

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							:  List[str]     =				logging.getLogger(__name__)

def      a__	(						UpperCAmelCase				:     int     )    ->		Optional[int]:
  random.seed(UpperCAmelCase     )
  np.random.seed(UpperCAmelCase     )
  torch.manual_seed(UpperCAmelCase     )
  torch.cuda.manual_seed_all(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Any     )    ->		Tuple:
  model.eval()
  UpperCAmelCase			:				int										=					next(model.parameters()     ).device
  UpperCAmelCase			:				Optional[int]										=					1
  UpperCAmelCase			:				int										=					torch.zeros((eval_batch_size, context_len)							,					dtype=torch.long							,					device=UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					DataLoader(UpperCAmelCase							,					shuffle=UpperCAmelCase							,					batch_size=UpperCAmelCase     )
  UpperCAmelCase			:				Union[str, Any]										=					torch.zeros(1							,					device=UpperCAmelCase     )
  UpperCAmelCase			:				Tuple										=					0
  for batch in eval_dataloader:
    batch.to(UpperCAmelCase     )
    # pad
    context.zero_()
    for i in range(UpperCAmelCase     ):
      UpperCAmelCase			:				Dict										=					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			:				Union[str, Any]										=					torch.exp(UpperCAmelCase     )
  model.train()
  return perplexity

def      a__	(						UpperCAmelCase				:     Tuple="gpt2"     )    ->		int:
  UpperCAmelCase			:				int										=					GPTaLMHeadModel.from_pretrained(UpperCAmelCase							,					output_hidden_states=UpperCAmelCase     )
  torch.save(model.state_dict()							,					model_name + '''local.pt'''     )
  return model

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     List[str]     )    ->		Tuple:
  UpperCAmelCase			:				Optional[int]										=					copy.deepcopy(UpperCAmelCase     )
  model.to(UpperCAmelCase     )

  UpperCAmelCase			:				Any										=					['''bias''', '''LayerNorm.weight''']
  UpperCAmelCase			:				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			:				Optional[int]										=					AdamW(UpperCAmelCase							,					lr=5E-5							,					eps=1E-8     )
  UpperCAmelCase			:				List[Any]										=					get_linear_schedule_with_warmup(UpperCAmelCase							,					0							,					UpperCAmelCase     )
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler

def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     List[Any]     )    ->		int:
  UpperCAmelCase			:				Dict										=					np.array(UpperCAmelCase     ).mean()
  UpperCAmelCase			:				List[Any]										=					np.array(UpperCAmelCase     ).std()
  UpperCAmelCase			:				Dict										=					(real_perps - avg) / std
  UpperCAmelCase			:				Any										=					list(zip(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase     )     )
  joblib.dump(UpperCAmelCase							,					UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Optional[Any]="dev.jbl"							,					UpperCAmelCase				:     List[Any]=recopy_gpta							,					)    ->		Dict:
  UpperCAmelCase			:				Optional[int]										=					[]
  UpperCAmelCase			:				Any										=					[]
  UpperCAmelCase			:				Optional[Any]										=					[]

  # Initialize the transformer model
  UpperCAmelCase			:				str										=					copy.deepcopy(UpperCAmelCase     )
  orig_model.to(device='''cpu'''     )
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Union[str, Any]										=					recopy_model(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase     )

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

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1_000 == 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					,		UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					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      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     int="data/tokenized_stories_train_wikitext103.jbl"							,					UpperCAmelCase				:     Tuple=100							,					UpperCAmelCase				:     Union[str, Any]=1_026							,					UpperCAmelCase				:     Tuple=True     )    ->		Tuple:
  UpperCAmelCase			:				int										=					joblib.load(UpperCAmelCase     )
  print('''data loaded'''     )
  UpperCAmelCase			:				Optional[int]										=					[]
  if trim:
    for i, example in enumerate(UpperCAmelCase     ):
      if len(example[0]     ) > min_len:
        UpperCAmelCase			:				Dict										=					random.randint(0							,					len(example[0]     ) - context_len - 1     )
        objective_set.append(example[0, start : start + context_len]     )
      if len(UpperCAmelCase     ) >= number:
        break
    UpperCAmelCase			:				Tuple										=					[]
    for j in range(i + 1							,					len(UpperCAmelCase     )     ):
      if len(data[j][0]     ) > min_len:
        train_data.append(data[j]     )
  else:
    UpperCAmelCase			:				List[str]										=					data[0:number]
    UpperCAmelCase			:				Any										=					data[number:]

