




'''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


A				: str					      =			logging.getLogger(__name__)
def 							snake_case_    (  a__						:      List[str]     ):



    """simple docstring"""




    random.seed(A__     )
    np.random.seed(A__     )
    torch.manual_seed(A__     )
    torch.cuda.manual_seed_all(A__     )
def 							snake_case_    (  a__						:      int       ,a__						:      List[Any]       ,a__						:      List[Any]     ):



    """simple docstring"""




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



    """simple docstring"""




    __lowercase     							=		GPTaLMHeadModel.from_pretrained(A__       ,output_hidden_states=A__     )
    torch.save(model.state_dict()       ,model_name + """local.pt"""     )
    return model
def 							snake_case_    (  a__						:      Dict       ,a__						:      int       ,a__						:      List[str]     ):



    """simple docstring"""




    __lowercase     							=		copy.deepcopy(A__     )
    model.to(A__     )

    __lowercase     							=		['bias', 'LayerNorm.weight']
    __lowercase     							=		[
        {
            '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},
    ]
    __lowercase     							=		AdamW(A__       ,lr=5e-5       ,eps=1e-8     )
    __lowercase     							=		get_linear_schedule_with_warmup(A__       ,0       ,A__     )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler
def 							snake_case_    (  a__						:      Any       ,a__						:      Dict       ,a__						:      List[Any]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		np.array(A__     ).mean()
    __lowercase     							=		np.array(A__     ).std()
    __lowercase     							=		(real_perps - avg) / std
    __lowercase     							=		list(zip(A__       ,A__       ,A__     )     )
    joblib.dump(A__       ,A__     )
def 							snake_case_    (  a__						:      Dict       ,a__						:      Dict       ,a__						:      Optional[Any]       ,a__						:      Any       ,a__						:      Optional[int]       ,a__						:      Union[str, Any]       ,a__						:      Optional[Any]       ,a__						:      List[Any]="dev.jbl"       ,a__						:      Dict=recopy_gpta       ,):



    """simple docstring"""




    __lowercase     							=		[]
    __lowercase     							=		[]
    __lowercase     							=		[]

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

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

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 0 and step > 1:
            intermittent_save(A__       ,A__       ,A__       ,A__     )

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        __lowercase     							=		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 							snake_case_    (  a__						:      List[Any]       ,a__						:      Any="data/tokenized_stories_train_wikitext103.jbl"       ,a__						:      Tuple=1_00       ,a__						:      str=10_26       ,a__						:      Tuple=True     ):



    """simple docstring"""




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

    joblib.dump(A__       ,"""objective_set.jbl"""     )
    print("""objective set saved"""     )
    return train_data, objective_set
def 							snake_case_    (  a__						:      Dict       ,a__						:      List[str]       ,a__						:      Optional[int]       ,a__						:      Optional[int]       ,a__						:      Optional[int]=50       ,a__						:      List[Any]="secondary_learner.pt"     ):



    """simple docstring"""




    __lowercase     							=		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
    __lowercase     							=		train_dataset[:5_12]
    __lowercase     							=		train_dataset[5_12:]
    __lowercase     							=		DataLoader(A__       ,shuffle=A__       ,batch_size=A__     )
    __lowercase     							=		DataLoader(A__       ,shuffle=A__       ,batch_size=A__     )

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

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

            secondary_learner.train()
    return secondary_learner






class 			SCREAMING_SNAKE_CASE(							nn.Module	):

       def __init__(       self   ,						lowerCamelCase__	)       ->   List[Any]:

           """simple docstring"""



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

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		torch.max(self.conv(self.embeddings(__UpperCamelCase	).squeeze(1	).transpose(1   ,						2	)	)   ,						2	)[0]
           __lowercase     							=		self.fc(__UpperCamelCase	)
           return qs.squeeze(1	)




       @classmethod
       def        snake_case__     (       cls   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		cls(__UpperCamelCase	)  # this calls __init__
           __lowercase     							=		torch.load(__UpperCamelCase	)
           secondary_learner.load_state_dict(__UpperCamelCase	)
           __lowercase     							=		model.transformer.wte
           __lowercase     							=		copy.deepcopy(model.transformer.wte.weight	)
           return secondary_learner





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowerCAmelCase__	:  Union[str, Any]						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


