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


UpperCamelCase													=		logging.getLogger(__name__)

def     lowercase_       (    _lowerCamelCase							: Optional[Any]):
    random.seed(_lowerCamelCase)
    np.random.seed(_lowerCamelCase)
    torch.manual_seed(_lowerCamelCase)
    torch.cuda.manual_seed_all(_lowerCamelCase)

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

def     lowercase_       (    _lowerCamelCase							: str="gpt2"):
    lowercase__				:     List[str]					      =       GPTaLMHeadModel.from_pretrained(_lowerCamelCase	,			output_hidden_states=_lowerCamelCase)
    torch.save(model.state_dict()	,			model_name + "local.pt")
    return model

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Dict):
    lowercase__				:     List[Any]					      =       copy.deepcopy(_lowerCamelCase)
    model.to(_lowerCamelCase)

    lowercase__				:     List[Any]					      =       ["bias", "LayerNorm.weight"]
    lowercase__				:     Dict					      =       [
        {
            "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__				:     List[Any]					      =       AdamW(_lowerCamelCase	,			lr=5E-5	,			eps=1E-8)
    lowercase__				:     str					      =       get_linear_schedule_with_warmup(_lowerCamelCase	,			0	,			_lowerCamelCase)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler

def     lowercase_       (    _lowerCamelCase							: Dict	,			_lowerCamelCase							: Any	,			_lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: List[str]):
    lowercase__				:     str					      =       np.array(_lowerCamelCase).mean()
    lowercase__				:     str					      =       np.array(_lowerCamelCase).std()
    lowercase__				:     Dict					      =       (real_perps - avg) / std
    lowercase__				:     Optional[Any]					      =       list(zip(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase))
    joblib.dump(_lowerCamelCase	,			_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: Optional[int]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: int	,			_lowerCamelCase							: int	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]	,			_lowerCamelCase							: List[Any]="dev.jbl"	,			_lowerCamelCase							: Tuple=recopy_gpta	,			):
    lowercase__				:     str					      =       []
    lowercase__				:     Any					      =       []
    lowercase__				:     List[str]					      =       []

    # Initialize the transformer model
    lowercase__				:     str					      =       copy.deepcopy(_lowerCamelCase)
    orig_model.to(device="cpu")
    torch.cuda.empty_cache()

    # Compute perplexity of initial transformer model for comparison
    model.train()
    lowercase__      ,     lowercase__      ,     lowercase__				:     Optional[Any]					      =       recopy_model(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

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

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

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        lowercase__      ,     lowercase__      ,     lowercase__				:     Tuple					      =       recopy_model(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

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

    intermittent_save(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: Any="data/tokenized_stories_train_wikitext103.jbl"	,			_lowerCamelCase							: List[Any]=100	,			_lowerCamelCase							: List[str]=1026	,			_lowerCamelCase							: Union[str, Any]=True):
    lowercase__				:     Dict					      =       joblib.load(_lowerCamelCase)
    print("data loaded")
    lowercase__				:     Optional[int]					      =       []
    if trim:
        for i, example in enumerate(_lowerCamelCase):
            if len(example[0]) > min_len:
                lowercase__				:     List[str]					      =       random.randint(0	,			len(example[0]) - context_len - 1)
                objective_set.append(example[0, start : start + context_len])
            if len(_lowerCamelCase) >= number:
                break
        lowercase__				:     Any					      =       []
        for j in range(i + 1	,			len(_lowerCamelCase)):
            if len(data[j][0]) > min_len:
                train_data.append(data[j])
    else:
        lowercase__				:     Optional[Any]					      =       data[0:number]
        lowercase__				:     Optional[int]					      =       data[number:]

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

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: Optional[Any]	,			_lowerCamelCase							: Optional[Any]	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]=50	,			_lowerCamelCase							: List[str]="secondary_learner.pt"):
    lowercase__				:     Optional[int]					      =       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__				:     List[Any]					      =       train_dataset[:512]
    lowercase__				:     int					      =       train_dataset[512:]
    lowercase__				:     Optional[int]					      =       DataLoader(_lowerCamelCase	,			shuffle=_lowerCamelCase	,			batch_size=_lowerCamelCase)
    lowercase__				:     Any					      =       DataLoader(_lowerCamelCase	,			shuffle=_lowerCamelCase	,			batch_size=_lowerCamelCase)

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

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

            secondary_learner.train()
    return secondary_learner



class 						snake_case_ (	nn.Module ):






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






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  Any						)      ->		int:
       lowercase__				:     Any					      =       torch.max(self.conv(self.embeddings(lowercase_						).squeeze(1						).transpose(1			,		2						)						)			,		2						)[0]
       lowercase__				:     Union[str, Any]					      =       self.fc(lowercase_						)
       return qs.squeeze(1						)







   @classmethod
   def    __UpperCamelCase  (   cls       :  str			,		lowercase_       :  Tuple			,		lowercase_       :  List[Any]						)      ->		int:
       lowercase__				:     Optional[int]					      =       cls(lowercase_						)  # this calls __init__
       lowercase__				:     int					      =       torch.load(lowercase_						)
       secondary_learner.load_state_dict(lowercase_						)
       lowercase__				:     str					      =       model.transformer.wte
       lowercase__				:     List[Any]					      =       copy.deepcopy(model.transformer.wte.weight						)
       return secondary_learner




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       self.tokenizer(
           text=text if text is not None else features["words"]			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features["boxes"]			,		word_labels=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_						)} and {len(lowercase_						)}'''						)

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



