



"""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   A_    (     _lowercase      ):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_					:str		=     {}
						snake_case_					:int		=     (
						    LoRAAttnProcessor2_0 if hasattr(_lowercase,   """scaled_dot_product_attention"""      ) else LoRAAttnProcessor
						)
						for name, module in text_encoder.named_modules():
												if name.endswith(_lowercase      ):
																		snake_case_					:Optional[Any]		=     lora_attn_processor_class(
																		    hidden_size=module.out_proj.out_features,   cross_attention_dim=_lowercase      )
						return text_lora_attn_procs
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Optional[int]		=     create_text_encoder_lora_attn_procs(_lowercase      )
						snake_case_					:int		=     AttnProcsLayers(_lowercase      )
						return text_encoder_lora_layers
def   A_    (     _lowercase,   _lowercase=False      ):







						'''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"""      ):
																								snake_case_					:int		=     (
																								    torch.randn_like(layer_module.up.weight      )
																								    if randn_weight
																								    else torch.zeros_like(layer_module.up.weight      )
																								)
																								snake_case_					:str		=     torch.nn.Parameter(_lowercase      )

class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Any		)  ->      Tuple:
											torch.manual_seed(0		)
											snake_case_					:List[Any]		=     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		,	)
											snake_case_					:Optional[int]		=     DDIMScheduler(
											    beta_start=0.0_0_0_8_5		,	beta_end=0.0_1_2		,	beta_schedule="""scaled_linear"""		,	clip_sample=snake_case		,	set_alpha_to_one=snake_case		,	steps_offset=1		,	)
											torch.manual_seed(0		)
											snake_case_					:int		=     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		,	)
											snake_case_					: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=1_000		,	)
											snake_case_					:Tuple		=     CLIPTextModel(snake_case		)
											snake_case_					:Optional[int]		=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

											snake_case_,			snake_case_					:List[Any]		=     create_unet_lora_layers(snake_case		)
											snake_case_					:Tuple		=     create_text_encoder_lora_layers(snake_case		)

											snake_case_					:List[str]		=     {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											snake_case_					:Union[str, Any]		=     {
											    """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:				str		)  ->      int:
											snake_case_					:Tuple		=     1
											snake_case_					:Any		=     10
											snake_case_					:List[str]		=     4
											snake_case_					:Optional[Any]		=     (32, 32)

											snake_case_					:List[Any]		=     torch.manual_seed(0		)
											snake_case_					:List[Any]		=     floats_tensor((batch_size, num_channels) + sizes		)
											snake_case_					:Optional[int]		=     torch.randint(1		,	snake_case		,	size=(batch_size, sequence_length)		,	generator=snake_case		)

											snake_case_					:Union[str, Any]		=     {
											    """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:				Optional[int]		)  ->      Optional[int]:
											snake_case_					:Tuple		=     77

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

											snake_case_					:List[str]		=     {}
											snake_case_					:Optional[Any]		=     inputs
											return prepared_inputs






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Any		)  ->      List[str]:
											snake_case_,			snake_case_					: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   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:Optional[int]		=     self.get_dummy_components()
											snake_case_					:Optional[Any]		=     StableDiffusionPipeline(**snake_case		)
											snake_case_					:Tuple		=     sd_pipe.to(snake_case		)
											sd_pipe.set_progress_bar_config(disable=snake_case		)

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

											snake_case_					:Dict		=     sd_pipe(**snake_case		).images
											snake_case_					:Union[str, 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"""]		,	)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case		,	"""pytorch_lora_weights.bin"""		)		)		)
																	sd_pipe.load_lora_weights(snake_case		)

											snake_case_					:Any		=     sd_pipe(**snake_case		).images
											snake_case_					: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   lowerCAmelCase_							(			self:				List[Any]		)  ->      Tuple:
											snake_case_,			snake_case_					:Optional[int]		=     self.get_dummy_components()
											snake_case_					:Tuple		=     StableDiffusionPipeline(**snake_case		)
											snake_case_					:Dict		=     sd_pipe.to(snake_case		)
											sd_pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_,			snake_case_,			snake_case_					:Dict		=     self.get_dummy_inputs()

											snake_case_					:Optional[Any]		=     sd_pipe(**snake_case		).images
											snake_case_					:str		=     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		)

											snake_case_					:Any		=     sd_pipe(**snake_case		).images
											snake_case_					: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   lowerCAmelCase_							(			self:				Optional[int]		)  ->      str:
											snake_case_,			snake_case_					:Union[str, Any]		=     self.get_dummy_components()
											snake_case_					:Optional[int]		=     lora_components["""unet_lora_attn_procs"""]
											snake_case_					:Dict		=     StableDiffusionPipeline(**snake_case		)
											snake_case_					:Optional[int]		=     sd_pipe.to(snake_case		)
											sd_pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_,			snake_case_,			snake_case_					:str		=     self.get_dummy_inputs()

											snake_case_					:Optional[Any]		=     sd_pipe(**snake_case		).images
											snake_case_					:List[Any]		=     original_images[0, -3:, -3:, -1]

											with tempfile.TemporaryDirectory() as tmpdirname:
																	snake_case_					:List[str]		=     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		)

											snake_case_					:str		=     sd_pipe(**snake_case		).images
											snake_case_					:Optional[Any]		=     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   lowerCAmelCase_							(			self:				Any		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:int		=     self.get_dummy_components()
											snake_case_					:Union[str, Any]		=     StableDiffusionPipeline(**snake_case		)

											snake_case_					:Optional[int]		=     self.get_dummy_tokens()

