






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_   :	int							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__="gpt2"	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							copy.deepcopy(lowerCAmelCase__	)
		model.to(lowerCAmelCase__	)

		__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(lowerCAmelCase__					,     lr=5E-5					,     eps=1E-8	)
		__A    =							get_linear_schedule_with_warmup(lowerCAmelCase__					,     0					,     lowerCAmelCase__	)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__="dev.jbl"					,     lowerCAmelCase__=recopy_gpta					,     ):


		'''simple docstring'''

		__A    =							[]
		__A    =							[]
		__A    =							[]

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

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

		for step in tqdm(range(lowerCAmelCase__	)	):
				__A    =							torch.zeros((1, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
				__A    =							random.choice(lowerCAmelCase__	)
				__A    =							random.randint(0					,     len(story[0]	) - context_len - 1	)
				__A    =							story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				__A    =							outputs[0]
				__A    =							compute_perplexity(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)
				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(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

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

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

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

		intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"					,     lowerCAmelCase__=100					,     lowerCAmelCase__=1026					,     lowerCAmelCase__=True	):


		'''simple docstring'''

		__A    =							joblib.load(lowerCAmelCase__	)
		print("data loaded"	)
		__A    =							[]
		if trim:
				for i, example in enumerate(lowerCAmelCase__	):
						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(lowerCAmelCase__	) >= number:
								break
				__A    =							[]
				for j in range(i + 1					,     len(lowerCAmelCase__	)	):
						if len(data[j][0]	) > min_len:
								train_data.append(data[j]	)
		else:
				__A    =							data[0:number]
				__A    =							data[number:]

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=50					,     lowerCAmelCase__="secondary_learner.pt"	):


		'''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(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		# secondary learner model set up
		__A    =							nn.MSELoss()
		__A    =							nn.MSELoss(reduction="sum"	)
		secondary_learner.to(lowerCAmelCase__	)
		__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(lowerCAmelCase__	)	):
				__A    =							0.0
				secondary_learner.train()
				for step, batch in enumerate(lowerCAmelCase__	):
						__A    =							batch[0].to(lowerCAmelCase__	)
						__A    =							batch[1].to(lowerCAmelCase__	)
						__A    =							secondary_learner(lowerCAmelCase__	)
						q_optimizer.zero_grad()
						__A    =							loss(lowerCAmelCase__					,     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(lowerCAmelCase__	)):
								__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(lowerCAmelCase__	):
										__A    =							batcha[0].to(lowerCAmelCase__	)
										__A    =							batcha[1].to(lowerCAmelCase__	)
										__A    =							secondary_learner(lowerCAmelCase__	)
										q_lossa += test_loss(lowerCAmelCase__					,     lowerCAmelCase__	).item()
										sum_qa += torch.sum(lowerCAmelCase__	).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(lowerCAmelCase__	)
								print(
								    "Epoch: "					,     lowerCAmelCase__					,     "step: "					,     lowerCAmelCase__					,     "Avg. q:"					,     sum_qa / len(lowerCAmelCase__	)					,     "Train Loss: "					,     lowerCAmelCase__					,     "Test Loss: "					,     lowerCAmelCase__					,     )
								if q_lossa < best_test_loss:
										joblib.dump((predicted, actual)					,     "pred_vs_actual.jbl"	)
										torch.save(secondary_learner.state_dict()					,     lowerCAmelCase__	)
										__A    =							q_lossa

						secondary_learner.train()
		return secondary_learner





class        a__      (	nn.Module		):






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				super(lowercase__							, 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 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							torch.max(self.conv(self.embeddings(lowercase__						).squeeze(1						).transpose(1							, 2						)						)							, 2						)[0]
				__A    =							self.fc(lowercase__						)
				return qs.squeeze(1						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							cls(lowercase__						)  # this calls __init__
				__A    =							torch.load(lowercase__						)
				secondary_learner.load_state_dict(lowercase__						)
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				return secondary_learner







"""simple docstring"""

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
_lowerCamelCase	:      int   =							20

# Time between time steps in seconds
_lowerCamelCase	:      Union[str, Any]   =							INTERVAL / 1000

class 							lowercase   :







				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float = 1.0   ,  _lowerCamelCase :       float = 1.0   ,  _lowerCamelCase :       str = "blue"   ,  ):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		position_x
					A_	:					Optional[int]						 =		position_y
					A_	:					List[str]						 =		velocity_x
					A_	:					List[Any]						 =		velocity_y
					A_	:					Dict						 =		mass
					A_	:					Optional[Any]						 =		size
					A_	:					Any						 =		color







				@property
				def        a_	(				self :       Any							):



					"""simple docstring"""

					return self.position_x, self.position_y







				@property
				def        a_	(				self :       int							):



					"""simple docstring"""

					return self.velocity_x, self.velocity_y







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float   ,  _lowerCamelCase :       float							):



					"""simple docstring"""

					self.velocity_x += force_x * delta_time
					self.velocity_y += force_y * delta_time







				def        a_	(				self :       int   ,  _lowerCamelCase :       float							):



					"""simple docstring"""

					self.position_x += self.velocity_x * delta_time
					self.position_y += self.velocity_y * delta_time

class 							lowercase   :







				def __init__(				self :       Optional[Any]   ,  _lowerCamelCase :       list[Body]   ,  _lowerCamelCase :       float = 1.0   ,  _lowerCamelCase :       float = 1.0   ,  _lowerCamelCase :       float = 0.0   ,  ):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		bodies
					A_	:					Union[str, Any]						 =		gravitation_constant
					A_	:					str						 =		time_factor
					A_	:					str						 =		softening_factor







				def __len__(				self :       Dict							):



