora_attn_procs': unet_lora_attn_procs,
							}
							return pipeline_components, lora_components

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     1
							_UpperCAmelCase							   =     10
							_UpperCAmelCase							   =     4
							_UpperCAmelCase							   =     (32, 32)

							_UpperCAmelCase							   =     torch.manual_seed(0       )
							_UpperCAmelCase							   =     floats_tensor((batch_size, num_channels) + sizes       )
							_UpperCAmelCase							   =     torch.randint(1			,       UpperCAmelCase			,       size=(batch_size, sequence_length)			,       generator=UpperCAmelCase       )

							_UpperCAmelCase							   =     {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}

							return noise, input_ids, pipeline_inputs

							# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     77

							_UpperCAmelCase							   =     torch.randint(2			,       56			,       size=(1, max_seq_length)			,       generator=torch.manual_seed(0       )       )

							_UpperCAmelCase							   =     {}
							_UpperCAmelCase							   =     inputs
							return prepared_inputs

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							LoraLoaderMixin.save_lora_weights(
							    save_directory=UpperCAmelCase			,       unet_lora_layers=lora_components['unet_lora_layers']			,       text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,       )
							self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.bin'       )       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
							_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_inputs()

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     original_images[0, -3:, -3:, -1]

							with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=UpperCAmelCase			,       unet_lora_layers=lora_components['unet_lora_layers']			,       text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,       )
													self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.bin'       )       )       )
													sd_pipe.load_lora_weights(UpperCAmelCase       )

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     lora_images[0, -3:, -3:, -1]

							# Outputs shouldn't match.
							self.assertFalse(torch.allclose(torch.from_numpy(UpperCAmelCase       )			,       torch.from_numpy(UpperCAmelCase       )       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
							_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_inputs()

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     original_images[0, -3:, -3:, -1]

							with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=UpperCAmelCase			,       unet_lora_layers=lora_components['unet_lora_layers']			,       text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,       safe_serialization=UpperCAmelCase			,       )
													self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.safetensors'       )       )       )
													sd_pipe.load_lora_weights(UpperCAmelCase       )

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     lora_images[0, -3:, -3:, -1]

							# Outputs shouldn't match.
							self.assertFalse(torch.allclose(torch.from_numpy(UpperCAmelCase       )			,       torch.from_numpy(UpperCAmelCase       )       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     lora_components['unet_lora_attn_procs']
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
							_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_inputs()

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     original_images[0, -3:, -3:, -1]

							with tempfile.TemporaryDirectory() as tmpdirname:
													_UpperCAmelCase							   =     sd_pipe.unet
													unet.set_attn_processor(UpperCAmelCase       )
													unet.save_attn_procs(UpperCAmelCase       )
													self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.bin'       )       )       )
													sd_pipe.load_lora_weights(UpperCAmelCase       )

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     lora_images[0, -3:, -3:, -1]

							# Outputs shouldn't match.
							self.assertFalse(torch.allclose(torch.from_numpy(UpperCAmelCase       )			,       torch.from_numpy(UpperCAmelCase       )       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )

							_UpperCAmelCase							   =     self.get_dummy_tokens()

							# inference without lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_without_lora.shape == (1, 77, 32)

							# create lora_attn_procs with zeroed out up.weights
							_UpperCAmelCase							   =     create_text_encoder_lora_attn_procs(pipe.text_encoder       )
							set_lora_up_weights(UpperCAmelCase			,       randn_weight=UpperCAmelCase       )

							# monkey patch
							pipe._modify_text_encoder(UpperCAmelCase       )

							# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
							del text_attn_procs
							gc.collect()

							# inference with lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_with_lora.shape == (1, 77, 32)

							assert torch.allclose(
							    UpperCAmelCase			,       UpperCAmelCase       ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

							# create lora_attn_procs with randn up.weights
							_UpperCAmelCase							   =     create_text_encoder_lora_attn_procs(pipe.text_encoder       )
							set_lora_up_weights(UpperCAmelCase			,       randn_weight=UpperCAmelCase       )

							# monkey patch
							pipe._modify_text_encoder(UpperCAmelCase       )

							# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
							del text_attn_procs
							gc.collect()

							# inference with lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_with_lora.shape == (1, 77, 32)

							assert not torch.allclose(
							    UpperCAmelCase			,       UpperCAmelCase       ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )

							_UpperCAmelCase							   =     self.get_dummy_tokens()

							# inference without lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_without_lora.shape == (1, 77, 32)

							# create lora_attn_procs with randn up.weights
							_UpperCAmelCase							   =     create_text_encoder_lora_attn_procs(pipe.text_encoder       )
							set_lora_up_weights(UpperCAmelCase			,       randn_weight=UpperCAmelCase       )

							# monkey patch
							pipe._modify_text_encoder(UpperCAmelCase       )

							# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
							del text_attn_procs
							gc.collect()

							# inference with lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_with_lora.shape == (1, 77, 32)

							assert not torch.allclose(
							    UpperCAmelCase			,       UpperCAmelCase       ), "lora outputs should be different to without lora outputs"

							# remove monkey patch
							pipe._remove_text_encoder_monkey_patch()

							# inference with removed lora
							_UpperCAmelCase							   =     pipe.text_encoder(**UpperCAmelCase       )[0]
							assert outputs_without_lora_removed.shape == (1, 77, 32)

							assert torch.allclose(
							    UpperCAmelCase			,       UpperCAmelCase       ), "remove lora monkey patch should restore the original outputs"

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
							_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=UpperCAmelCase			,       unet_lora_layers=lora_components['unet_lora_layers']			,       text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,       )
													self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.bin'       )       )       )
													sd_pipe.load_lora_weights(UpperCAmelCase       )

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     lora_images[0, -3:, -3:, -1]

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase			,       cross_attention_kwargs={'scale': 0.5}       ).images
							_UpperCAmelCase							   =     lora_images_with_scale[0, -3:, -3:, -1]

							# Outputs shouldn't match.
							self.assertFalse(
							    torch.allclose(torch.from_numpy(UpperCAmelCase       )			,       torch.from_numpy(UpperCAmelCase       )       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(UpperCAmelCase       )

													_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
													_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
													_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
													sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

													# check if vanilla attention processors are used
													for _, module in sd_pipe.unet.named_modules():
																			if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
																									self.assertIsInstance(module.processor			,       (AttnProcessor, AttnProcessor2_0)       )

            # load LoRA weight file
													sd_pipe.load_lora_weights(UpperCAmelCase       )

													# check if lora attention processors are used
													for _, module in sd_pipe.unet.named_modules():
																			if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
																									_UpperCAmelCase							   =     (
																									    LoRAAttnProcessor2_0 if hasattr(UpperCAmelCase			,       'scaled_dot_product_attention'       ) else LoRAAttnProcessor
																									)
																									self.assertIsInstance(module.processor			,       UpperCAmelCase       )

	@unittest.skipIf(torch_device != 'cuda'			,       'This test is supposed to run on GPU'       )
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(UpperCAmelCase       )

													_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
													_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
													_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
													sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

													# enable XFormers
													sd_pipe.enable_xformers_memory_efficient_attention()

													# check if xFormers attention processors are used
													for _, module in sd_pipe.unet.named_modules():
																			if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
																									self.assertIsInstance(module.processor			,       UpperCAmelCase       )

            # load LoRA weight file
													sd_pipe.load_lora_weights(UpperCAmelCase       )

													# check if lora attention processors are used
													for _, module in sd_pipe.unet.named_modules():
																			if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
																									self.assertIsInstance(module.processor			,       UpperCAmelCase       )

	@unittest.skipIf(torch_device != 'cuda'			,       'This test is supposed to run on GPU'       )
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_components()
							_UpperCAmelCase							   =     StableDiffusionPipeline(**UpperCAmelCase       )
							_UpperCAmelCase							   =     sd_pipe.to(UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_dummy_inputs()

							# enable XFormers
							sd_pipe.enable_xformers_memory_efficient_attention()

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     original_images[0, -3:, -3:, -1]

							with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=UpperCAmelCase			,       unet_lora_layers=lora_components['unet_lora_layers']			,       text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,       )
													self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase			,       'pytorch_lora_weights.bin'       )       )       )
													sd_pipe.load_lora_weights(UpperCAmelCase       )

							_UpperCAmelCase							   =     sd_pipe(**UpperCAmelCase       ).images
							_UpperCAmelCase							   =     lora_images[0, -3:, -3:, -1]

							# Outputs shouldn't match.
							self.assertFalse(torch.allclose(torch.from_numpy(UpperCAmelCase       )			,       torch.from_numpy(UpperCAmelCase       )       )       )








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
