


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 							_UpperCAmelCase    (UpperCamelCase__					:  List[Any]						):
       random.seed(UpperCamelCase__						)
       np.random.seed(UpperCamelCase__						)
       torch.manual_seed(UpperCamelCase__						)
       torch.cuda.manual_seed_all(UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  str    ,							UpperCamelCase__					:  List[Any]						):
       model.eval()
       _A				:	Optional[Any]       =			next(model.parameters()						).device
       _A				:	Any       =			1
       _A				:	int       =			torch.zeros((eval_batch_size, context_len)    ,							dtype=torch.long    ,							device=UpperCamelCase__						)
       _A				:	List[Any]       =			DataLoader(UpperCamelCase__    ,							shuffle=UpperCamelCase__    ,							batch_size=UpperCamelCase__						)
       _A				:	Any       =			torch.zeros(1    ,							device=UpperCamelCase__						)
       _A				:	Optional[Any]       =			0
       for batch in eval_dataloader:
              batch.to(UpperCamelCase__						)
              # pad
              context.zero_()
              for i in range(UpperCamelCase__						):
                     _A				:	int       =			batch[i]
              _A				:	str       =			model(UpperCamelCase__    ,							labels=UpperCamelCase__						)
              eval_loss += outputs[0].sum().item()
              nb_eval_examples += batch.size(0						)
       _A				:	str       =			eval_loss / nb_eval_examples
       _A				:	str       =			torch.exp(UpperCamelCase__						)
       model.train()
       return perplexity
def 							_UpperCAmelCase    (UpperCamelCase__					:  Dict="gpt2"						):
       _A				:	int       =			GPTaLMHeadModel.from_pretrained(UpperCamelCase__    ,							output_hidden_states=UpperCamelCase__						)
       torch.save(model.state_dict()    ,							model_name + "local.pt"						)
       return model
def 							_UpperCAmelCase    (UpperCamelCase__					:  str    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int						):
       _A				:	List[str]       =			copy.deepcopy(UpperCamelCase__						)
       model.to(UpperCamelCase__						)

       _A				:	Optional[Any]       =			["bias", "LayerNorm.weight"]
       _A				:	Optional[int]       =			[
           {
               "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				:	Union[str, Any]       =			AdamW(UpperCamelCase__    ,							lr=5E-5    ,							eps=1E-8						)
       _A				:	Union[str, Any]       =			get_linear_schedule_with_warmup(UpperCamelCase__    ,							0    ,							UpperCamelCase__						)
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  Optional[int]						):
       _A				:	Any       =			np.array(UpperCamelCase__						).mean()
       _A				:	Tuple       =			np.array(UpperCamelCase__						).std()
       _A				:	Any       =			(real_perps - avg) / std
       _A				:	int       =			list(zip(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)						)
       joblib.dump(UpperCamelCase__    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Tuple="dev.jbl"    ,							UpperCamelCase__					:  Tuple=recopy_gpta    ,							):
       _A				:	str       =			[]
       _A				:	Optional[int]       =			[]
       _A				:	Tuple       =			[]

       # Initialize the transformer model
       _A				:	Any       =			copy.deepcopy(UpperCamelCase__						)
       orig_model.to(device="cpu"						)
       torch.cuda.empty_cache()

       # Compute perplexity of initial transformer model for comparison
       model.train()
       _A   ,      _A   ,      _A				:	Dict       =			recopy_model(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

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

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

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              _A   ,      _A   ,      _A				:	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 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Dict="data/tokenized_stories_train_wikitext103.jbl"    ,							UpperCamelCase__					:  str=100    ,							UpperCamelCase__					:  Optional[int]=1026    ,							UpperCamelCase__					:  Optional[int]=True						):
       _A				:	List[Any]       =			joblib.load(UpperCamelCase__						)
       print("data loaded"						)
       _A				:	List[str]       =			[]
       if trim:
              for i, example in enumerate(UpperCamelCase__						):
                     if len(example[0]						) > min_len:
                            _A				:	Union[str, Any]       =			random.randint(0    ,							len(example[0]						) - context_len - 1						)
                            objective_set.append(example[0, start : start + context_len]						)
                     if len(UpperCamelCase__						) >= number:
                            break
              _A				:	Dict       =			[]
              for j in range(i + 1    ,							len(UpperCamelCase__						)						):
                     if len(data[j][0]						) > min_len:
                            train_data.append(data[j]						)
       else:
              _A				:	Optional[int]       =			data[0:number]
              _A				:	List[str]       =			data[number:]

       joblib.dump(UpperCamelCase__    ,							"objective_set.jbl"						)
       print("objective set saved"						)
       return train_data, objective_set
def 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Union[str, Any]=50    ,							UpperCamelCase__					:  List[str]="secondary_learner.pt"						):
       _A				:	Tuple       =			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				:	List[str]       =			train_dataset[:512]
       _A				:	str       =			train_dataset[512:]
       _A				:	List[str]       =			DataLoader(UpperCamelCase__    ,							shuffle=UpperCamelCase__    ,							batch_size=UpperCamelCase__						)
       _A				:	List[Any]       =			DataLoader(UpperCamelCase__    ,							shuffle=UpperCamelCase__    ,							batch_size=UpperCamelCase__						)

