'''simple docstring'''




import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
  from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
  raise ImportError('You need to install the version of fairseq from the Mega repo!')




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Tuple:
           super().__init__()
           A_     =   mega_args
           A_     =   nn.Embedding(_SCREAMING_SNAKE_CASE    ,     self.mega_args.encoder_embed_dim )
           A_     =   nn.ModuleList([MegaEncoderLayer(self.mega_args ) for _ in range(_SCREAMING_SNAKE_CASE )] )
           A_     =   depth

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=0 )		->     Any:

           # Mega expects embeddings to be (time, batch, embedding size), but
           # Hugging Face returns tokens as (batch, time)
           if batch_first:
               A_     =   input_ids.T

           # to make things more confusing, Mega expects the attention mask to
           # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
           # which is the opposite of what HF returns
           if ignore_mask_value == 0:
               A_     =   1 - attention_mask

           # get token embeddings from IDs
           A_     =   self.embedding_layer(_SCREAMING_SNAKE_CASE )

           # pass through the Mega layers
           # input is (time, batch, encoder dim) and output is the same
           for encoder in self.encoders:
               A_     =   encoder(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

           # return according to the shape specified
           if batch_first:
               # (T, B, H) --> (B, T, H)
               return torch.transpose(_SCREAMING_SNAKE_CASE    ,     0    ,     1 )
           else:
               return embeds




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           super().__init__()
           A_     =   MegaLM(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
           A_     =   nn.Linear(mega_args.encoder_embed_dim    ,     _SCREAMING_SNAKE_CASE )
           A_     =   nn.Dropout(p=0.1 )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=0 )		->     List[str]:
           A_     =   self.mega(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
           return self.mlm_head(self.dropout(_SCREAMING_SNAKE_CASE ) )





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str],   _UpperCamelCase    :	Any,   _UpperCamelCase    :	str   )	->      str:
    with open(os.path.join(_UpperCamelCase,   '''model_args.pkl'''   ),   '''rb'''   ) as f:
        A_     =   pkl.load(_UpperCamelCase   )

    # load the original encoder
    A_     =   OriginalMegaForMaskedLM(**_UpperCamelCase   ).eval()

    # load its weights
    print(
        '''Original Mega encoder:''',   original_mlm.mega.load_state_dict(
            torch.load(os.path.join(_UpperCamelCase,   '''encoder_weights.pt'''   ),   map_location='''cpu'''   )   ),   )
    print(
        '''Original Mega MLM layer:''',   original_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(_UpperCamelCase,   '''mlm_head_weights.pt'''   ),   map_location='''cpu'''   )   ),   )

    # create a new config from the old one
    A_     =   MegaConfig(
        num_hidden_layers=mega_original_args['''depth'''],   vocab_size=mega_original_args['''vocab_size'''],   hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim,   shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim,   intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim,   ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim,   dropout_prob=mega_original_args['''mega_args'''].dropout,   attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout,   hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout,   activation=mega_original_args['''mega_args'''].activation_fn,   attention_activation=mega_original_args['''mega_args'''].attention_activation_fn,   bidirectional=mega_original_args['''mega_args'''].bidirectional,   use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0,   chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size,   truncation=mega_original_args['''mega_args'''].truncation_length,   normalization_type=mega_original_args['''mega_args'''].normalization_type,   normalize_before_mega=_UpperCamelCase,   norm_affine=_UpperCamelCase,   use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout,   relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias,   max_positions=mega_original_args['''mega_args'''].max_source_positions,   nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim,   normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before,   nffn_activation_dropout_prob=0.0,   add_token_type_embeddings=_UpperCamelCase,   add_lm_hidden_dense_layer=_UpperCamelCase,   )

    A_     =   MegaForMaskedLM(_UpperCamelCase   ).eval()

    # the originl checkpoint just uses nn.Embedding for the word embeddings
    # we use a wrapper module for embeddings to add support for positional embeddings
    A_     =   original_mlm.mega.embedding_layer.weight

    # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
    # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
    # also renaming previously confusing parameter names
    A_     =   original_mlm.mega.encoders.state_dict()
    A_     =   {}
    for module_name in original_state_dict.keys():
        A_     =   None
        # have to handle gamma, beta, and alpha differently due to their use
        # in multiple modules within the original repository;
        # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
        # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
        if "beta" in module_name:
            # EMA sub-layers were always called "move" in the original repo
            if "move.beta" in module_name:
                A_     =   module_name.replace('''move.beta''',   '''ema_gate.ema_expansion_matrix'''   )
            elif "mega_layer.beta" in module_name:
                A_     =   module_name.replace('''beta''',   '''qk_bias'''   )
            else:
                A_     =   module_name.replace('''beta''',   '''b_param'''   )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
        elif "gamma" in module_name:
            if "move.gamma" in module_name:
                A_     =   module_name.replace('''move.gamma''',   '''ema_gate.kernel_projection_matrix'''   )
            elif "mega_layer.gamma" in module_name:
                A_     =   module_name.replace('''gamma''',   '''qk_weight'''   )
            else:
                A_     =   module_name.replace('''gamma''',   '''g_param'''   )
        # alpha is used in EMA and positional bias; renaming to improve readability
        elif "move.alpha" in module_name:
            A_     =   module_name.replace('''move.alpha''',   '''ema_gate.decay_factor'''   )
        # delta is only used in EMA; renaming to improve readability
        elif "move.delta" in module_name:
            A_     =   module_name.replace('''move.delta''',   '''ema_gate.damping_factor'''   )
        # omega is only used in EMA; renaming to improve readability
        elif "omega" in module_name:
            A_     =   module_name.replace('''move.omega''',   '''ema_gate.residual_weight'''   )

        if new_module_name:
            A_     =   new_module_name

    if len(_UpperCamelCase   ) != 0:
        print(F'''Renaming these keys: {updated_keys.keys()}'''   )
    else:
        print('''No need to rename state dict entries'''   )
    for old, new in updated_keys.items():
        A_     =   original_state_dict.pop(_UpperCamelCase   )

    # now attempt to load the state dictionary with updated names
    # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
    print('''HF Mega encoder:''',   hf_mlm.mega.layers.load_state_dict(_UpperCamelCase   )   )

    # load the MLM head weights directly
    print(
        '''HF Mega MLM layer:''',   hf_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(_UpperCamelCase,   '''mlm_head_weights.pt'''   ),   map_location='''cpu'''   )   ),   )

    # test on a randomly generated input sequence
    A_     =   torch.randint(0,   hf_config.vocab_size,   size=(4, 2_56)   )
    A_     =   torch.ones_like(_UpperCamelCase   )
    # mask a few tokens to make sure masking is applied appropriately :)
    A_     =   0

    # run forward passes
    A_     =   original_mlm(_UpperCamelCase,   _UpperCamelCase,   batch_first=_UpperCamelCase,   ignore_mask_value=0   )
    A_     =   hf_mlm(_UpperCamelCase,   _UpperCamelCase   )[0]

    # print shapes and diff
    print(F'''original output {original_output.shape}'''   )
    print(F'''hf output {hf_output.shape}'''   )
    print(F'''max diff: {(original_output - hf_output).max()}'''   )  # 0.0
    A_     =   torch.allclose(_UpperCamelCase,   _UpperCamelCase,   atol=1E-3   )

    if success:
        print('''Yay!'''   )
        hf_mlm.save_pretrained(_UpperCamelCase   )
    else:
        raise RuntimeError(F'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''   )

    if includes_tokenizer:
        print('''Transferring tokenizer'''   )
        A_     =   AutoTokenizer.from_pretrained(_UpperCamelCase   )
        tokenizer.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
  __snake_case   :      int						    =  argparse.ArgumentParser()

  parser.add_argument(
      '--pretrained_checkpoint_path',
      default=None,
      type=str,
      required=True,
      help='Point to the directory containing your model weights using the official Mega repo',
  )

  parser.add_argument(
      '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
  )

  parser.add_argument(
      '--includes_tokenizer',
      action='store_true',
      help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
  )

  __snake_case   :      Dict						    =  parser.parse_args()

  convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)











'''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