

'''simple docstring'''




import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
      from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import SwinaSRImageProcessor






class   _SCREAMING_SNAKE_CASE    :



 def __init__(    self		:    List[str] ,      a__		:    Union[str, Any] ,      a__		:    Union[str, Any]=13 ,      a__		:    str=32 ,      a__		:    Optional[int]=1 ,      a__		:    int=3 ,      a__		:    int=16 ,      a__		:    int=[1, 2, 1] ,      a__		:    Optional[Any]=[2, 2, 4] ,      a__		:    str=2 ,      a__		:    Any=2.0 ,      a__		:    Optional[Any]=True ,      a__		:    int=0.0 ,      a__		:    str=0.0 ,      a__		:    Any=0.1 ,      a__		:    str="gelu" ,      a__		:    int=False ,      a__		:    Optional[Any]=True ,      a__		:    Optional[int]=0.02 ,      a__		:    Any=1E-5 ,      a__		:    Optional[Any]=True ,      a__		:    List[Any]=None ,      a__		:    Any=False ,      a__		:    int=2 ,      ):
      __magic_name__						   =				parent
      __magic_name__						   =				batch_size
      __magic_name__						   =				image_size
      __magic_name__						   =				patch_size
      __magic_name__						   =				num_channels
      __magic_name__						   =				embed_dim
      __magic_name__						   =				depths
      __magic_name__						   =				num_heads
      __magic_name__						   =				window_size
      __magic_name__						   =				mlp_ratio
      __magic_name__						   =				qkv_bias
      __magic_name__						   =				hidden_dropout_prob
      __magic_name__						   =				attention_probs_dropout_prob
      __magic_name__						   =				drop_path_rate
      __magic_name__						   =				hidden_act
      __magic_name__						   =				use_absolute_embeddings
      __magic_name__						   =				patch_norm
      __magic_name__						   =				layer_norm_eps
      __magic_name__						   =				initializer_range
      __magic_name__						   =				is_training
      __magic_name__						   =				scope
      __magic_name__						   =				use_labels
      __magic_name__						   =				upscale

      # here we set some attributes to make tests pass
      __magic_name__						   =				len(A_			)
      __magic_name__						   =				embed_dim
      __magic_name__						   =				(image_size // patch_size) ** 2



 def  snake_case__  (    self		:    Any			):
      __magic_name__						   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      __magic_name__						   =				None
      if self.use_labels:
           __magic_name__						   =				ids_tensor([self.batch_size] ,      self.type_sequence_label_size			)

      __magic_name__						   =				self.get_config()

      return config, pixel_values, labels



 def  snake_case__  (    self		:    Tuple			):
      return SwinaSRConfig(
          image_size=self.image_size ,      patch_size=self.patch_size ,      num_channels=self.num_channels ,      embed_dim=self.embed_dim ,      depths=self.depths ,      num_heads=self.num_heads ,      window_size=self.window_size ,      mlp_ratio=self.mlp_ratio ,      qkv_bias=self.qkv_bias ,      hidden_dropout_prob=self.hidden_dropout_prob ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob ,      drop_path_rate=self.drop_path_rate ,      hidden_act=self.hidden_act ,      use_absolute_embeddings=self.use_absolute_embeddings ,      path_norm=self.patch_norm ,      layer_norm_eps=self.layer_norm_eps ,      initializer_range=self.initializer_range ,      upscale=self.upscale ,      )



 def  snake_case__  (    self		:    Optional[int] ,      a__		:    Optional[Any] ,      a__		:    List[str] ,      a__		:    Tuple			):
      __magic_name__						   =				SwinaSRModel(config=A_			)
      model.to(A_			)
      model.eval()
      __magic_name__						   =				model(A_			)

      self.parent.assertEqual(
          result.last_hidden_state.shape ,      (self.batch_size, self.embed_dim, self.image_size, self.image_size)			)



 def  snake_case__  (    self		:    Tuple ,      a__		:    Tuple ,      a__		:    str ,      a__		:    Union[str, Any]			):
      __magic_name__						   =				SwinaSRForImageSuperResolution(A_			)
      model.to(A_			)
      model.eval()
      __magic_name__						   =				model(A_			)

      __magic_name__						   =				self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape ,      (self.batch_size, self.num_channels, expected_image_size, expected_image_size)			)



 def  snake_case__  (    self		:    Any			):
      __magic_name__						   =				self.prepare_config_and_inputs()
      __magic_name__   ,  __magic_name__   ,  __magic_name__						   =				config_and_inputs
      __magic_name__						   =				{'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class   _SCREAMING_SNAKE_CASE    (    _lowerCAmelCase		,_lowerCAmelCase		,unittest.TestCase	):
 __SCREAMING_SNAKE_CASE     :int			       =     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE     :int			       =     {"feature-extraction": SwinaSRModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE     :List[str]			       =     False
 __SCREAMING_SNAKE_CASE     :List[Any]			       =     False
 __SCREAMING_SNAKE_CASE     :List[str]			       =     False
 __SCREAMING_SNAKE_CASE     :int			       =     False
 __SCREAMING_SNAKE_CASE     :Tuple			       =     False



 def  snake_case__  (    self		:    Tuple			):
      __magic_name__						   =				SwinaSRModelTester(self			)
      __magic_name__						   =				ConfigTester(self ,      config_class=A_ ,      embed_dim=37			)



 def  snake_case__  (    self		:    Optional[Any]			):
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



 def  snake_case__  (    self		:    Any			):
      __magic_name__						   =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_			)



 def  snake_case__  (    self		:    Tuple			):
      __magic_name__						   =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*A_			)



