



'''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.'''  )










'''simple docstring'''






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 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       Tuple:
							UpperCAmelCase			:	Any										=		{}
							for name in unet.attn_processors.keys():
														UpperCAmelCase			:	Optional[Any]										=		None if name.endswith('''attn1.processor'''  ) else unet.config.cross_attention_dim
														if name.startswith('''mid_block'''  ):
																					UpperCAmelCase			:	List[Any]										=		unet.config.block_out_channels[-1]
														elif name.startswith('''up_blocks'''  ):
																					UpperCAmelCase			:	Union[str, Any]										=		int(name[len('''up_blocks.'''  )]  )
																					UpperCAmelCase			:	Optional[int]										=		list(reversed(unet.config.block_out_channels  )  )[block_id]
														elif name.startswith('''down_blocks'''  ):
																					UpperCAmelCase			:	str										=		int(name[len('''down_blocks.'''  )]  )
																					UpperCAmelCase			:	str										=		unet.config.block_out_channels[block_id]
														UpperCAmelCase			:	Tuple										=		(
														    LoRAAttnProcessor2_0 if hasattr(_lowerCAmelCase       ,				'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
														)
														UpperCAmelCase			:	Optional[Any]										=		lora_attn_processor_class(
														    hidden_size=_lowerCAmelCase       ,				cross_attention_dim=_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[Any]										=		AttnProcsLayers(_lowerCAmelCase  )
							return lora_attn_procs, unet_lora_layers


def 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       int:
							UpperCAmelCase			:	Any										=		{}
							UpperCAmelCase			:	List[str]										=		(
							    LoRAAttnProcessor2_0 if hasattr(_lowerCAmelCase       ,				'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
							)
							for name, module in text_encoder.named_modules():
														if name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase			:	List[Any]										=		lora_attn_processor_class(
																					    hidden_size=module.out_proj.out_features       ,				cross_attention_dim=_lowerCAmelCase  )
							return text_lora_attn_procs


def 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       Optional[Any]:
							UpperCAmelCase			:	Dict										=		create_text_encoder_lora_attn_procs(_lowerCAmelCase  )
							UpperCAmelCase			:	Union[str, Any]										=		AttnProcsLayers(_lowerCAmelCase  )
							return text_encoder_lora_layers


def 			snake_case_					( _lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[int]=False  )				->       int:
							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			:	List[Any]										=		(
																												    torch.randn_like(layer_module.up.weight  )
																												    if randn_weight
																												    else torch.zeros_like(layer_module.up.weight  )
																												)
																												UpperCAmelCase			:	Optional[Any]										=		torch.nn.Parameter(_lowerCAmelCase  )




class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					str		)      ->		Dict:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[int]										=		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			:	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		,      steps_offset=1		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	str										=		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			:	Union[str, 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			:	Union[str, Any]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	List[str]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		create_unet_lora_layers(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		create_text_encoder_lora_layers(__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								UpperCAmelCase			:	Dict										=		{
								    '''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 					A     (						self      :					Union[str, Any]		)      ->		Tuple:
								UpperCAmelCase			:	Optional[int]										=		1
								UpperCAmelCase			:	str										=		10
								UpperCAmelCase			:	int										=		4
								UpperCAmelCase			:	Tuple										=		(32, 32)

								UpperCAmelCase			:	Tuple										=		torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		floats_tensor((batch_size, num_channels) + sizes		)
								UpperCAmelCase			:	Tuple										=		torch.randint(1		,      __snake_case		,      size=(batch_size, sequence_length)		,      generator=__snake_case		)

								UpperCAmelCase			:	int										=		{
								    '''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 					A     (						self      :					str		)      ->		Tuple:
								UpperCAmelCase			:	Tuple										=		77

								UpperCAmelCase			:	Any										=		torch.randint(2		,      56		,      size=(1, max_seq_length)		,      generator=torch.manual_seed(0		)		)

								UpperCAmelCase			:	Union[str, Any]										=		{}
								UpperCAmelCase			:	Union[str, Any]										=		inputs
								return prepared_inputs







	def 					A     (						self      :					Any		,      __snake_case      :					Dict		)      ->		Union[str, Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_components()
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__snake_case		,      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(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)







	def 					A     (						self      :					List[str]		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Dict										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		original_images[0, -3:, -3:, -1]

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      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(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	str										=		lora_images[0, -3:, -3:, -1]

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







	def 					A     (						self      :					Any		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[Any]										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Optional[int]										=		self.get_dummy_inputs()

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

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

								UpperCAmelCase			:	Any										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		lora_images[0, -3:, -3:, -1]

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







	def 					A     (						self      :					List[str]		)      ->		int:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[int]										=		lora_components['''unet_lora_attn_procs''']
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								UpperCAmelCase			:	Tuple										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		original_images[0, -3:, -3:, -1]

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

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

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







	def 					A     (						self      :					Optional[int]		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)

								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# 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			:	Any										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert torch.allclose(
								    __snake_case		,      __snake_case		), "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			:	List[Any]										=		create_text_encoder_lora_attn_procs(pipe.text_encoder		)
								set_lora_up_weights(__snake_case		,      randn_weight=__snake_case		)

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# 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			:	Optional[int]										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

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







	def 					A     (						self      :					Optional[int]		)      ->		str:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[int]										=		StableDiffusionPipeline(**__snake_case		)

								UpperCAmelCase			:	Tuple										=		self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# 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			:	int										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

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

								# remove monkey patch
								pipe._remove_text_encoder_monkey_patch()

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

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







	def 					A     (						self      :					str		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Tuple										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      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(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	str										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Union[str, Any]										=		lora_images[0, -3:, -3:, -1]

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

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







	def 					A     (						self      :					Tuple		)      ->		Dict:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
															UpperCAmelCase			:	Tuple										=		StableDiffusionPipeline(**__snake_case		)
															UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
															sd_pipe.set_progress_bar_config(disable=__snake_case		)

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

            # load LoRA weight file
															sd_pipe.load_lora_weights(__snake_case		)

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







	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''		)
	def 					A     (						self      :					List[str]		)      ->		Optional[Any]:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
															UpperCAmelCase			:	List[str]										=		StableDiffusionPipeline(**__snake_case		)
															UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
															sd_pipe.set_progress_bar_config(disable=__snake_case		)

															# 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(__snake_case		,      __snake_case		):
																													self.assertIsInstance(module.processor		,      __snake_case		)

            # load LoRA weight file
															sd_pipe.load_lora_weights(__snake_case		)

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







	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''		)
	def 					A     (						self      :					List[Any]		)      ->		Any:
								UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
								UpperCAmelCase			:	str										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      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(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		lora_images[0, -3:, -3:, -1]

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





