






"""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_				(  __A  :		nn.Module			):
					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_				(  __A  :		nn.Module			):
					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_				(  __A  :		nn.Module			):
					lowerCamelCase__				=create_text_encoder_lora_attn_procs(lowercase_			)
					lowerCamelCase__				=AttnProcsLayers(lowercase_			)
					return text_encoder_lora_layers




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		Any=False			):
					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        __lowercase(       unittest.TestCase  ):






						def 		lowerCAmelCase   (					self)			->  Any:




											"""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=0.00085    ,						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__				=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)			->  List[str]:




											"""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)			->  Union[str, Any]:




											"""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)			->  Optional[Any]:




											"""simple docstring"""





											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)			->  Union[str, Any]:




											"""simple docstring"""





											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__				=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)			->  int:




											"""simple docstring"""





											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__				=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)			->  Dict:




											"""simple docstring"""





											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__				=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)			->  Any:




											"""simple docstring"""





											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)			->  Optional[int]:




											"""simple docstring"""





											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)			->  Dict:




											"""simple docstring"""





											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__				=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)			->  List[Any]:




											"""simple docstring"""





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

																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)			->  Optional[Any]:




											"""simple docstring"""





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

																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)			->  List[str]:




											"""simple docstring"""





											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__				=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 copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_snake_case       :      Union[str, Any]       =					logging.getLogger(__name__)




def 						lowerCamelCase_				(  __A  :		Any			):
					random.seed(__A			)
					np.random.seed(__A			)
					torch.manual_seed(__A			)
					torch.cuda.manual_seed_all(__A			)




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		int			,	__A  :		Tuple			):
					model.eval()
					lowerCamelCase__				=next(model.parameters()			).device
					lowerCamelCase__				=1
					lowerCamelCase__				=torch.zeros((eval_batch_size, context_len)			,	dtype=torch.long			,	device=__A			)
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			)
					lowerCamelCase__				=torch.zeros(1			,	device=__A			)
					lowerCamelCase__				=0
					for batch in eval_dataloader:
										batch.to(__A			)
										# pad
										context.zero_()
										for i in range(__A			):
															lowerCamelCase__				=batch[i]
										lowerCamelCase__				=model(__A			,	labels=__A			)
										eval_loss += outputs[0].sum().item()
										nb_eval_examples += batch.size(0			)
					lowerCamelCase__				=eval_loss / nb_eval_examples
					lowerCamelCase__				=torch.exp(__A			)
					model.train()
					return perplexity




def 						lowerCamelCase_				(  __A  :		List[str]="gpt2"			):
					lowerCamelCase__				=GPTaLMHeadModel.from_pretrained(__A			,	output_hidden_states=__A			)
					torch.save(model.state_dict()			,	model_name + 'local.pt'			)
					return model




def 						lowerCamelCase_				(  __A  :		Optional[Any]			,	__A  :		Tuple			,	__A  :		List[str]			):
					lowerCamelCase__				=copy.deepcopy(__A			)
					model.to(__A			)

					lowerCamelCase__				=['bias', 'LayerNorm.weight']
					lowerCamelCase__				=[
					    {
					        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
					        'weight_decay': 0.0,
					    },
					    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)], 'weight_decay': 0.0},
					]
					lowerCamelCase__				=AdamW(__A			,	lr=5E-5			,	eps=1E-8			)
					lowerCamelCase__				=get_linear_schedule_with_warmup(__A			,	0			,	__A			)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		Optional[int]			,	__A  :		str			,	__A  :		Tuple			):
					lowerCamelCase__				=np.array(__A			).mean()
					lowerCamelCase__				=np.array(__A			).std()
					lowerCamelCase__				=(real_perps - avg) / std
					lowerCamelCase__				=list(zip(__A			,	__A			,	__A			)			)
					joblib.dump(__A			,	__A			)




def 						lowerCamelCase_				(  __A  :		Optional[int]			,	__A  :		Union[str, Any]			,	__A  :		Any			,	__A  :		Optional[Any]			,	__A  :		Any			,	__A  :		Dict			,	__A  :		Any			,	__A  :		List[str]="dev.jbl"			,	__A  :		List[str]=recopy_gpta			,	):
					lowerCamelCase__				=[]
					lowerCamelCase__				=[]
					lowerCamelCase__				=[]