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

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int=50							,					UpperCAmelCase				:     Optional[int]="secondary_learner.pt"     )    ->		int:
  UpperCAmelCase			:				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			:				Optional[int]										=					train_dataset[:512]
  UpperCAmelCase			:				Any										=					train_dataset[512:]
  UpperCAmelCase			:				str										=					DataLoader(UpperCAmelCase							,					shuffle=UpperCAmelCase							,					batch_size=UpperCAmelCase     )
  UpperCAmelCase			:				List[Any]										=					DataLoader(UpperCAmelCase							,					shuffle=UpperCAmelCase							,					batch_size=UpperCAmelCase     )

  # secondary learner model set up
  UpperCAmelCase			:				Dict										=					nn.MSELoss()
  UpperCAmelCase			:				Any										=					nn.MSELoss(reduction='''sum'''     )
  secondary_learner.to(UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					torch.optim.Adam(secondary_learner.parameters()							,					lr=0.00001     )
  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			:				Tuple										=					float('''inf'''     )
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(UpperCAmelCase     )     ):
    UpperCAmelCase			:				List[str]										=					0.0
    secondary_learner.train()
    for step, batch in enumerate(UpperCAmelCase     ):
      UpperCAmelCase			:				List[Any]										=					batch[0].to(UpperCAmelCase     )
      UpperCAmelCase			:				List[Any]										=					batch[1].to(UpperCAmelCase     )
      UpperCAmelCase			:				Tuple										=					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			:				Union[str, Any]										=					tr_q_loss / (step + 1)

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

      secondary_learner.train()
  return secondary_learner






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[Any],						__A				:	Any		):
     super(__A,						self		).__init__()
     UpperCAmelCase			:				str										=					model.transformer.wte
     UpperCAmelCase			:				Optional[Any]										=					copy.deepcopy(model.transformer.wte.weight		)
     UpperCAmelCase			:				Union[str, Any]										=					nn.Convad(self.embeddings.weight.size(1		),						2_5_6,						3,						padding=1		)
     UpperCAmelCase			:				List[Any]										=					nn.Sequential(nn.Linear(2_5_6,						3_2		),						nn.Dropout(p=0.1		),						nn.Linear(3_2,						3_2		),						nn.Linear(3_2,						1		)		)



   def 			__magic_name__       (				self				:	Tuple,						__A				:	List[str]		):
     UpperCAmelCase			:				Optional[int]										=					torch.max(self.conv(self.embeddings(__A		).squeeze(1		).transpose(1,						2		)		),						2		)[0]
     UpperCAmelCase			:				List[Any]										=					self.fc(__A		)
     return qs.squeeze(1		)





   @classmethod
   def 			__magic_name__       (				cls				:	Tuple,						__A				:	int,						__A				:	List[str]		):
     UpperCAmelCase			:				Any										=					cls(__A		)  # this calls __init__
     UpperCAmelCase			:				Tuple										=					torch.load(__A		)
     secondary_learner.load_state_dict(__A		)
     UpperCAmelCase			:				Any										=					model.transformer.wte
     UpperCAmelCase			:				List[Any]										=					copy.deepcopy(model.transformer.wte.weight		)
     return secondary_learner











"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   """vision-text-dual-encoder"""
	A_				:					Tuple      =   True




	def __init__( self	:						Tuple   ,     lowercase	:						List[Any]=512   ,     lowercase	:						Any=2.65_92   ,     **lowercase	:						int						)       ->			int:
						super().__init__(**lowercase						)

						if "vision_config" not in kwargs:
											raise ValueError("""`vision_config` can not be `None`."""						)

						if "text_config" not in kwargs:
											raise ValueError("""`text_config` can not be `None`."""						)

						__snake_case        =						kwargs.pop("""vision_config"""						)
						__snake_case        =						kwargs.pop("""text_config"""						)

						__snake_case        =						vision_config.pop("""model_type"""						)
						__snake_case        =						text_config.pop("""model_type"""						)

						if vision_model_type == "clip":
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						).vision_config
						elif vision_model_type == "clip_vision_model":
											__snake_case        =						CLIPVisionConfig(**lowercase						)
						else:
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						projection_dim
						__snake_case        =						logit_scale_init_value




	@classmethod
	def 				a_		( cls	:						Union[str, Any]   ,     lowercase	:						PretrainedConfig   ,     lowercase	:						PretrainedConfig   ,     **lowercase	:						Optional[int]						)       ->			Dict:
						return cls(vision_config=vision_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **lowercase						)




	def 				a_		( self	:						Dict						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.vision_config.to_dict()
						__snake_case        =						self.text_config.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


