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


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






def    UpperCAmelCase__(						lowerCamelCase_="gpt2"  ):



			"""simple docstring"""





			_snake_case              =      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"""





			_snake_case              =      copy.deepcopy(lowerCamelCase_  )
			model.to(lowerCamelCase_  )

			_snake_case              =      ["bias", "LayerNorm.weight"]
			_snake_case              =      [
			    {
			        "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},
			]
			_snake_case              =      AdamW(lowerCamelCase_							,	lr=5E-5							,	eps=1E-8  )
			_snake_case              =      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"""





			_snake_case              =      np.array(lowerCamelCase_  ).mean()
			_snake_case              =      np.array(lowerCamelCase_  ).std()
			_snake_case              =      (real_perps - avg) / std
			_snake_case              =      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"""





			_snake_case              =      []
			_snake_case              =      []
			_snake_case              =      []

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

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

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





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





			_snake_case              =      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
			_snake_case              =      train_dataset[:512]
			_snake_case              =      train_dataset[512:]
			_snake_case              =      DataLoader(lowerCamelCase_							,	shuffle=lowerCamelCase_							,	batch_size=lowerCamelCase_  )
			_snake_case              =      DataLoader(lowerCamelCase_							,	shuffle=lowerCamelCase_							,	batch_size=lowerCamelCase_  )

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

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

									secondary_learner.train()
			return secondary_learner







class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		def __init__(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[Any]   ):






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



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      torch.max(self.conv(self.embeddings(__SCREAMING_SNAKE_CASE   ).squeeze(1   ).transpose(1  ,					2   )   )  ,					2   )[0]
					_snake_case              =      self.fc(__SCREAMING_SNAKE_CASE   )
					return qs.squeeze(1   )







		@classmethod
		def 				_a				(			cls    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					_snake_case              =      cls(__SCREAMING_SNAKE_CASE   )  # this calls __init__
					_snake_case              =      torch.load(__SCREAMING_SNAKE_CASE   )
					secondary_learner.load_state_dict(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      model.transformer.wte
					_snake_case              =      copy.deepcopy(model.transformer.wte.weight   )
					return secondary_learner


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

