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


lowercase_  							=			logging.getLogger(__name__)







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[Any]   ):







    '''simple docstring'''
    random.seed(_UpperCamelCase   )
    np.random.seed(_UpperCamelCase   )
    torch.manual_seed(_UpperCamelCase   )
    torch.cuda.manual_seed_all(_UpperCamelCase   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					int   ):







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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict="gpt2"   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    GPTaLMHeadModel.from_pretrained(_UpperCamelCase		,							output_hidden_states=_UpperCamelCase   )
    torch.save(model.state_dict()		,							model_name + """local.pt"""   )
    return model







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Dict   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    copy.deepcopy(_UpperCamelCase   )
    model.to(_UpperCamelCase   )

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Optional[int]		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    __snake_case       :				int 			=    np.array(_UpperCamelCase   ).mean()
    __snake_case       :				Dict 			=    np.array(_UpperCamelCase   ).std()
    __snake_case       :				str 			=    (real_perps - avg) / std
    __snake_case       :				Optional[Any] 			=    list(zip(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase   )   )
    joblib.dump(_UpperCamelCase		,							_UpperCamelCase   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					List[str]="dev.jbl"		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=recopy_gpta		,							):







    '''simple docstring'''
    __snake_case       :				Any 			=    []
    __snake_case       :				List[str] 			=    []
    __snake_case       :				Optional[int] 			=    []

    # Initialize the transformer model
    __snake_case       :				Optional[Any] 			=    copy.deepcopy(_UpperCamelCase   )
    orig_model.to(device="""cpu"""   )
    torch.cuda.empty_cache()

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

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 1_0_0_0 == 0 and step > 1:
            intermittent_save(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase   )

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        __snake_case       :				Optional[Any] 			=    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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple="data/tokenized_stories_train_wikitext103.jbl"		,							__SCREAMING_SNAKE_CASE       :					Dict=1_0_0		,							__SCREAMING_SNAKE_CASE       :					str=1_0_2_6		,							__SCREAMING_SNAKE_CASE       :					Tuple=True   ):







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

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					List[str]=5_0		,							__SCREAMING_SNAKE_CASE       :					Tuple="secondary_learner.pt"   ):







    '''simple docstring'''
    __snake_case       :				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
    __snake_case       :				Tuple 			=    train_dataset[:5_1_2]
    __snake_case       :				List[Any] 			=    train_dataset[5_1_2:]
    __snake_case       :				Any 			=    DataLoader(_UpperCamelCase		,							shuffle=_UpperCamelCase		,							batch_size=_UpperCamelCase   )
    __snake_case       :				str 			=    DataLoader(_UpperCamelCase		,							shuffle=_UpperCamelCase		,							batch_size=_UpperCamelCase   )

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

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

            secondary_learner.train()
    return secondary_learner







class      SCREAMING_SNAKE_CASE__					(  nn.Module    ):

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Optional[int]			):
           super(UpperCAmelCase_			,			self			).__init__()
           __snake_case       :				Optional[Any] 			=    model.transformer.wte
           __snake_case       :				Optional[int] 			=    copy.deepcopy(model.transformer.wte.weight			)
           __snake_case       :				List[str] 			=    nn.Convad(self.embeddings.weight.size(1			)			,			2_56			,			3			,			padding=1			)
           __snake_case       :				int 			=    nn.Sequential(nn.Linear(2_56			,			32			)			,			nn.Dropout(p=0.1			)			,			nn.Linear(32			,			32			)			,			nn.Linear(32			,			1			)			)

       def 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     List[str]			):
           __snake_case       :				int 			=    torch.max(self.conv(self.embeddings(UpperCAmelCase_			).squeeze(1			).transpose(1			,			2			)			)			,			2			)[0]
           __snake_case       :				str 			=    self.fc(UpperCAmelCase_			)
           return qs.squeeze(1			)





       @classmethod
       def 			snake_case__    (							cls					:     List[Any]			,			_lowerCAmelCase					:     Optional[Any]			,			_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				Dict 			=    cls(UpperCAmelCase_			)  # this calls __init__
           __snake_case       :				List[Any] 			=    torch.load(UpperCAmelCase_			)
           secondary_learner.load_state_dict(UpperCAmelCase_			)
           __snake_case       :				List[str] 			=    model.transformer.wte
           __snake_case       :				int 			=    copy.deepcopy(model.transformer.wte.weight			)
           return secondary_learner








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__			:   Optional[int]      			=    {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['ErnieMTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   Dict      			=    [
					    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ErnieMForMultipleChoice',
					    'ErnieMForQuestionAnswering',
					    'ErnieMForSequenceClassification',
					    'ErnieMForTokenClassification',
					    'ErnieMModel',
					    'ErnieMPreTrainedModel',
					    'ErnieMForInformationExtraction',
					]


if TYPE_CHECKING:
					from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ernie_m import ErnieMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie_m import (
										    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieMForInformationExtraction,
										    ErnieMForMultipleChoice,
										    ErnieMForQuestionAnswering,
										    ErnieMForSequenceClassification,
										    ErnieMForTokenClassification,
										    ErnieMModel,
										    ErnieMPreTrainedModel,
										)


else:
					import sys

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