
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__  							=							logging.getLogger(__name__)

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[Any]       ):
						random.seed(lowercase_       )
						np.random.seed(lowercase_       )
						torch.manual_seed(lowercase_       )
						torch.cuda.manual_seed_all(lowercase_       )

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]="gpt2"       ):
						lowercase       :    Union[str, Any]											=  GPTaLMHeadModel.from_pretrained(lowercase_							,			output_hidden_states=lowercase_       )
						torch.save(model.state_dict()							,			model_name + '''local.pt'''       )
						return model

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Dict       ):
						lowercase       :    List[str]											=  copy.deepcopy(lowercase_       )
						model.to(lowercase_       )

						lowercase       :    int											=  ['''bias''', '''LayerNorm.weight''']
						lowercase       :    Optional[int]											=  [
						    {
						        '''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},
						]
						lowercase       :    Dict											=  AdamW(lowercase_							,			lr=5E-5							,			eps=1E-8       )
						lowercase       :    Optional[Any]											=  get_linear_schedule_with_warmup(lowercase_							,			0							,			lowercase_       )
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Union[str, Any]							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				List[Any]="dev.jbl"							,			UpperCAmelCase_							:				List[Any]=recopy_gpta							,			):
						lowercase       :    List[Any]											=  []
						lowercase       :    int											=  []
						lowercase       :    Tuple											=  []

						# Initialize the transformer model
						lowercase       :    Union[str, Any]											=  copy.deepcopy(lowercase_       )
						orig_model.to(device='''cpu'''       )
						torch.cuda.empty_cache()

						# Compute perplexity of initial transformer model for comparison
						model.train()
						lowercase				,				lowercase				,				lowercase       :    Tuple											=  recopy_model(lowercase_							,			lowercase_							,			lowercase_       )

						for step in tqdm(range(lowercase_       )       ):
												lowercase       :    Tuple											=  torch.zeros((1, context_len)							,			dtype=torch.long							,			device=lowercase_       )
												lowercase       :    int											=  random.choice(lowercase_       )
												lowercase       :    Dict											=  random.randint(0							,			len(story[0]       ) - context_len - 1       )
												lowercase       :    str											=  story[0][start : start + context_len]
												lm_optimizer.zero_grad()
												lowercase       :    int											=  model(lowercase_							,			labels=lowercase_       )
												lowercase       :    Optional[Any]											=  outputs[0]
												lowercase       :    Optional[int]											=  compute_perplexity(lowercase_							,			lowercase_							,			lowercase_       )
												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
												lowercase       :    List[Any]											=  compute_perplexity(lowercase_							,			lowercase_							,			lowercase_       )

												# Periodically save the stored (X, IG(X)) pairs
												if step % 10_00 == 0 and step > 1:
																		intermittent_save(lowercase_							,			lowercase_							,			lowercase_							,			lowercase_       )

												# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
												lowercase				,				lowercase				,				lowercase       :    Optional[Any]											=  recopy_model(lowercase_							,			lowercase_							,			lowercase_       )

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

						intermittent_save(lowercase_							,			lowercase_							,			lowercase_							,			lowercase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"							,			UpperCAmelCase_							:				List[str]=1_00							,			UpperCAmelCase_							:				str=10_26							,			UpperCAmelCase_							:				int=True       ):
						lowercase       :    List[str]											=  joblib.load(lowercase_       )
						print('''data loaded'''       )
						lowercase       :    Optional[Any]											=  []
						if trim:
												for i, example in enumerate(lowercase_       ):
																		if len(example[0]       ) > min_len:
																								lowercase       :    Tuple											=  random.randint(0							,			len(example[0]       ) - context_len - 1       )
																								objective_set.append(example[0, start : start + context_len]       )
																		if len(lowercase_       ) >= number:
																								break
												lowercase       :    Tuple											=  []
												for j in range(i + 1							,			len(lowercase_       )       ):
																		if len(data[j][0]       ) > min_len:
																								train_data.append(data[j]       )
						else:
												lowercase       :    str											=  data[0:number]
												lowercase       :    Dict											=  data[number:]