					"""simple docstring"""

					return len(self.bodies							)







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       float							):



					"""simple docstring"""

					for bodya in self.bodies:
						A_	:					Tuple						 =		0.0
						A_	:					int						 =		0.0
						for bodya in self.bodies:
							if bodya != bodya:
								A_	:					Any						 =		bodya.position_x - bodya.position_x
								A_	:					Optional[int]						 =		bodya.position_y - bodya.position_y

								# Calculation of the distance using Pythagoras's theorem
								# Extra factor due to the softening technique
								A_	:					List[str]						 =		(dif_x**2 + dif_y**2 + self.softening_factor) ** (
								    1 / 2
								)

								# Newton's law of universal gravitation.
								force_x += (
								    self.gravitation_constant * bodya.mass * dif_x / distance**3
								)
								force_y += (
								    self.gravitation_constant * bodya.mass * dif_y / distance**3
								)

            # Update the body's velocity once all the force components have been added
						bodya.update_velocity(_lowerCamelCase   ,  _lowerCamelCase   ,  delta_time * self.time_factor							)

					# Update the positions only after all the velocities have been updated
					for body in self.bodies:
						body.update_position(delta_time * self.time_factor							)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	body_system.update_system(_UpperCAmelCase						)

	# Update the positions of the patches
	for patch, body in zip(_UpperCAmelCase	,   body_system.bodies						):
		A_	:					str						 =		(body.position_x, body.position_y)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = -1	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = -1	,   _UpperCAmelCase = 1	,   ):




	"""simple docstring"""






	A_	:					Optional[Any]						 =		plt.figure()
	fig.canvas.set_window_title(_UpperCAmelCase						)
	A_	:					Union[str, Any]						 =		plt.axes(
	    xlim=(x_start, x_end)	,   ylim=(y_start, y_end)						)  # Set section to be plotted
	plt.gca().set_aspect('''equal'''						)  # Fix aspect ratio

	# Each body is drawn as a patch by the plt-function
	A_	:					List[str]						 =		[
	    plt.Circle((body.position_x, body.position_y)	,   body.size	,   fc=body.color						)
	    for body in body_system.bodies
	]

	for patch in patches:
		ax.add_patch(_UpperCAmelCase						)

	# Function called at each step of the animation
	def update(_UpperCAmelCase						) -> list[plt.Circle]:
		update_step(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)
		return patches

	A_	:					str						 =		animation.FuncAnimation(  # noqa: F841
	    _UpperCAmelCase	,   _UpperCAmelCase	,   interval=_UpperCAmelCase	,   blit=_UpperCAmelCase						)

	plt.show()

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Optional[int]						 =		0.970_0436
	A_	:					Any						 =		-0.2430_8753
	A_	:					Tuple						 =		0.4_6620_3685
	A_	:					Any						 =		0.4323_6573

	A_	:					Dict						 =		[
	    Body(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   size=0.2	,   color='''red'''						),
	    Body(-position_x	,   -position_y	,   _UpperCAmelCase	,   _UpperCAmelCase	,   size=0.2	,   color='''green'''						),
	    Body(0	,   0	,   -2 * velocity_x	,   -2 * velocity_y	,   size=0.2	,   color='''blue'''						),
	]
	return BodySystem(_UpperCAmelCase	,   time_factor=3						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					int						 =		7.3476E22
	A_	:					Optional[Any]						 =		5.972E24
	A_	:					List[Any]						 =		1022
	A_	:					Optional[int]						 =		3_8439_9000
	A_	:					Tuple						 =		6.674E-11

	# Calculation of the respective velocities so that total impulse is zero,
	# i.e. the two bodies together don't move
	A_	:					Any						 =		earth_mass * velocity_dif / (earth_mass + moon_mass)
	A_	:					List[Any]						 =		moon_velocity - velocity_dif

	A_	:					Optional[Any]						 =		Body(-earth_moon_distance	,   0	,   0	,   _UpperCAmelCase	,   _UpperCAmelCase	,   1000_0000	,   '''grey'''						)
	A_	:					List[Any]						 =		Body(0	,   0	,   0	,   _UpperCAmelCase	,   _UpperCAmelCase	,   5000_0000	,   '''blue'''						)
	return BodySystem([earth, moon]	,   _UpperCAmelCase	,   time_factor=100_0000						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		[]
	for _ in range(10						):
		A_	:					str						 =		random.uniform(-0.5	,   0.5						)
		A_	:					Optional[Any]						 =		random.uniform(-0.5	,   0.5						)

		# Bodies are created pairwise with opposite velocities so that the
		# total impulse remains zero
		bodies.append(
		    Body(
		        random.uniform(-0.5	,   0.5						)	,   random.uniform(-0.5	,   0.5						)	,   _UpperCAmelCase	,   _UpperCAmelCase	,   size=0.05	,   )						)
		bodies.append(
		    Body(
		        random.uniform(-0.5	,   0.5						)	,   random.uniform(-0.5	,   0.5						)	,   -velocity_x	,   -velocity_y	,   size=0.05	,   )						)
	return BodySystem(_UpperCAmelCase	,   0.01	,   10	,   0.1						)


if __name__ == "__main__":
		plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
		plot(
		    'Moon\'s orbit around the earth',
		    example_a(),
		    -430000000,
		    430000000,
		    -430000000,
		    430000000,
		)
		plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)





