



'''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


_UpperCamelCase											=			logging.getLogger(__name__)


def   lowercase_	(   lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  List[Any] ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  Tuple="gpt2" ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  str ):

			"""simple docstring"""





			__UpperCAmelCase   :      str										=     copy.deepcopy(lowerCAmelCase__ )
			model.to(lowerCAmelCase__ )

			__UpperCAmelCase   :      Dict										=     ["""bias""", """LayerNorm.weight"""]
			__UpperCAmelCase   :      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},
			]
			__UpperCAmelCase   :      Optional[int]										=     AdamW(lowerCAmelCase__			,							lr=5E-5			,							eps=1E-8 )
			__UpperCAmelCase   :      List[str]										=     get_linear_schedule_with_warmup(lowerCAmelCase__			,							0			,							lowerCAmelCase__ )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Optional[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Dict										=     np.array(lowerCAmelCase__ ).mean()
			__UpperCAmelCase   :      Optional[Any]										=     np.array(lowerCAmelCase__ ).std()
			__UpperCAmelCase   :      str										=     (real_perps - avg) / std
			__UpperCAmelCase   :      Optional[Any]										=     list(zip(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ ) )
			joblib.dump(lowerCAmelCase__			,							lowerCAmelCase__ )


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

			"""simple docstring"""





			__UpperCAmelCase   :      Union[str, Any]										=     []
			__UpperCAmelCase   :      Dict										=     []
			__UpperCAmelCase   :      Union[str, Any]										=     []

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

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

			for step in tqdm(range(lowerCAmelCase__ ) ):
						__UpperCAmelCase   :      Union[str, Any]										=     torch.zeros((1, context_len)			,							dtype=torch.long			,							device=lowerCAmelCase__ )
						__UpperCAmelCase   :      Dict										=     random.choice(lowerCAmelCase__ )
						__UpperCAmelCase   :      Optional[int]										=     random.randint(0			,							len(story[0] ) - context_len - 1 )
						__UpperCAmelCase   :      Union[str, Any]										=     story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						__UpperCAmelCase   :      List[Any]										=     model(lowerCAmelCase__			,							labels=lowerCAmelCase__ )
						__UpperCAmelCase   :      Any										=     outputs[0]
						__UpperCAmelCase   :      Optional[int]										=     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
						__UpperCAmelCase   :      Dict										=     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
						__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     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__	:  int="data/tokenized_stories_train_wikitext103.jbl"			,							lowerCAmelCase__	:  int=100			,							lowerCAmelCase__	:  int=1026			,							lowerCAmelCase__	:  List[str]=True ):

			"""simple docstring"""





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

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


def   lowercase_	(   lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Any=50			,							lowerCAmelCase__	:  Union[str, Any]="secondary_learner.pt" ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     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
			__UpperCAmelCase   :      List[Any]										=     train_dataset[:512]
			__UpperCAmelCase   :      Optional[int]										=     train_dataset[512:]
			__UpperCAmelCase   :      Optional[Any]										=     DataLoader(lowerCAmelCase__			,							shuffle=lowerCAmelCase__			,							batch_size=lowerCAmelCase__ )
			__UpperCAmelCase   :      Optional[Any]										=     DataLoader(lowerCAmelCase__			,							shuffle=lowerCAmelCase__			,							batch_size=lowerCAmelCase__ )

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

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

									secondary_learner.train()
			return secondary_learner






class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     int:

					'''simple docstring'''
					super(__UpperCAmelCase ,       self				).__init__()
					__UpperCAmelCase   :      Any										=     model.transformer.wte
					__UpperCAmelCase   :      Optional[int]										=     copy.deepcopy(model.transformer.wte.weight				)
					__UpperCAmelCase   :      Optional[int]										=     nn.Convad(self.embeddings.weight.size(1				) ,       256 ,       3 ,       padding=1				)
					__UpperCAmelCase   :      Optional[Any]										=     nn.Sequential(nn.Linear(256 ,       32				) ,       nn.Dropout(p=0.1				) ,       nn.Linear(32 ,       32				) ,       nn.Linear(32 ,       1				)				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     torch.max(self.conv(self.embeddings(__UpperCAmelCase				).squeeze(1				).transpose(1 ,       2				)				) ,       2				)[0]
					__UpperCAmelCase   :      str										=     self.fc(__UpperCAmelCase				)
					return qs.squeeze(1				)





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     cls(__UpperCAmelCase				)  # this calls __init__
					__UpperCAmelCase   :      Optional[int]										=     torch.load(__UpperCAmelCase				)
					secondary_learner.load_state_dict(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     model.transformer.wte
					__UpperCAmelCase   :      str										=     copy.deepcopy(model.transformer.wte.weight				)
					return secondary_learner









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




