



"""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


__UpperCAmelCase      :      List[Any]   					=					logging.getLogger(__name__)
def      A      (			_A							):




					"""simple docstring"""
					random.seed(_A							)
					np.random.seed(_A							)
					torch.manual_seed(_A							)
					torch.cuda.manual_seed_all(_A							)
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					model.eval()
					snake_case_						:str								=						next(model.parameters()							).device
					snake_case_						:Optional[Any]								=						1
					snake_case_						:Dict								=						torch.zeros((eval_batch_size, context_len),     dtype=torch.long,     device=_A							)
					snake_case_						:str								=						DataLoader(_A,     shuffle=_A,     batch_size=_A							)
					snake_case_						:List[str]								=						torch.zeros(1,     device=_A							)
					snake_case_						:int								=						0
					for batch in eval_dataloader:
										batch.to(_A							)
										# pad
										context.zero_()
										for i in range(_A							):
															snake_case_						:Optional[int]								=						batch[i]
										snake_case_						:str								=						model(_A,     labels=_A							)
										eval_loss += outputs[0].sum().item()
										nb_eval_examples += batch.size(0							)
					snake_case_						:Optional[int]								=						eval_loss / nb_eval_examples
					snake_case_						:Any								=						torch.exp(_A							)
					model.train()
					return perplexity
def      A      (			_A="gpt2"							):




					"""simple docstring"""
					snake_case_						:str								=						GPTaLMHeadModel.from_pretrained(_A,     output_hidden_states=_A							)
					torch.save(model.state_dict(),     model_name + "local.pt"							)
					return model
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:str								=						copy.deepcopy(_A							)
					model.to(_A							)

					snake_case_						:Optional[int]								=						["bias", "LayerNorm.weight"]
					snake_case_						:Dict								=						[
					    {
					        "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_						:Optional[int]								=						AdamW(_A,     lr=5e-5,     eps=1e-8							)
					snake_case_						:List[Any]								=						get_linear_schedule_with_warmup(_A,     0,     _A							)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler
def      A      (			_A,     _A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:Optional[int]								=						np.array(_A							).mean()
					snake_case_						:Optional[int]								=						np.array(_A							).std()
					snake_case_						:Optional[int]								=						(real_perps - avg) / std
					snake_case_						:List[Any]								=						list(zip(_A,     _A,     _A							)							)
					joblib.dump(_A,     _A							)
def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     _A,     _A="dev.jbl",     _A=recopy_gpta,     ):




					"""simple docstring"""
					snake_case_						:List[str]								=						[]
					snake_case_						:Union[str, Any]								=						[]
					snake_case_						:List[Any]								=						[]

					# Initialize the transformer model
					snake_case_						:int								=						copy.deepcopy(_A							)
					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_						:List[str]								=						recopy_model(_A,     _A,     _A							)

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

										# Periodically save the stored (X, IG(X)) pairs
										if step % 1_000 == 0 and step > 1:
															intermittent_save(_A,     _A,     _A,     _A							)

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										snake_case_	,  snake_case_	,  snake_case_						:Union[str, Any]								=						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,     _A="data/tokenized_stories_train_wikitext103.jbl",     _A=100,     _A=1_026,     _A=True							):




					"""simple docstring"""
					snake_case_						:Optional[int]								=						joblib.load(_A							)
					print("data loaded"							)
					snake_case_						:List[Any]								=						[]
					if trim:
										for i, example in enumerate(_A							):
															if len(example[0]							) > min_len:
																				snake_case_						:List[str]								=						random.randint(0,     len(example[0]							) - context_len - 1							)
																				objective_set.append(example[0, start : start + context_len]							)
															if len(_A							) >= number:
																				break
										snake_case_						:Optional[int]								=						[]
										for j in range(i + 1,     len(_A							)							):
															if len(data[j][0]							) > min_len:
																				train_data.append(data[j]							)
					else:
										snake_case_						:List[str]								=						data[0:number]
										snake_case_						:Tuple								=						data[number:]

					joblib.dump(_A,     "objective_set.jbl"							)
					print("objective set saved"							)
					return train_data, objective_set
def      A      (			_A,     _A,     _A,     _A,     _A=50,     _A="secondary_learner.pt"							):




					"""simple docstring"""
					snake_case_						:Dict								=						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_						:Dict								=						train_dataset[:512]
					snake_case_						:List[str]								=						train_dataset[512:]
					snake_case_						:int								=						DataLoader(_A,     shuffle=_A,     batch_size=_A							)
					snake_case_						:List[str]								=						DataLoader(_A,     shuffle=_A,     batch_size=_A							)

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

																				secondary_learner.eval()
																				snake_case_						:List[Any]								=						0.0
																				snake_case_						:Tuple								=						0.0
																				snake_case_						:Union[str, Any]								=						[]
																				snake_case_						:Union[str, Any]								=						[]
																				# Compute performance of the secondary learner after this batch
																				for stepa, batcha in enumerate(_A							):
																									snake_case_						:int								=						batcha[0].to(_A							)
																									snake_case_						:Optional[Any]								=						batcha[1].to(_A							)
																									snake_case_						:Optional[Any]								=						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							)
																									snake_case_						:Union[str, Any]								=						q_lossa

															secondary_learner.train()
					return secondary_learner

class 	__lowerCAmelCase							(nn.Module  ):


					'''simple docstring'''


					def __init__(      self    ,       a   ):




										"""simple docstring"""





										super(UpperCamelCase__    ,       self   ).__init__()
										snake_case_						:List[str]								=						model.transformer.wte
										snake_case_						:int								=						copy.deepcopy(model.transformer.wte.weight   )
										snake_case_						:Tuple								=						nn.Convad(self.embeddings.weight.size(1   )    ,       2_56    ,       3    ,       padding=1   )
										snake_case_						:Tuple								=						nn.Sequential(nn.Linear(2_56    ,       32   )    ,       nn.Dropout(p=0.1   )    ,       nn.Linear(32    ,       32   )    ,       nn.Linear(32    ,       1   )   )







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:Any								=						torch.max(self.conv(self.embeddings(UpperCamelCase__   ).squeeze(1   ).transpose(1    ,       2   )   )    ,       2   )[0]
										snake_case_						:Union[str, Any]								=						self.fc(UpperCamelCase__   )
										return qs.squeeze(1   )







					@classmethod
					def 		_a (      cls    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						cls(UpperCamelCase__   )  # this calls __init__
										snake_case_						:List[str]								=						torch.load(UpperCamelCase__   )
										secondary_learner.load_state_dict(UpperCamelCase__   )
										snake_case_						:Any								=						model.transformer.wte
										snake_case_						:List[str]								=						copy.deepcopy(model.transformer.wte.weight   )
										return secondary_learner









"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