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Union[str, Any]							,			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				str=50							,			UpperCAmelCase_							:				List[Any]="secondary_learner.pt"       ):
						lowercase       :    Union[str, Any]											=  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
						lowercase       :    Tuple											=  train_dataset[:5_12]
						lowercase       :    Optional[Any]											=  train_dataset[5_12:]
						lowercase       :    str											=  DataLoader(lowercase_							,			shuffle=lowercase_							,			batch_size=lowercase_       )
						lowercase       :    Optional[Any]											=  DataLoader(lowercase_							,			shuffle=lowercase_							,			batch_size=lowercase_       )

						# secondary learner model set up
						lowercase       :    Any											=  nn.MSELoss()
						lowercase       :    Dict											=  nn.MSELoss(reduction='''sum'''       )
						secondary_learner.to(lowercase_       )
						lowercase       :    Tuple											=  torch.optim.Adam(secondary_learner.parameters()							,			lr=0.0_00_01       )
						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
						lowercase       :    Optional[int]											=  float('''inf'''       )
						# Iterate through batches until we've used max_steps batches
						for epoch in range(int(lowercase_       )       ):
												lowercase       :    Optional[Any]											=  0.0
												secondary_learner.train()
												for step, batch in enumerate(lowercase_       ):
																		lowercase       :    List[Any]											=  batch[0].to(lowercase_       )
																		lowercase       :    List[Any]											=  batch[1].to(lowercase_       )
																		lowercase       :    Optional[int]											=  secondary_learner(lowercase_       )
																		q_optimizer.zero_grad()
																		lowercase       :    Optional[Any]											=  loss(lowercase_							,			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(lowercase_       )):
																								lowercase       :    Tuple											=  tr_q_loss / (step + 1)

																								secondary_learner.eval()
																								lowercase       :    Tuple											=  0.0
																								lowercase       :    List[str]											=  0.0
																								lowercase       :    List[Any]											=  []
																								lowercase       :    Optional[int]											=  []
																								# Compute performance of the secondary learner after this batch
																								for stepa, batcha in enumerate(lowercase_       ):
																														lowercase       :    Dict											=  batcha[0].to(lowercase_       )
																														lowercase       :    List[str]											=  batcha[1].to(lowercase_       )
																														lowercase       :    int											=  secondary_learner(lowercase_       )
																														q_lossa += test_loss(lowercase_							,			lowercase_       ).item()
																														sum_qa += torch.sum(lowercase_       ).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(lowercase_       )
																								print(
																								    '''Epoch: '''							,			lowercase_							,			'''step: '''							,			lowercase_							,			'''Avg. q:'''							,			sum_qa / len(lowercase_       )							,			'''Train Loss: '''							,			lowercase_							,			'''Test Loss: '''							,			lowercase_							,			)
																								if q_lossa < best_test_loss:
																														joblib.dump((predicted, actual)							,			'''pred_vs_actual.jbl'''       )
																														torch.save(secondary_learner.state_dict()							,			lowercase_       )
																														lowercase       :    Optional[int]											=  q_lossa

																		secondary_learner.train()
						return secondary_learner







class 							UpperCAmelCase	(   nn.Module					):

		def __init__(			self      :					Optional[Any] ,      lowerCAmelCase      :					Optional[Any]					):
								super(__UpperCamelCase ,      self					).__init__()
								lowercase       :    str											=  model.transformer.wte
								lowercase       :    List[str]											=  copy.deepcopy(model.transformer.wte.weight					)
								lowercase       :    Optional[int]											=  nn.Convad(self.embeddings.weight.size(1					) ,      256 ,      3 ,      padding=1					)
								lowercase       :    Optional[int]											=  nn.Sequential(nn.Linear(256 ,      32					) ,      nn.Dropout(p=0.1					) ,      nn.Linear(32 ,      32					) ,      nn.Linear(32 ,      1					)					)

		def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					List[Any]					):
								lowercase       :    Optional[Any]											=  torch.max(self.conv(self.embeddings(__UpperCamelCase					).squeeze(1					).transpose(1 ,      2					)					) ,      2					)[0]
								lowercase       :    Optional[Any]											=  self.fc(__UpperCamelCase					)
								return qs.squeeze(1					)




		@classmethod
		def        _lowerCAmelCase	(			cls      :					Optional[int] ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Tuple					):
								lowercase       :    str											=  cls(__UpperCamelCase					)  # this calls __init__
								lowercase       :    Union[str, Any]											=  torch.load(__UpperCamelCase					)
								secondary_learner.load_state_dict(__UpperCamelCase					)
								lowercase       :    List[Any]											=  model.transformer.wte
								lowercase       :    Tuple											=  copy.deepcopy(model.transformer.wte.weight					)
								return secondary_learner




