



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


_lowercase:				Dict       =	logging.getLogger(__name__)






def 					a(   A :					Union[str, Any] )	-> List[str]:
					"""simple docstring"""





					random.seed(A )
					np.random.seed(A )
					torch.manual_seed(A )
					torch.cuda.manual_seed_all(A )






def 					a(   A :					Union[str, Any]				,  A :					List[Any]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





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






def 					a(   A :					Optional[Any]="gpt2" )	-> Any:
					"""simple docstring"""





					a        =     GPTaLMHeadModel.from_pretrained(A				,  output_hidden_states=A )
					torch.save(model.state_dict()				,  model_name + "local.pt" )
					return model






def 					a(   A :					Any				,  A :					Optional[int]				,  A :					str )	-> List[str]:
					"""simple docstring"""





					a        =     copy.deepcopy(A )
					model.to(A )

					a        =     ["bias", "LayerNorm.weight"]
					a        =     [
					    {
					        "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},
					]
					a        =     AdamW(A				,  lr=5e-5				,  eps=1e-8 )
					a        =     get_linear_schedule_with_warmup(A				,  0				,  A )
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler






def 					a(   A :					Dict				,  A :					List[Any]				,  A :					Any				,  A :					Dict )	-> Any:
					"""simple docstring"""





					a        =     np.array(A ).mean()
					a        =     np.array(A ).std()
					a        =     (real_perps - avg) / std
					a        =     list(zip(A				,  A				,  A ) )
					joblib.dump(A				,  A )






def 					a(   A :					Tuple				,  A :					List[str]				,  A :					str				,  A :					Any				,  A :					Any				,  A :					str				,  A :					str				,  A :					Optional[Any]="dev.jbl"				,  A :					Any=recopy_gpta				,  )	-> Tuple:
					"""simple docstring"""





					a        =     []
					a        =     []
					a        =     []

					# Initialize the transformer model
					a        =     copy.deepcopy(A )
					orig_model.to(device="cpu" )
					torch.cuda.empty_cache()

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

					for step in tqdm(range(A ) ):
										a        =     torch.zeros((1, context_len)				,  dtype=torch.long				,  device=A )
										a        =     random.choice(A )
										a        =     random.randint(0				,  len(story[0] ) - context_len - 1 )
										a        =     story[0][start : start + context_len]
										lm_optimizer.zero_grad()
										a        =     model(A				,  labels=A )
										a        =     outputs[0]
										a        =     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
										a        =     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
										a  ,       a  ,       a        =     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 					a(   A :					str				,  A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"				,  A :					str=100				,  A :					str=1026				,  A :					List[Any]=True )	-> Optional[int]:
					"""simple docstring"""





					a        =     joblib.load(A )
					print("data loaded" )
					a        =     []
					if trim:
										for i, example in enumerate(A ):
															if len(example[0] ) > min_len:
																				a        =     random.randint(0				,  len(example[0] ) - context_len - 1 )
																				objective_set.append(example[0, start : start + context_len] )
															if len(A ) >= number:
																				break
										a        =     []
										for j in range(i + 1				,  len(A ) ):
															if len(data[j][0] ) > min_len:
																				train_data.append(data[j] )
					else:
										a        =     data[0:number]
										a        =     data[number:]

					joblib.dump(A				,  "objective_set.jbl" )
					print("objective set saved" )
					return train_data, objective_set






def 					a(   A :					Any				,  A :					Tuple				,  A :					Tuple				,  A :					Union[str, Any]				,  A :					int=50				,  A :					str="secondary_learner.pt" )	-> Union[str, Any]:
					"""simple docstring"""





					a        =     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
					a        =     train_dataset[:512]
					a        =     train_dataset[512:]
					a        =     DataLoader(A				,  shuffle=A				,  batch_size=A )
					a        =     DataLoader(A				,  shuffle=A				,  batch_size=A )

					# secondary learner model set up
					a        =     nn.MSELoss()
					a        =     nn.MSELoss(reduction="sum" )
					secondary_learner.to(A )
					a        =     torch.optim.Adam(secondary_learner.parameters()				,  lr=0.00_001 )
					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
					a        =     float("inf" )
					# Iterate through batches until we've used max_steps batches
					for epoch in range(int(A ) ):
										a        =     0.0
										secondary_learner.train()
										for step, batch in enumerate(A ):
															a        =     batch[0].to(A )
															a        =     batch[1].to(A )
															a        =     secondary_learner(A )
															q_optimizer.zero_grad()
															a        =     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 )):
																				a        =     tr_q_loss / (step + 1)

																				secondary_learner.eval()
																				a        =     0.0
																				a        =     0.0
																				a        =     []
																				a        =     []
																				# Compute performance of the secondary learner after this batch
																				for stepa, batcha in enumerate(A ):
																									a        =     batcha[0].to(A )
																									a        =     batcha[1].to(A )
																									a        =     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 )
																									a        =     q_lossa

															secondary_learner.train()
					return secondary_learner







class 			_lowercase							(     nn.Module			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						super(lowerCamelCase_	, self	).__init__()
						a        =     model.transformer.wte
						a        =     copy.deepcopy(model.transformer.wte.weight	)
						a        =     nn.Convad(self.embeddings.weight.size(1	)	, 256	, 3	, padding=1	)
						a        =     nn.Sequential(nn.Linear(256	, 32	)	, nn.Dropout(p=0.1	)	, nn.Linear(32	, 32	)	, nn.Linear(32	, 1	)	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

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




	@classmethod
	def   UpperCamelCase_ (cls	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	, [0.0002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 108.4439	) < 1E-2
						assert abs(result_mean.item() - 0.1412	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3