       # secondary learner model set up
       _A				:	Tuple       =			nn.MSELoss()
       _A				:	Union[str, Any]       =			nn.MSELoss(reduction="sum"						)
       secondary_learner.to(UpperCamelCase__						)
       _A				:	Dict       =			torch.optim.Adam(secondary_learner.parameters()    ,							lr=0.0_00_01						)
       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				:	Dict       =			float("inf"						)
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(UpperCamelCase__						)						):
              _A				:	Tuple       =			0.0
              secondary_learner.train()
              for step, batch in enumerate(UpperCamelCase__						):
                     _A				:	Optional[Any]       =			batch[0].to(UpperCamelCase__						)
                     _A				:	Union[str, Any]       =			batch[1].to(UpperCamelCase__						)
                     _A				:	List[str]       =			secondary_learner(UpperCamelCase__						)
                     q_optimizer.zero_grad()
                     _A				:	Any       =			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__						)):
                            _A				:	int       =			tr_q_loss / (step + 1)

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

                     secondary_learner.train()
       return secondary_learner







class 					lowerCAmelCase__	(    nn.Module):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase)      ->    List[str]:
            super(__lowerCamelCase			,						self).__init__()
            _A				:	Dict       =			model.transformer.wte
            _A				:	List[Any]       =			copy.deepcopy(model.transformer.wte.weight)
            _A				:	int       =			nn.Convad(self.embeddings.weight.size(1)			,						2_5_6			,						3			,						padding=1)
            _A				:	Optional[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 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            _A				:	str       =			torch.max(self.conv(self.embeddings(__lowerCamelCase).squeeze(1).transpose(1			,						2))			,						2)[0]
            _A				:	List[Any]       =			self.fc(__lowerCamelCase)
            return qs.squeeze(1)


     @classmethod
     def 			_lowerCamelCase			(						cls			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Tuple:
            _A				:	Dict       =			cls(__lowerCamelCase)  # this calls __init__
            _A				:	Tuple       =			torch.load(__lowerCamelCase)
            secondary_learner.load_state_dict(__lowerCamelCase)
            _A				:	Tuple       =			model.transformer.wte
            _A				:	Union[str, Any]       =			copy.deepcopy(model.transformer.wte.weight)
            return secondary_learner




import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import BlipTextModel
    from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=0			,						__lowerCamelCase=None			,						)      ->    str:
            _A				:	Optional[int]       =			parent
            _A				:	Optional[int]       =			batch_size
            _A				:	Optional[int]       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Optional[int]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Dict       =			hidden_size
            _A				:	Optional[Any]       =			projection_dim
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	List[str]       =			intermediate_size
            _A				:	Any       =			dropout
            _A				:	List[str]       =			attention_dropout
            _A				:	str       =			max_position_embeddings
            _A				:	List[Any]       =			initializer_range
            _A				:	int       =			scope
            _A				:	List[str]       =			bos_token_id


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			None
            if self.use_input_mask:
                   _A				:	Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length])

            if input_mask is not None:
                   _A   ,      _A				:	int       =			input_mask.shape
                   _A				:	Optional[Any]       =			np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
                   for batch_idx, start_index in enumerate(__lowerCamelCase):
                          _A				:	Tuple       =			1
                          _A				:	List[str]       =			0

            _A				:	str       =			self.get_config()

            return config, input_ids, input_mask


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            return BlipTextConfig(
                vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						projection_dim=self.projection_dim			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						dropout=self.dropout			,						attention_dropout=self.attention_dropout			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						bos_token_id=self.bos_token_id			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			BlipTextModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            with torch.no_grad():
                   _A				:	int       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)
                   _A				:	List[str]       =			model(__lowerCamelCase)
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
            self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size))


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Dict       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	str       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(BlipTextModel,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			BlipTextModelTester(self)
            _A				:	List[str]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     @unittest.skip(reason="Blip does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Union[str, Any]       =			BlipTextModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase)
