



'''simple docstring'''






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class     SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase      ):







	"""simple docstring"""






	lowerCamelCase__      			=	StableDiffusionControlNetInpaintPipeline
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowerCamelCase__      			=	frozenset({"""control_image"""}      )  # skip `image` and `mask` for now, only test for control_image
	lowerCamelCase__      			=	TEXT_TO_IMAGE_IMAGE_PARAMS







	def 					A     (						self      :					int		)      ->		str:
								torch.manual_seed(0		)
								UpperCAmelCase			:	str										=		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=9		,      out_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,      cross_attention_dim=32		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	List[str]										=		ControlNetModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Dict										=		DDIMScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		,      clip_sample=__snake_case		,      set_alpha_to_one=__snake_case		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	List[Any]										=		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		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Any										=		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			:	List[Any]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	List[str]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase			:	Dict										=		{
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def 					A     (						self      :					Any		,      __snake_case      :					List[Any]		,      __snake_case      :					List[str]=0		)      ->		int:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase			:	str										=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase			:	List[str]										=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		2
								UpperCAmelCase			:	Optional[int]										=		randn_tensor(
								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=__snake_case		,      device=torch.device(__snake_case		)		,      )
								UpperCAmelCase			:	Any										=		floats_tensor((1, 3, 32, 32)		,      rng=random.Random(__snake_case		)		).to(__snake_case		)
								UpperCAmelCase			:	Dict										=		init_image.cpu().permute(0		,      2		,      3		,      1		)[0]

								UpperCAmelCase			:	List[str]										=		Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((64, 64)		)
								UpperCAmelCase			:	str										=		Image.fromarray(np.uinta(init_image + 4		)		).convert('''RGB'''		).resize((64, 64)		)

								UpperCAmelCase			:	Dict										=		{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''mask_image''': mask_image,
								    '''control_image''': control_image,
								}

								return inputs







	def 					A     (						self      :					Dict		)      ->		Optional[int]:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)







	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()		,      reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,      )
	def 					A     (						self      :					Optional[int]		)      ->		Any:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







	def 					A     (						self      :					Any		)      ->		List[Any]:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	StableDiffusionControlNetInpaintPipeline
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowerCamelCase__      			=	frozenset([]      )







	def 					A     (						self      :					Any		)      ->		str:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Dict										=		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		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[int]										=		ControlNetModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	str										=		DDIMScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		,      clip_sample=__snake_case		,      set_alpha_to_one=__snake_case		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Tuple										=		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		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[int]										=		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			:	Tuple										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase			:	str										=		{
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components

class     SCREAMING_SNAKE_CASE(							A__							,		A__							,		unittest.TestCase      ):







	"""simple docstring"""






	lowerCamelCase__      			=	StableDiffusionControlNetInpaintPipeline
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	lowerCamelCase__      			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







	def 					A     (						self      :					Union[str, Any]		)      ->		Optional[Any]:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Dict										=		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=9		,      out_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,      cross_attention_dim=32		,      )
								torch.manual_seed(0		)

								def init_weights(__snake_case      :					str		):
															if isinstance(__snake_case		,      torch.nn.Convad		):
																						torch.nn.init.normal(m.weight		)
																						m.bias.data.fill_(1.0		)

								UpperCAmelCase			:	Tuple										=		ControlNetModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
								controlneta.controlnet_down_blocks.apply(__snake_case		)

								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		ControlNetModel(
								    block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
								controlneta.controlnet_down_blocks.apply(__snake_case		)

								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		DDIMScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		,      clip_sample=__snake_case		,      set_alpha_to_one=__snake_case		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	Any										=		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		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	str										=		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			:	List[Any]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	int										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase			:	Any										=		MultiControlNetModel([controlneta, controlneta]		)

								UpperCAmelCase			:	Union[str, Any]										=		{
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					Tuple		,      __snake_case      :					Optional[int]=0		)      ->		List[str]:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase			:	Any										=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase			:	Optional[Any]										=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)

