'''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


__snake_case   :      Optional[int]						    =  logging.getLogger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      str:
    random.seed(_UpperCamelCase   )
    np.random.seed(_UpperCamelCase   )
    torch.manual_seed(_UpperCamelCase   )
    torch.cuda.manual_seed_all(_UpperCamelCase   )





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





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any="gpt2"   )	->      Optional[int]:
    A_     =   GPTaLMHeadModel.from_pretrained(_UpperCamelCase,   output_hidden_states=_UpperCamelCase   )
    torch.save(model.state_dict(),   model_name + '''local.pt'''   )
    return model





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	str,   _UpperCamelCase    :	List[str]   )	->      Tuple:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    model.to(_UpperCamelCase   )

    A_     =   ['''bias''', '''LayerNorm.weight''']
    A_     =   [
        {
            '''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_     =   AdamW(_UpperCamelCase,   lr=5E-5,   eps=1E-8   )
    A_     =   get_linear_schedule_with_warmup(_UpperCamelCase,   0,   _UpperCamelCase   )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    A_     =   np.array(_UpperCamelCase   ).mean()
    A_     =   np.array(_UpperCamelCase   ).std()
    A_     =   (real_perps - avg) / std
    A_     =   list(zip(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )   )
    joblib.dump(_UpperCamelCase,   _UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	str="dev.jbl",   _UpperCamelCase    :	Dict=recopy_gpta,   )	->      List[Any]:
    A_     =   []
    A_     =   []
    A_     =   []

    # Initialize the transformer model
    A_     =   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_     =   recopy_model(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 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_     =   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    :	Any,   _UpperCamelCase    :	List[Any]="data/tokenized_stories_train_wikitext103.jbl",   _UpperCamelCase    :	List[str]=1_00,   _UpperCamelCase    :	Optional[int]=10_26,   _UpperCamelCase    :	Dict=True   )	->      Optional[Any]:
    A_     =   joblib.load(_UpperCamelCase   )
    print('''data loaded'''   )
    A_     =   []
    if trim:
        for i, example in enumerate(_UpperCamelCase   ):
            if len(example[0]   ) > min_len:
                A_     =   random.randint(0,   len(example[0]   ) - context_len - 1   )
                objective_set.append(example[0, start : start + context_len]   )
            if len(_UpperCamelCase   ) >= number:
                break
        A_     =   []
        for j in range(i + 1,   len(_UpperCamelCase   )   ):
            if len(data[j][0]   ) > min_len:
                train_data.append(data[j]   )
    else:
        A_     =   data[0:number]
        A_     =   data[number:]

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





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Union[str, Any]=50,   _UpperCamelCase    :	Tuple="secondary_learner.pt"   )	->      List[Any]:
    A_     =   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_     =   train_dataset[:5_12]
    A_     =   train_dataset[5_12:]
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )

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

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

            secondary_learner.train()
    return secondary_learner




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



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

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           A_     =   torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE ).squeeze(1 ).transpose(1    ,     2 ) )    ,     2 )[0]
           A_     =   self.fc(_SCREAMING_SNAKE_CASE )
           return qs.squeeze(1 )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   cls(_SCREAMING_SNAKE_CASE )  # this calls __init__
           A_     =   torch.load(_SCREAMING_SNAKE_CASE )
           secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE )
           A_     =   model.transformer.wte
           A_     =   copy.deepcopy(model.transformer.wte.weight )
           return secondary_learner







'''simple docstring'''




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






