





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





def    _lowerCAmelCase						(						lowercase	)					->					List[Any]:
			__lowerCAmelCase											=				{}
			__lowerCAmelCase											=				(
			    LoRAAttnProcessor2_0 if hasattr(lowercase					,	"""scaled_dot_product_attention"""	) else LoRAAttnProcessor
			)
			for name, module in text_encoder.named_modules():
						if name.endswith(lowercase	):
									__lowerCAmelCase											=				lora_attn_processor_class(
									    hidden_size=module.out_proj.out_features					,	cross_attention_dim=lowercase	)
			return text_lora_attn_procs





def    _lowerCAmelCase						(						lowercase	)					->					Tuple:
			__lowerCAmelCase											=				create_text_encoder_lora_attn_procs(lowercase	)
			__lowerCAmelCase											=				AttnProcsLayers(lowercase	)
			return text_encoder_lora_layers





def    _lowerCAmelCase						(						lowercase					,	lowercase=False	)					->					Optional[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"""	):
												__lowerCAmelCase											=				(
												    torch.randn_like(layer_module.up.weight	)
												    if randn_weight
												    else torch.zeros_like(layer_module.up.weight	)
												)
												__lowerCAmelCase											=				torch.nn.Parameter(lowercase	)







class     _UpperCAmelCase							(						unittest.TestCase   ):
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					torch.manual_seed(0			)
					__lowerCAmelCase											=				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,)
					__lowerCAmelCase											=				DDIMScheduler(
					    beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,steps_offset=1,)
					torch.manual_seed(0			)
					__lowerCAmelCase											=				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,)
					__lowerCAmelCase											=				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=10_00,)
					__lowerCAmelCase											=				CLIPTextModel(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

					__lowerCAmelCase						,       __lowerCAmelCase											=				create_unet_lora_layers(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				create_text_encoder_lora_layers(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """safety_checker""": None,
					    """feature_extractor""": None,
					}
					__lowerCAmelCase											=				{
					    """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   lowerCamelCase__			(  self			):



					'''simple docstring'''

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

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

					__lowerCAmelCase											=				{
					    """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   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				77

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

					__lowerCAmelCase											=				{}
					__lowerCAmelCase											=				inputs
					return prepared_inputs
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__SCREAMING_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(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""			)			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_inputs()

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

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_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(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""			)			)			)
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE			)

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

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE			),torch.from_numpy(__SCREAMING_SNAKE_CASE			)			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_inputs()

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

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

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

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE			),torch.from_numpy(__SCREAMING_SNAKE_CASE			)			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				lora_components["""unet_lora_attn_procs"""]
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_inputs()

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

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

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

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE			),torch.from_numpy(__SCREAMING_SNAKE_CASE			)			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				self.get_dummy_tokens()

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

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

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_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
					__lowerCAmelCase											=				pipe.text_encoder(**__SCREAMING_SNAKE_CASE			)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_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
					__lowerCAmelCase											=				create_text_encoder_lora_attn_procs(pipe.text_encoder			)
					set_lora_up_weights(__SCREAMING_SNAKE_CASE,randn_weight=__SCREAMING_SNAKE_CASE			)

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_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
					__lowerCAmelCase											=				pipe.text_encoder(**__SCREAMING_SNAKE_CASE			)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				self.get_dummy_tokens()

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

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

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_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
					__lowerCAmelCase											=				pipe.text_encoder(**__SCREAMING_SNAKE_CASE			)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

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

					# remove monkey patch
					pipe._remove_text_encoder_monkey_patch()

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

					assert torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_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(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""			)			)			)
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE			)

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

					__lowerCAmelCase											=				sd_pipe(**__SCREAMING_SNAKE_CASE,cross_attention_kwargs={"""scale""": 0.5}			).images
					__lowerCAmelCase											=				lora_images_with_scale[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(
					    torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE			),torch.from_numpy(__SCREAMING_SNAKE_CASE			)			)			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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

								__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
								__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

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

            # load LoRA weight file
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE			)

								# check if lora attention processors are used
								for _, module in sd_pipe.unet.named_modules():
											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):
														__lowerCAmelCase											=				(
														    LoRAAttnProcessor2_0 if hasattr(__SCREAMING_SNAKE_CASE,"""scaled_dot_product_attention"""			) else LoRAAttnProcessor
														)
														self.assertIsInstance(module.processor,__SCREAMING_SNAKE_CASE			)
		@unittest.skipIf(torch_device != """cuda""","""This test is supposed to run on GPU"""			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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

								__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
								__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):
														self.assertIsInstance(module.processor,__SCREAMING_SNAKE_CASE			)

            # load LoRA weight file
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE			)

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







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



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_components()
					__lowerCAmelCase											=				StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				sd_pipe.to(__SCREAMING_SNAKE_CASE			)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self.get_dummy_inputs()

					# enable XFormers
					sd_pipe.enable_xformers_memory_efficient_attention()

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

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_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(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""			)			)			)
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE			)

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

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








'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask

