



'''simple docstring'''

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	self.config_class(**self.inputs_dict      )
							self.parent.assertTrue(hasattr(A_	,       """hidden_sizes"""      )      )
							self.parent.assertTrue(hasattr(A_	,       """num_encoder_blocks"""      )      )







class __snake_case:


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_=13	,       A_=64	,       A_=3	,       A_=4	,       A_=[2, 2, 2, 2]	,       A_=[8, 4, 2, 1]	,       A_=[16, 32, 64, 128]	,       A_=[1, 4, 8, 16]	,       A_=False	,       A_=True	,       A_="gelu"	,       A_=0.1	,       A_=0.0_2	,       A_=3	,       A_=None	,       )	->	Any:
							lowerCAmelCase    =	parent
							lowerCAmelCase    =	batch_size
							lowerCAmelCase    =	image_size
							lowerCAmelCase    =	num_channels
							lowerCAmelCase    =	num_encoder_blocks
							lowerCAmelCase    =	sr_ratios
							lowerCAmelCase    =	depths
							lowerCAmelCase    =	hidden_sizes
							lowerCAmelCase    =	downsampling_rates
							lowerCAmelCase    =	is_training
							lowerCAmelCase    =	use_labels
							lowerCAmelCase    =	hidden_act
							lowerCAmelCase    =	hidden_dropout_prob
							lowerCAmelCase    =	initializer_range
							lowerCAmelCase    =	num_labels
							lowerCAmelCase    =	scope


						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

							lowerCAmelCase    =	None
							if self.use_labels:
								lowerCAmelCase    =	ids_tensor([self.batch_size, self.image_size, self.image_size]	,       self.num_labels      )

							lowerCAmelCase    =	PoolFormerConfig(
							    image_size=self.image_size	,       num_channels=self.num_channels	,       num_encoder_blocks=self.num_encoder_blocks	,       depths=self.depths	,       hidden_sizes=self.hidden_sizes	,       hidden_act=self.hidden_act	,       hidden_dropout_prob=self.hidden_dropout_prob	,       initializer_range=self.initializer_range	,       )

							return config, pixel_values, labels


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	List[Any]:
							lowerCAmelCase    =	PoolFormerModel(config=A_      )
							model.to(A_      )
							model.eval()
							lowerCAmelCase    =	model(A_      )
							lowerCAmelCase    =	lowerCAmelCase    =	self.image_size // 3_2.0
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )




						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	self.prepare_config_and_inputs()
							lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	config_and_inputs
							lowerCAmelCase    =	{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Any           =		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						UpperCAmelCase							:    Optional[Any]           =		(
						    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase							:    Optional[Any]           =		False
						UpperCAmelCase							:    Union[str, Any]           =		False
						UpperCAmelCase							:    Union[str, Any]           =		False
						UpperCAmelCase							:    Tuple           =		False
						UpperCAmelCase							:    Union[str, Any]           =		False


						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	PoolFormerModelTester(self      )
							lowerCAmelCase    =	PoolFormerConfigTester(self	,       config_class=A_      )


						def 				__snake_case    (					self      )	->	Union[str, Any]:
							self.config_tester.run_common_tests()


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						@unittest.skip("""PoolFormer does not use inputs_embeds"""      )
						def 				__snake_case    (					self      )	->	Dict:
							pass


						@unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""      )
						def 				__snake_case    (					self      )	->	Tuple:
							pass


						def 				__snake_case    (					self      )	->	Tuple:
							def check_hidden_states_output(A_	,       A_	,       A_      ):
								lowerCAmelCase    =	model_class(A_      )
								model.to(A_      )
								model.eval()

								with torch.no_grad():
									lowerCAmelCase    =	model(**self._prepare_for_class(A_	,       A_      )      )

								lowerCAmelCase    =	outputs.hidden_states

								lowerCAmelCase    =	self.model_tester.num_encoder_blocks
								self.assertEqual(len(A_      )	,       A_      )

								# verify the first hidden states (first block)
								self.assertListEqual(
								    list(hidden_states[0].shape[-3:]      )	,       [
								        self.model_tester.hidden_sizes[0],
								        self.model_tester.image_size // 4,
								        self.model_tester.image_size // 4,
								    ]	,       )

							lowerCAmelCase,   lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase    =	True
								check_hidden_states_output(A_	,       A_	,       A_      )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase    =	True

								check_hidden_states_output(A_	,       A_	,       A_      )


						def 				__snake_case    (					self      )	->	List[str]:
							if not self.model_tester.is_training:
								return

							lowerCAmelCase,   lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase    =	True

							for model_class in self.all_model_classes:
								if model_class in get_values(A_      ):
									continue
								lowerCAmelCase    =	model_class(A_      )
								model.to(A_      )
								model.train()
								lowerCAmelCase    =	self._prepare_for_class(A_	,       A_	,       return_labels=A_      )
								lowerCAmelCase    =	model(**A_      ).loss
								loss.backward()


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase,   lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase    =	model_class(A_      )
								lowerCAmelCase    =	inspect.signature(model.forward      )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								lowerCAmelCase    =	[*signature.parameters.keys()]

								lowerCAmelCase    =	["""pixel_values"""]
								self.assertListEqual(arg_names[:1]	,       A_      )




						@slow
						def 				__snake_case    (					self      )	->	Optional[int]:
							for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase    =	PoolFormerModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )







def 		_snake_case		(					)			->						Tuple:







	"""simple docstring"""
	lowerCAmelCase    =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
	return image





@require_torch
class __snake_case(      unittest.TestCase					):


						'''simple docstring'''



						@slow
						def 				__snake_case    (					self      )	->	Union[str, Any]:
							lowerCAmelCase    =	PoolFormerImageProcessor()
							lowerCAmelCase    =	PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""      ).to(A_      )

							lowerCAmelCase    =	image_processor(images=prepare_img()	,       return_tensors="""pt"""      ).to(A_      )

							# forward pass
							with torch.no_grad():
								lowerCAmelCase    =	model(**A_      )

							# verify the logits
							lowerCAmelCase    =	torch.Size((1, 1000)      )
							self.assertEqual(outputs.logits.shape	,       A_      )

							lowerCAmelCase    =	torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]      ).to(A_      )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       A_	,       atol=1e-4      )      )




'''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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Dict:







	"""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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							)			->						int:







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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int="gpt2"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE							,		output_hidden_states=_SCREAMING_SNAKE_CASE							)
	torch.save(model.state_dict()							,		model_name + """local.pt"""							)
	return model






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase    =	copy.deepcopy(_SCREAMING_SNAKE_CASE							)
	model.to(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase    =	["""bias""", """LayerNorm.weight"""]
	lowerCAmelCase    =	[
	    {
	        """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},
	]
	lowerCAmelCase    =	AdamW(_SCREAMING_SNAKE_CASE							,		lr=5E-5							,		eps=1E-8							)
	lowerCAmelCase    =	get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE							,		0							,		_SCREAMING_SNAKE_CASE							)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Any							)			->						List[str]:







	"""simple docstring"""
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).mean()
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).std()
	lowerCAmelCase    =	(real_perps - avg) / std
	lowerCAmelCase    =	list(zip(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)
	joblib.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Tuple="dev.jbl"							,		_SCREAMING_SNAKE_CASE     :						List[Any]=recopy_gpta							,		)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]

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

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

	for step in tqdm(range(_SCREAMING_SNAKE_CASE							)							):
		lowerCAmelCase    =	torch.zeros((1, context_len)							,		dtype=torch.long							,		device=_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	random.choice(_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	random.randint(0							,		len(story[0]							) - context_len - 1							)
		lowerCAmelCase    =	story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		lowerCAmelCase    =	model(_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	outputs[0]
		lowerCAmelCase    =	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
		lowerCAmelCase    =	compute_perplexity(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1_000 == 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
		lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						int="data/tokenized_stories_train_wikitext103.jbl"							,		_SCREAMING_SNAKE_CASE     :						Any=100							,		_SCREAMING_SNAKE_CASE     :						Optional[int]=1_026							,		_SCREAMING_SNAKE_CASE     :						List[str]=True							)			->						Optional[Any]:







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

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Any							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						str=50							,		_SCREAMING_SNAKE_CASE     :						Tuple="secondary_learner.pt"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	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
	lowerCAmelCase    =	train_dataset[:512]
	lowerCAmelCase    =	train_dataset[512:]
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)

	# secondary learner model set up
	lowerCAmelCase    =	nn.MSELoss()
	lowerCAmelCase    =	nn.MSELoss(reduction="""sum"""							)
	secondary_learner.to(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	torch.optim.Adam(secondary_learner.parameters()							,		lr=0.0_0001							)
	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
	lowerCAmelCase    =	float("""inf"""							)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(_SCREAMING_SNAKE_CASE							)							):
		lowerCAmelCase    =	0.0
		secondary_learner.train()
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase    =	batch[0].to(_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	batch[1].to(_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	secondary_learner(_SCREAMING_SNAKE_CASE							)
			q_optimizer.zero_grad()
			lowerCAmelCase    =	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							)):
				lowerCAmelCase    =	tr_q_loss / (step + 1)

				secondary_learner.eval()
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	[]
				lowerCAmelCase    =	[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(_SCREAMING_SNAKE_CASE							):
					lowerCAmelCase    =	batcha[0].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	batcha[1].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	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							)
					lowerCAmelCase    =	q_lossa

			secondary_learner.train()
	return secondary_learner





class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	Dict:
							super(A_	,       self      ).__init__()
							lowerCAmelCase    =	model.transformer.wte
							lowerCAmelCase    =	copy.deepcopy(model.transformer.wte.weight      )
							lowerCAmelCase    =	nn.Convad(self.embeddings.weight.size(1      )	,       256	,       3	,       padding=1      )
							lowerCAmelCase    =	nn.Sequential(nn.Linear(256	,       32      )	,       nn.Dropout(p=0.1      )	,       nn.Linear(32	,       32      )	,       nn.Linear(32	,       1      )      )


						def 				__snake_case    (					self	,       A_      )	->	Optional[int]:
							lowerCAmelCase    =	torch.max(self.conv(self.embeddings(A_      ).squeeze(1      ).transpose(1	,       2      )      )	,       2      )[0]
							lowerCAmelCase    =	self.fc(A_      )
							return qs.squeeze(1      )




						@classmethod
						def 				__snake_case    (					cls	,       A_	,       A_      )	->	str:
							lowerCAmelCase    =	cls(A_      )  # this calls __init__
							lowerCAmelCase    =	torch.load(A_      )
							secondary_learner.load_state_dict(A_      )
							lowerCAmelCase    =	model.transformer.wte
							lowerCAmelCase    =	copy.deepcopy(model.transformer.wte.weight      )
							return secondary_learner