					# Initialize the transformer model
					lowerCamelCase__				=copy.deepcopy(__A			)
					orig_model.to(device='cpu'			)
					torch.cuda.empty_cache()

					# Compute perplexity of initial transformer model for comparison
					model.train()
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=recopy_model(__A			,	__A			,	__A			)

					for step in tqdm(range(__A			)			):
										lowerCamelCase__				=torch.zeros((1, context_len)			,	dtype=torch.long			,	device=__A			)
										lowerCamelCase__				=random.choice(__A			)
										lowerCamelCase__				=random.randint(0			,	len(story[0]			) - context_len - 1			)
										lowerCamelCase__				=story[0][start : start + context_len]
										lm_optimizer.zero_grad()
										lowerCamelCase__				=model(__A			,	labels=__A			)
										lowerCamelCase__				=outputs[0]
										lowerCamelCase__				=compute_perplexity(__A			,	__A			,	__A			)
										model.train()
										lm_loss.backward()
										# Do LM backprop
										torch.nn.utils.clip_grad_norm_(model.parameters()			,	3.0			)
										lm_optimizer.step()
										lm_scheduler.step()  # Update learning rate schedule

										# Compute perplexity after back-propagating on the selected context
										lowerCamelCase__				=compute_perplexity(__A			,	__A			,	__A			)

										# Periodically save the stored (X, IG(X)) pairs
										if step % 1000 == 0 and step > 1:
															intermittent_save(__A			,	__A			,	__A			,	__A			)

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=recopy_model(__A			,	__A			,	__A			)

										past_perps.append(past_perp.item()			)
										real_perps.append(orig_perp - real_perp.item()			)
										contexts.append(np.array(context.cpu()			)			)

					intermittent_save(__A			,	__A			,	__A			,	__A			)




def 						lowerCamelCase_				(  __A  :		Optional[Any]			,	__A  :		Any="data/tokenized_stories_train_wikitext103.jbl"			,	__A  :		Any=100			,	__A  :		str=1026			,	__A  :		int=True			):
					lowerCamelCase__				=joblib.load(__A			)
					print('data loaded'			)
					lowerCamelCase__				=[]
					if trim:
										for i, example in enumerate(__A			):
															if len(example[0]			) > min_len:
																				lowerCamelCase__				=random.randint(0			,	len(example[0]			) - context_len - 1			)
																				objective_set.append(example[0, start : start + context_len]			)
															if len(__A			) >= number:
																				break
										lowerCamelCase__				=[]
										for j in range(i + 1			,	len(__A			)			):
															if len(data[j][0]			) > min_len:
																				train_data.append(data[j]			)
					else:
										lowerCamelCase__				=data[0:number]
										lowerCamelCase__				=data[number:]

					joblib.dump(__A			,	'objective_set.jbl'			)
					print('objective set saved'			)
					return train_data, objective_set




def 						lowerCamelCase_				(  __A  :		Optional[int]			,	__A  :		List[str]			,	__A  :		Optional[Any]			,	__A  :		str			,	__A  :		Optional[int]=50			,	__A  :		Optional[int]="secondary_learner.pt"			):
					lowerCamelCase__				=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
					# We will use the first 512 pairs from our dataset as a test set for
					# our secondary learner and the rest to train
					lowerCamelCase__				=train_dataset[:512]
					lowerCamelCase__				=train_dataset[512:]
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			)
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			)

