






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_   :	int							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__="gpt2"	):


		'''simple docstring'''

		__A    =							GPTaLMHeadModel.from_pretrained(lowerCAmelCase__					,     output_hidden_states=lowerCAmelCase__	)
		torch.save(model.state_dict()					,     model_name + "local.pt"	)
		return model



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							copy.deepcopy(lowerCAmelCase__	)
		model.to(lowerCAmelCase__	)

		__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(lowerCAmelCase__					,     lr=5E-5					,     eps=1E-8	)
		__A    =							get_linear_schedule_with_warmup(lowerCAmelCase__					,     0					,     lowerCAmelCase__	)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.array(lowerCAmelCase__	).mean()
		__A    =							np.array(lowerCAmelCase__	).std()
		__A    =							(real_perps - avg) / std
		__A    =							list(zip(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)	)
		joblib.dump(lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__="dev.jbl"					,     lowerCAmelCase__=recopy_gpta					,     ):


		'''simple docstring'''

		__A    =							[]
		__A    =							[]
		__A    =							[]

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

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

		for step in tqdm(range(lowerCAmelCase__	)	):
				__A    =							torch.zeros((1, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
				__A    =							random.choice(lowerCAmelCase__	)
				__A    =							random.randint(0					,     len(story[0]	) - context_len - 1	)
				__A    =							story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				__A    =							outputs[0]
				__A    =							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
				__A    =							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
				__A ,   __A ,   __A    =							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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"					,     lowerCAmelCase__=100					,     lowerCAmelCase__=1026					,     lowerCAmelCase__=True	):


		'''simple docstring'''

		__A    =							joblib.load(lowerCAmelCase__	)
		print("data loaded"	)
		__A    =							[]
		if trim:
				for i, example in enumerate(lowerCAmelCase__	):
						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(lowerCAmelCase__	) >= number:
								break
				__A    =							[]
				for j in range(i + 1					,     len(lowerCAmelCase__	)	):
						if len(data[j][0]	) > min_len:
								train_data.append(data[j]	)
		else:
				__A    =							data[0:number]
				__A    =							data[number:]

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=50					,     lowerCAmelCase__="secondary_learner.pt"	):


		'''simple docstring'''

		__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[:512]
		__A    =							train_dataset[512:]
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		# secondary learner model set up
		__A    =							nn.MSELoss()
		__A    =							nn.MSELoss(reduction="sum"	)
		secondary_learner.to(lowerCAmelCase__	)
		__A    =							torch.optim.Adam(secondary_learner.parameters()					,     lr=0.00_001	)
		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(lowerCAmelCase__	)	):
				__A    =							0.0
				secondary_learner.train()
				for step, batch in enumerate(lowerCAmelCase__	):
						__A    =							batch[0].to(lowerCAmelCase__	)
						__A    =							batch[1].to(lowerCAmelCase__	)
						__A    =							secondary_learner(lowerCAmelCase__	)
						q_optimizer.zero_grad()
						__A    =							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__	)):
								__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(lowerCAmelCase__	):
										__A    =							batcha[0].to(lowerCAmelCase__	)
										__A    =							batcha[1].to(lowerCAmelCase__	)
										__A    =							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__	)
										__A    =							q_lossa

						secondary_learner.train()
		return secondary_learner





class        a__      (	nn.Module		):






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






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





"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs