

'''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


lowerCAmelCase__      				=			logging.getLogger(__name__)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Any:
	random.seed(UpperCamelCase	)
	np.random.seed(UpperCamelCase	)
	torch.manual_seed(UpperCamelCase	)
	torch.cuda.manual_seed_all(UpperCamelCase	)







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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase="gpt2"	)     ->     str:
	UpperCAmelCase_		:   Union[str, Any]													=       GPTaLMHeadModel.from_pretrained(UpperCamelCase ,output_hidden_states=UpperCamelCase	)
	torch.save(model.state_dict() ,model_name + 'local.pt'	)
	return model







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Optional[Any]:
	UpperCAmelCase_		:   Union[str, Any]													=       copy.deepcopy(UpperCamelCase	)
	model.to(UpperCamelCase	)

	UpperCAmelCase_		:   Optional[int]													=       ['''bias''', '''LayerNorm.weight''']
	UpperCAmelCase_		:   Union[str, Any]													=       [
	    {
	        '''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},
	]
	UpperCAmelCase_		:   Tuple													=       AdamW(UpperCamelCase ,lr=5e-5 ,eps=1e-8	)
	UpperCAmelCase_		:   Optional[int]													=       get_linear_schedule_with_warmup(UpperCamelCase ,0 ,UpperCamelCase	)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler







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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase="dev.jbl" ,UpperCamelCase=recopy_gpta ,)     ->     Dict:
	UpperCAmelCase_		:   Tuple													=       []
	UpperCAmelCase_		:   Optional[int]													=       []
	UpperCAmelCase_		:   Any													=       []

	# Initialize the transformer model
	UpperCAmelCase_		:   Optional[int]													=       copy.deepcopy(UpperCamelCase	)
	orig_model.to(device='cpu'	)
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	UpperCAmelCase_		:   List[Any]													=       recopy_model(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)

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

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1_0_0_0 == 0 and step > 1:
			intermittent_save(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		UpperCAmelCase_		:   List[Any]													=       recopy_model(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)

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

	intermittent_save(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl" ,UpperCamelCase=1_0_0 ,UpperCamelCase=1_0_2_6 ,UpperCamelCase=True	)     ->     int:
	UpperCAmelCase_		:   Optional[int]													=       joblib.load(UpperCamelCase	)
	print('data loaded'	)
	UpperCAmelCase_		:   Dict													=       []
	if trim:
		for i, example in enumerate(UpperCamelCase	):
			if len(example[0]	) > min_len:
				UpperCAmelCase_		:   int													=       random.randint(0 ,len(example[0]	) - context_len - 1	)
				objective_set.append(example[0, start : start + context_len]	)
			if len(UpperCamelCase	) >= number:
				break
		UpperCAmelCase_		:   Any													=       []
		for j in range(i + 1 ,len(UpperCamelCase	)	):
			if len(data[j][0]	) > min_len:
				train_data.append(data[j]	)
	else:
		UpperCAmelCase_		:   Optional[Any]													=       data[0:number]
		UpperCAmelCase_		:   int													=       data[number:]

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=5_0 ,UpperCamelCase="secondary_learner.pt"	)     ->     Dict:
	UpperCAmelCase_		:   List[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
	UpperCAmelCase_		:   str													=       train_dataset[:5_1_2]
	UpperCAmelCase_		:   Optional[int]													=       train_dataset[5_1_2:]
	UpperCAmelCase_		:   Dict													=       DataLoader(UpperCamelCase ,shuffle=UpperCamelCase ,batch_size=UpperCamelCase	)
	UpperCAmelCase_		:   Dict													=       DataLoader(UpperCamelCase ,shuffle=UpperCamelCase ,batch_size=UpperCamelCase	)

	# secondary learner model set up
	UpperCAmelCase_		:   List[Any]													=       nn.MSELoss()
	UpperCAmelCase_		:   Tuple													=       nn.MSELoss(reduction='sum'	)
	secondary_learner.to(UpperCamelCase	)
	UpperCAmelCase_		:   Optional[Any]													=       torch.optim.Adam(secondary_learner.parameters() ,lr=0.0_0001	)
	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
	UpperCAmelCase_		:   Union[str, Any]													=       float('inf'	)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(UpperCamelCase	)	):
		UpperCAmelCase_		:   List[Any]													=       0.0
		secondary_learner.train()
		for step, batch in enumerate(UpperCamelCase	):
			UpperCAmelCase_		:   List[str]													=       batch[0].to(UpperCamelCase	)
			UpperCAmelCase_		:   Optional[int]													=       batch[1].to(UpperCamelCase	)
			UpperCAmelCase_		:   List[Any]													=       secondary_learner(UpperCamelCase	)
			q_optimizer.zero_grad()
			UpperCAmelCase_		:   str													=       loss(UpperCamelCase ,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(UpperCamelCase	)):
				UpperCAmelCase_		:   Optional[int]													=       tr_q_loss / (step + 1)

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

			secondary_learner.train()
	return secondary_learner







class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case)				->					List[str]:
								super(_snake_case     ,      self).__init__()
								UpperCAmelCase_		:   str													=       model.transformer.wte
								UpperCAmelCase_		:   str													=       copy.deepcopy(model.transformer.wte.weight)
								UpperCAmelCase_		:   List[Any]													=       nn.Convad(self.embeddings.weight.size(1)     ,      256     ,      3     ,      padding=1)
								UpperCAmelCase_		:   Any													=       nn.Sequential(nn.Linear(256     ,      32)     ,      nn.Dropout(p=0.1)     ,      nn.Linear(32     ,      32)     ,      nn.Linear(32     ,      1))


							def     _snake_case				(      self     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Optional[Any]													=       torch.max(self.conv(self.embeddings(_snake_case).squeeze(1).transpose(1     ,      2))     ,      2)[0]
								UpperCAmelCase_		:   str													=       self.fc(_snake_case)
								return qs.squeeze(1)





							@classmethod
							def     _snake_case				(      cls     ,      _snake_case     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   Optional[int]													=       cls(_snake_case)  # this calls __init__
								UpperCAmelCase_		:   Any													=       torch.load(_snake_case)
								secondary_learner.load_state_dict(_snake_case)
								UpperCAmelCase_		:   str													=       model.transformer.wte
								UpperCAmelCase_		:   Any													=       copy.deepcopy(model.transformer.wte.weight)
								return secondary_learner









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A									=						{
    """configuration_imagegpt""": ["""IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ImageGPTConfig""", """ImageGPTOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						["""ImageGPTFeatureExtractor"""]
 A									=						["""ImageGPTImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						[
     """IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ImageGPTForCausalImageModeling""",
     """ImageGPTForImageClassification""",
     """ImageGPTModel""",
     """ImageGPTPreTrainedModel""",
     """load_tf_weights_in_imagegpt""",
 ]


if TYPE_CHECKING:
 from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
  from .image_processing_imagegpt import ImageGPTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_imagegpt import (
      IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ImageGPTForCausalImageModeling,
      ImageGPTForImageClassification,
      ImageGPTModel,
      ImageGPTPreTrainedModel,
      load_tf_weights_in_imagegpt,
  )

else:
 import sys

 A									=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