											# inference without lora
											snake_case_					: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
											snake_case_					:Union[str, 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
											snake_case_					: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
											snake_case_					:Dict		=     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
											snake_case_					:Dict		=     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   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:Optional[Any]		=     self.get_dummy_components()
											snake_case_					:List[Any]		=     StableDiffusionPipeline(**snake_case		)

											snake_case_					:Tuple		=     self.get_dummy_tokens()

											# inference without lora
											snake_case_					:Union[str, Any]		=     pipe.text_encoder(**snake_case		)[0]
											assert outputs_without_lora.shape == (1, 77, 32)

											# create lora_attn_procs with randn up.weights
											snake_case_					:List[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
											snake_case_					:Tuple		=     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
											snake_case_					:str		=     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   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      List[str]:
											snake_case_,			snake_case_					:List[Any]		=     self.get_dummy_components()
											snake_case_					:str		=     StableDiffusionPipeline(**snake_case		)
											snake_case_					:Tuple		=     sd_pipe.to(snake_case		)
											sd_pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_,			snake_case_,			snake_case_					:List[str]		=     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		)

											snake_case_					:Optional[Any]		=     sd_pipe(**snake_case		).images
											snake_case_					:List[str]		=     lora_images[0, -3:, -3:, -1]

											snake_case_					:Optional[Any]		=     sd_pipe(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).images
											snake_case_					:int		=     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   lowerCAmelCase_							(			self:				Tuple		)  ->      Any:
											with tempfile.TemporaryDirectory() as tmpdirname:
																	self.create_lora_weight_file(snake_case		)

																	snake_case_,			snake_case_					:int		=     self.get_dummy_components()
																	snake_case_					:Tuple		=     StableDiffusionPipeline(**snake_case		)
																	snake_case_					: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		):
																													snake_case_					:Dict		=     (
																													    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   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      List[str]:
											with tempfile.TemporaryDirectory() as tmpdirname:
																	self.create_lora_weight_file(snake_case		)

																	snake_case_,			snake_case_					:Union[str, Any]		=     self.get_dummy_components()
																	snake_case_					:Optional[Any]		=     StableDiffusionPipeline(**snake_case		)
																	snake_case_					:int		=     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   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      int:
											snake_case_,			snake_case_					:Dict		=     self.get_dummy_components()
											snake_case_					:Optional[int]		=     StableDiffusionPipeline(**snake_case		)
											snake_case_					:List[Any]		=     sd_pipe.to(snake_case		)
											sd_pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_,			snake_case_,			snake_case_					:List[str]		=     self.get_dummy_inputs()

											# enable XFormers
											sd_pipe.enable_xformers_memory_efficient_attention()

											snake_case_					:str		=     sd_pipe(**snake_case		).images
											snake_case_					: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		)

											snake_case_					:List[str]		=     sd_pipe(**snake_case		).images
											snake_case_					:Dict		=     lora_images[0, -3:, -3:, -1]

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


"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

snake_case					  =						version.parse(version.parse(torch.__version__).base_version)

snake_case					  =						parsed_torch_version_base >= version.parse('2.0')
snake_case					  =						parsed_torch_version_base >= version.parse('1.12')
snake_case					  =						parsed_torch_version_base >= version.parse('1.11')
snake_case					  =						parsed_torch_version_base < version.parse('1.11')

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self     )
					else:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self.dtype     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
										else:
															SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      nn.Linear(new_size[1],	new_size[0],	bias=layer.bias is not None     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					if layer.bias is not None:
										SCREAMING_SNAKE_CASE           =      False
										new_layer.bias.copy_(b.contiguous()     )
										SCREAMING_SNAKE_CASE           =      True
					return new_layer




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      ConvaD(new_size[1],	new_size[0]     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					SCREAMING_SNAKE_CASE           =      False
					new_layer.bias.copy_(b.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					return new_layer

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					if isinstance(SCREAMING_SNAKE_CASE_,	nn.Linear     ):
										return prune_linear_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=0 if dim is None else dim     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										return prune_convad_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=1 if dim is None else dim     )
					else:
										raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_SNAKE_CASE_     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																				    f'''found shape {input_tensor.shape[chunk_dim]}'''     )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
															    f'''size {chunk_size}'''     )

										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =      tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     ) for input_tensor in input_tensors     )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =      tuple(forward_fn(*SCREAMING_SNAKE_CASE_     ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_     )     )
										# concatenate output at same dimension
										return torch.cat(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     )

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      set(SCREAMING_SNAKE_CASE_     ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										SCREAMING_SNAKE_CASE           =      head - sum(1 if h < head else 0 for h in already_pruned_heads     )
										SCREAMING_SNAKE_CASE           =      0
					SCREAMING_SNAKE_CASE           =      mask.view(-1     ).contiguous().eq(1     )
					SCREAMING_SNAKE_CASE           =      torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
					return heads, index

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