 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''			)
 def  snake_case__  (    self		:    Any			):
      pass



 @unittest.skip(reason='''Swin2SR does not use inputs_embeds'''			)
 def  snake_case__  (    self		:    List[str]			):
      pass



 @unittest.skip(reason='''Swin2SR does not support training yet'''			)
 def  snake_case__  (    self		:    List[str]			):
      pass



 @unittest.skip(reason='''Swin2SR does not support training yet'''			)
 def  snake_case__  (    self		:    List[Any]			):
      pass



 def  snake_case__  (    self		:    str			):
      __magic_name__   ,  __magic_name__						   =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           __magic_name__						   =				model_class(A_			)
           self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)			)
           __magic_name__						   =				model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(A_ ,      nn.Linear			)			)



 def  snake_case__  (    self		:    int			):
      __magic_name__   ,  __magic_name__						   =				self.model_tester.prepare_config_and_inputs_for_common()

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

           __magic_name__						   =				['''pixel_values''']
           self.assertListEqual(arg_names[:1] ,      A_			)



 @slow
 def  snake_case__  (    self		:    Union[str, Any]			):
      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						   =				SwinaSRModel.from_pretrained(A_			)
           self.assertIsNotNone(A_			)



 def  snake_case__  (    self		:    Any			):
      __magic_name__   ,  __magic_name__						   =				self.model_tester.prepare_config_and_inputs_for_common()

      __magic_name__						   =				_config_zero_init(A_			)
      for model_class in self.all_model_classes:
           __magic_name__						   =				model_class(config=A_			)
           for name, param in model.named_parameters():
                if "logit_scale" in name:
                     continue
                if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1E9).round() / 1E9).item() ,      [0.0, 1.0] ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,      )



 def  snake_case__  (    self		:    str			):
      __magic_name__   ,  __magic_name__						   =				self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						   =				True

      for model_class in self.all_model_classes:
           __magic_name__						   =				True
           __magic_name__						   =				False
           __magic_name__						   =				True
           __magic_name__						   =				model_class(A_			)
           model.to(A_			)
           model.eval()
           with torch.no_grad():
                __magic_name__						   =				model(**self._prepare_for_class(A_ ,      A_			)			)
           __magic_name__						   =				outputs.attentions
           __magic_name__						   =				len(self.model_tester.depths			)
           self.assertEqual(len(A_			) ,      A_			)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           __magic_name__						   =				True
           __magic_name__						   =				config.window_size**2
           __magic_name__						   =				model_class(A_			)
           model.to(A_			)
           model.eval()
           with torch.no_grad():
                __magic_name__						   =				model(**self._prepare_for_class(A_ ,      A_			)			)
           __magic_name__						   =				outputs.attentions
           self.assertEqual(len(A_			) ,      A_			)

           self.assertListEqual(
               list(attentions[0].shape[-3:]			) ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,      )
           __magic_name__						   =				len(A_			)

           # Check attention is always last and order is fine
           __magic_name__						   =				True
           __magic_name__						   =				True
           __magic_name__						   =				model_class(A_			)
           model.to(A_			)
           model.eval()
           with torch.no_grad():
                __magic_name__						   =				model(**self._prepare_for_class(A_ ,      A_			)			)

           self.assertEqual(out_len + 1 ,      len(A_			)			)

           __magic_name__						   =				outputs.attentions

           self.assertEqual(len(A_			) ,      A_			)

           self.assertListEqual(
               list(self_attentions[0].shape[-3:]			) ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,      )






@require_vision
@require_torch
@slow
class   _SCREAMING_SNAKE_CASE    (    unittest.TestCase	):



 def  snake_case__  (    self		:    Dict			):
      __magic_name__						   =				SwinaSRImageProcessor()
      __magic_name__						   =				SwinaSRForImageSuperResolution.from_pretrained('''caidas/swin2SR-classical-sr-x2-64'''			).to(A_			)

      __magic_name__						   =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      __magic_name__						   =				processor(images=A_ ,      return_tensors='''pt'''			).to(A_			)

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

      # verify the logits
      __magic_name__						   =				torch.Size([1, 3, 976, 1296]			)
      self.assertEqual(outputs.reconstruction.shape ,      A_			)
      __magic_name__						   =				torch.tensor(
          [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]			).to(A_			)
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :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