								UpperCAmelCase			:	Any										=		2

								UpperCAmelCase			:	Optional[Any]										=		[
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=__snake_case		,      device=torch.device(__snake_case		)		,      ),
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=__snake_case		,      device=torch.device(__snake_case		)		,      ),
								]
								UpperCAmelCase			:	Optional[Any]										=		floats_tensor((1, 3, 32, 32)		,      rng=random.Random(__snake_case		)		).to(__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		init_image.cpu().permute(0		,      2		,      3		,      1		)[0]

								UpperCAmelCase			:	Dict										=		Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((64, 64)		)
								UpperCAmelCase			:	Optional[Any]										=		Image.fromarray(np.uinta(init_image + 4		)		).convert('''RGB'''		).resize((64, 64)		)

								UpperCAmelCase			:	Any										=		{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''mask_image''': mask_image,
								    '''control_image''': control_image,
								}

								return inputs







	def 					A     (						self      :					Dict		)      ->		str:
								UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_components()
								UpperCAmelCase			:	List[str]										=		self.pipeline_class(**__snake_case		)
								pipe.to(__snake_case		)

								UpperCAmelCase			:	Tuple										=		10.0
								UpperCAmelCase			:	List[Any]										=		4

								UpperCAmelCase			:	Dict										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	str										=		steps
								UpperCAmelCase			:	Tuple										=		scale
								UpperCAmelCase			:	Optional[Any]										=		pipe(**__snake_case		)[0]

								UpperCAmelCase			:	List[str]										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		steps
								UpperCAmelCase			:	Dict										=		scale
								UpperCAmelCase			:	Any										=		pipe(**__snake_case		,      control_guidance_start=0.1		,      control_guidance_end=0.2		)[0]

								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	Optional[int]										=		steps
								UpperCAmelCase			:	Union[str, Any]										=		scale
								UpperCAmelCase			:	Optional[Any]										=		pipe(**__snake_case		,      control_guidance_start=[0.1, 0.3]		,      control_guidance_end=[0.2, 0.7]		)[0]

								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	int										=		steps
								UpperCAmelCase			:	str										=		scale
								UpperCAmelCase			:	Union[str, Any]										=		pipe(**__snake_case		,      control_guidance_start=0.4		,      control_guidance_end=[0.5, 0.8]		)[0]

								# make sure that all outputs are different
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3







	def 					A     (						self      :					Optional[Any]		)      ->		Any:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)







	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()		,      reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,      )
	def 					A     (						self      :					List[Any]		)      ->		int:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







	def 					A     (						self      :					Optional[int]		)      ->		Optional[Any]:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







	def 					A     (						self      :					Tuple		)      ->		str:
								UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	List[str]										=		self.pipeline_class(**__snake_case		)
								pipe.to(__snake_case		)
								pipe.set_progress_bar_config(disable=__snake_case		)
								with tempfile.TemporaryDirectory() as tmpdir:
															try:
																						# save_pretrained is not implemented for Multi-ControlNet
																						pipe.save_pretrained(__snake_case		)
															except NotImplementedError:
																						pass

@slow
@require_torch_gpu
class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					Optional[int]		)      ->		Tuple:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def 					A     (						self      :					Dict		)      ->		str:
								UpperCAmelCase			:	List[str]										=		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

								UpperCAmelCase			:	List[str]										=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,      safety_checker=__snake_case		,      controlnet=__snake_case		)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		torch.Generator(device='''cpu'''		).manual_seed(0		)
								UpperCAmelCase			:	str										=		load_image(
								    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''		).resize((512, 512)		)

								UpperCAmelCase			:	List[str]										=		load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
								    '''/stable_diffusion_inpaint/input_bench_mask.png'''		).resize((512, 512)		)

								UpperCAmelCase			:	List[Any]										=		'''pitch black hole'''

								UpperCAmelCase			:	Any										=		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''		).resize((512, 512)		)

								UpperCAmelCase			:	List[Any]										=		pipe(
								    __snake_case		,      image=__snake_case		,      mask_image=__snake_case		,      control_image=__snake_case		,      generator=__snake_case		,      output_type='''np'''		,      num_inference_steps=3		,      )

								UpperCAmelCase			:	int										=		output.images[0]

								assert image.shape == (512, 512, 3)

								UpperCAmelCase			:	int										=		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''		)

								assert np.abs(expected_image - image		).max() < 9E-2







	def 					A     (						self      :					List[str]		)      ->		List[str]:
								UpperCAmelCase			:	Optional[int]										=		ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''		)

								UpperCAmelCase			:	List[Any]										=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''		,      safety_checker=__snake_case		,      controlnet=__snake_case		)
								UpperCAmelCase			:	str										=		DDIMScheduler.from_config(pipe.scheduler.config		)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	List[Any]										=		torch.Generator(device='''cpu'''		).manual_seed(33		)

								UpperCAmelCase			:	Tuple										=		load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''		)
								UpperCAmelCase			:	Dict										=		init_image.resize((512, 512)		)

								UpperCAmelCase			:	str										=		load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''		)
								UpperCAmelCase			:	str										=		mask_image.resize((512, 512)		)

								UpperCAmelCase			:	Union[str, Any]										=		'''a handsome man with ray-ban sunglasses'''

								def make_inpaint_condition(__snake_case      :					Optional[Any]		,      __snake_case      :					str		):
															UpperCAmelCase			:	int										=		np.array(image.convert('''RGB'''		)		).astype(np.floataa		) / 2_55.0
															UpperCAmelCase			:	Tuple										=		np.array(image_mask.convert('''L'''		)		).astype(np.floataa		) / 2_55.0

															assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
															UpperCAmelCase			:	int										=		-1.0  # set as masked pixel
															UpperCAmelCase			:	Any										=		np.expand_dims(__snake_case		,      0		).transpose(0		,      3		,      1		,      2		)
															UpperCAmelCase			:	List[Any]										=		torch.from_numpy(__snake_case		)
															return image

								UpperCAmelCase			:	List[Any]										=		make_inpaint_condition(__snake_case		,      __snake_case		)

								UpperCAmelCase			:	Any										=		pipe(
								    __snake_case		,      image=__snake_case		,      mask_image=__snake_case		,      control_image=__snake_case		,      guidance_scale=9.0		,      eta=1.0		,      generator=__snake_case		,      num_inference_steps=20		,      output_type='''np'''		,      )
								UpperCAmelCase			:	List[str]										=		output.images[0]

								assert image.shape == (512, 512, 3)

								UpperCAmelCase			:	Optional[int]										=		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''		)

								assert np.abs(expected_image - image		).max() < 9E-2










'''simple docstring'''






import importlib.metadata
import subprocess
import sys


def 			snake_case_					( _lowerCAmelCase  :						bool = False  )				->       str:
							UpperCAmelCase			:	List[str]										=		False
							if "IPython" in sys.modules:
														UpperCAmelCase			:	Optional[Any]										=		'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()  )

							if in_colab:
														if upgrade:
																					UpperCAmelCase			:	List[Any]										=		['''pip''', '''install''', '''-U''', '''torch''']
																					subprocess.run(_lowerCAmelCase       ,				check=_lowerCAmelCase  )
														# get the current version of torch
														UpperCAmelCase			:	Dict										=		importlib.metadata.version('''torch'''  )
														UpperCAmelCase			:	int										=		torch_version[: torch_version.rindex('''.'''  )]
														UpperCAmelCase			:	Any										=		f"""https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl"""
														UpperCAmelCase			:	List[str]										=		['''pip''', '''install''', xla_wheel]
														subprocess.run(_lowerCAmelCase       ,				check=_lowerCAmelCase  )
							else:
														raise RuntimeError('''`install_xla` utility works only on google colab.'''  )





