
'''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_           =    logging.getLogger(__name__)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    random.seed(__SCREAMING_SNAKE_CASE							)
    np.random.seed(__SCREAMING_SNAKE_CASE							)
    torch.manual_seed(__SCREAMING_SNAKE_CASE							)
    torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE="gpt2"							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE,		output_hidden_states=__SCREAMING_SNAKE_CASE							)
    torch.save(model.state_dict(),		model_name + "local.pt"							)
    return model

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Union[str, Any]        =    copy.deepcopy(__SCREAMING_SNAKE_CASE							)
    model.to(__SCREAMING_SNAKE_CASE							)

    snake_case_			:     List[Any]        =    ["bias", "LayerNorm.weight"]
    snake_case_			:     Union[str, 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(__SCREAMING_SNAKE_CASE,		lr=5E-5,		eps=1E-8							)
    snake_case_			:     Optional[int]        =    get_linear_schedule_with_warmup(__SCREAMING_SNAKE_CASE,		0,		__SCREAMING_SNAKE_CASE							)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     str        =    np.array(__SCREAMING_SNAKE_CASE							).mean()
    snake_case_			:     List[str]        =    np.array(__SCREAMING_SNAKE_CASE							).std()
    snake_case_			:     str        =    (real_perps - avg) / std
    snake_case_			:     List[str]        =    list(zip(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)							)
    joblib.dump(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE="dev.jbl",		__SCREAMING_SNAKE_CASE=recopy_gpta,		):
    """simple docstring"""






    snake_case_			:     str        =    []
    snake_case_			:     Dict        =    []
    snake_case_			:     Dict        =    []

    # Initialize the transformer model
    snake_case_			:     int        =    copy.deepcopy(__SCREAMING_SNAKE_CASE							)
    orig_model.to(device="cpu"							)
    torch.cuda.empty_cache()

    # Compute perplexity of initial transformer model for comparison
    model.train()
    snake_case_      ,			snake_case_      ,			snake_case_			:     Any        =    recopy_model(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

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

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

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        snake_case_      ,			snake_case_      ,			snake_case_			:     List[Any]        =    recopy_model(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

        past_perps.append(past_perp.item()							)
        real_perps.append(orig_perp - real_perp.item()							)
        contexts.append(np.array(context.cpu()							)							)

    intermittent_save(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl",		__SCREAMING_SNAKE_CASE=1_0_0,		__SCREAMING_SNAKE_CASE=1_0_2_6,		__SCREAMING_SNAKE_CASE=True							):
    """simple docstring"""






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

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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=5_0,		__SCREAMING_SNAKE_CASE="secondary_learner.pt"							):
    """simple docstring"""






    snake_case_			:     Dict        =    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_			:     List[str]        =    DataLoader(__SCREAMING_SNAKE_CASE,		shuffle=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Optional[int]        =    DataLoader(__SCREAMING_SNAKE_CASE,		shuffle=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE							)

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

                secondary_learner.eval()
                snake_case_			:     List[str]        =    0.0
                snake_case_			:     Any        =    0.0
                snake_case_			:     Dict        =    []
                snake_case_			:     Optional[Any]        =    []
                # Compute performance of the secondary learner after this batch
                for stepa, batcha in enumerate(__SCREAMING_SNAKE_CASE							):
                    snake_case_			:     Any        =    batcha[0].to(__SCREAMING_SNAKE_CASE							)
                    snake_case_			:     Dict        =    batcha[1].to(__SCREAMING_SNAKE_CASE							)
                    snake_case_			:     Dict        =    secondary_learner(__SCREAMING_SNAKE_CASE							)
                    q_lossa += test_loss(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							).item()
                    sum_qa += torch.sum(__SCREAMING_SNAKE_CASE							).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(__SCREAMING_SNAKE_CASE							)
                print(
                    "Epoch: ",		__SCREAMING_SNAKE_CASE,		"step: ",		__SCREAMING_SNAKE_CASE,		"Avg. q:",		sum_qa / len(__SCREAMING_SNAKE_CASE							),		"Train Loss: ",		__SCREAMING_SNAKE_CASE,		"Test Loss: ",		__SCREAMING_SNAKE_CASE,		)
                if q_lossa < best_test_loss:
                    joblib.dump((predicted, actual),		"pred_vs_actual.jbl"							)
                    torch.save(secondary_learner.state_dict(),		__SCREAMING_SNAKE_CASE							)
                    snake_case_			:     List[Any]        =    q_lossa

            secondary_learner.train()
    return secondary_learner






class  UpperCAmelCase_					(						nn.Module       ):





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





 def 							snake_case__		(     self			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    torch.max(self.conv(self.embeddings(lowercase_).squeeze(1).transpose(1			,							2))			,							2)[0]
     snake_case_			:     List[str]        =    self.fc(lowercase_)
     return qs.squeeze(1)





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






'''simple docstring'''
import numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


# kernel to be applied
a_           =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