					# secondary learner model set up
					lowerCamelCase__				=nn.MSELoss()
					lowerCamelCase__				=nn.MSELoss(reduction='sum'			)
					secondary_learner.to(__A			)
					lowerCamelCase__				=torch.optim.Adam(secondary_learner.parameters()			,	lr=0.0_0_0_0_1			)
					secondary_learner.train()

					# TODO in original code this is written as number of actual batches seen
					# not number of items seen but other places it is number of items instead.
					# improve consistency! changed this to epochs for clarity
					lowerCamelCase__				=float('inf'			)
					# Iterate through batches until we've used max_steps batches
					for epoch in range(int(__A			)			):
										lowerCamelCase__				=0.0
										secondary_learner.train()
										for step, batch in enumerate(__A			):
															lowerCamelCase__				=batch[0].to(__A			)
															lowerCamelCase__				=batch[1].to(__A			)
															lowerCamelCase__				=secondary_learner(__A			)
															q_optimizer.zero_grad()
															lowerCamelCase__				=loss(__A			,	real_q.float()			)
															q_loss.backward()
															q_optimizer.step()
															tr_q_loss += q_loss.item()

															# model trains fairly quickly so we won't wait for a full epoch
															# eval is triggered at eval_freq and end of epochs
															if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__A			)):
																				lowerCamelCase__				=tr_q_loss / (step + 1)

																				secondary_learner.eval()
																				lowerCamelCase__				=0.0
																				lowerCamelCase__				=0.0
																				lowerCamelCase__				=[]
																				lowerCamelCase__				=[]
																				# Compute performance of the secondary learner after this batch
																				for stepa, batcha in enumerate(__A			):
																									lowerCamelCase__				=batcha[0].to(__A			)
																									lowerCamelCase__				=batcha[1].to(__A			)
																									lowerCamelCase__				=secondary_learner(__A			)
																									q_lossa += test_loss(__A			,	__A			).item()
																									sum_qa += torch.sum(__A			).item()
																									for ei, i in enumerate(predicted_qa.cpu().detach().numpy()			):
																														predicted.append(i.item()			)
																									for ei, i in enumerate(real_qa.cpu().detach().numpy()			):
																														actual.append(i.item()			)

																				q_lossa /= len(__A			)
																				print(
																				    'Epoch: '			,	__A			,	'step: '			,	__A			,	'Avg. q:'			,	sum_qa / len(__A			)			,	'Train Loss: '			,	__A			,	'Test Loss: '			,	__A			,	)
																				if q_lossa < best_test_loss:
																									joblib.dump((predicted, actual)			,	'pred_vs_actual.jbl'			)
																									torch.save(secondary_learner.state_dict()			,	__A			)
																									lowerCamelCase__				=q_lossa

															secondary_learner.train()
					return secondary_learner




class        __lowercase(       nn.Module  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[int]:




											"""simple docstring"""





											super(_SCREAMING_SNAKE_CASE    ,						self).__init__()
											lowerCamelCase__				=model.transformer.wte
											lowerCamelCase__				=copy.deepcopy(model.transformer.wte.weight)
											lowerCamelCase__				=nn.Convad(self.embeddings.weight.size(1)    ,						2_56    ,						3    ,						padding=1)
											lowerCamelCase__				=nn.Sequential(nn.Linear(2_56    ,						32)    ,						nn.Dropout(p=0.1)    ,						nn.Linear(32    ,						32)    ,						nn.Linear(32    ,						1))






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				=torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE).squeeze(1).transpose(1    ,						2))    ,						2)[0]
											lowerCamelCase__				=self.fc(_SCREAMING_SNAKE_CASE)
											return qs.squeeze(1)






						@classmethod
						def 		lowerCAmelCase   (					cls    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=cls(_SCREAMING_SNAKE_CASE)  # this calls __init__
											lowerCamelCase__				=torch.load(_SCREAMING_SNAKE_CASE)
											secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=model.transformer.wte
											lowerCamelCase__				=copy.deepcopy(model.transformer.wte.weight)
											return secondary_learner