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_pt_objects import *  # noqa F403
else:
				from .consistency_models import ConsistencyModelPipeline
				from .dance_diffusion import DanceDiffusionPipeline
				from .ddim import DDIMPipeline
				from .ddpm import DDPMPipeline
				from .dit import DiTPipeline
				from .latent_diffusion import LDMSuperResolutionPipeline
				from .latent_diffusion_uncond import LDMPipeline
				from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
				from .pndm import PNDMPipeline
				from .repaint import RePaintPipeline
				from .score_sde_ve import ScoreSdeVePipeline
				from .stochastic_karras_ve import KarrasVePipeline

try:
				if not (is_torch_available() and is_librosa_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
				from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
				if not (is_torch_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
				from .audioldm import AudioLDMPipeline
				from .controlnet import (
				    StableDiffusionControlNetImgaImgPipeline,
				    StableDiffusionControlNetInpaintPipeline,
				    StableDiffusionControlNetPipeline,
				)
				from .deepfloyd_if import (
				    IFImgaImgPipeline,
				    IFImgaImgSuperResolutionPipeline,
				    IFInpaintingPipeline,
				    IFInpaintingSuperResolutionPipeline,
				    IFPipeline,
				    IFSuperResolutionPipeline,
				)
				from .kandinsky import (
				    KandinskyImgaImgPipeline,
				    KandinskyInpaintPipeline,
				    KandinskyPipeline,
				    KandinskyPriorPipeline,
				)
				from .kandinskya_a import (
				    KandinskyVaaControlnetImgaImgPipeline,
				    KandinskyVaaControlnetPipeline,
				    KandinskyVaaImgaImgPipeline,
				    KandinskyVaaInpaintPipeline,
				    KandinskyVaaPipeline,
				    KandinskyVaaPriorEmbaEmbPipeline,
				    KandinskyVaaPriorPipeline,
				)
				from .latent_diffusion import LDMTextToImagePipeline
				from .paint_by_example import PaintByExamplePipeline
				from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
				from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
				from .stable_diffusion import (
				    CycleDiffusionPipeline,
				    StableDiffusionAttendAndExcitePipeline,
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionImageVariationPipeline,
				    StableDiffusionImgaImgPipeline,
				    StableDiffusionInpaintPipeline,
				    StableDiffusionInpaintPipelineLegacy,
				    StableDiffusionInstructPixaPixPipeline,
				    StableDiffusionLatentUpscalePipeline,
				    StableDiffusionLDMaDPipeline,
				    StableDiffusionModelEditingPipeline,
				    StableDiffusionPanoramaPipeline,
				    StableDiffusionParadigmsPipeline,
				    StableDiffusionPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				    StableDiffusionSAGPipeline,
				    StableDiffusionUpscalePipeline,
				    StableUnCLIPImgaImgPipeline,
				    StableUnCLIPPipeline,
				)
				from .stable_diffusion_safe import StableDiffusionPipelineSafe
				from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
				from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
				from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
				from .versatile_diffusion import (
				    VersatileDiffusionDualGuidedPipeline,
				    VersatileDiffusionImageVariationPipeline,
				    VersatileDiffusionPipeline,
				    VersatileDiffusionTextToImagePipeline,
				)
				from .vq_diffusion import VQDiffusionPipeline


try:
				if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
				from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
				if not is_onnx_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_onnx_objects import *  # noqa F403
else:
				from .onnx_utils import OnnxRuntimeModel

try:
				if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
				from .stable_diffusion import (
				    OnnxStableDiffusionImgaImgPipeline,
				    OnnxStableDiffusionInpaintPipeline,
				    OnnxStableDiffusionInpaintPipelineLegacy,
				    OnnxStableDiffusionPipeline,
				    OnnxStableDiffusionUpscalePipeline,
				    StableDiffusionOnnxPipeline,
				)

try:
				if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_objects import *  # noqa F403
else:
				from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
				if not (is_flax_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
				from .controlnet import FlaxStableDiffusionControlNetPipeline
				from .stable_diffusion import (
				    FlaxStableDiffusionImgaImgPipeline,
				    FlaxStableDiffusionInpaintPipeline,
				    FlaxStableDiffusionPipeline,
				)
try:
				if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
				from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline


