code: 
  
  
  
  
  import argparse
  import os
  from functools import reduce
  
  import fairseq
  import torch
  from datasets import load_dataset
  
  from transformers import WavaVecaProcessor, logging
  from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig
  
  # Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
  from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
  from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel
  
  
  logging.set_verbosity_info()
  _lowerCamelCase							:		List[Any]										=						logging.get_logger(__name__)
  
  _lowerCamelCase							:		Union[str, Any]										=						{
      "post_extract_proj": "feature_projection.projection",
      "models.0.layer_norm": "feature_projection.layer_norm",
      "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
      "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
      "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
      "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
      "self_attn_layer_norm": "encoder.layers.*.layer_norm",
      "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
      "fc2": "encoder.layers.*.feed_forward.output_dense",
      "final_layer_norm": "encoder.layers.*.final_layer_norm",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "feature_projection.layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  _lowerCamelCase							:		Optional[Any]										=						[
      "lm_head",
  ]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		for attribute in key.split("."						):
  				_snake_case			 = getattr(lowercase_		,							lowercase_						)
  
  		if weight_type is not None:
  				_snake_case			 = getattr(lowercase_		,							lowercase_						).shape
  		else:
  				_snake_case			 = hf_pointer.shape
  
  		if hf_shape != value.shape:
  				raise ValueError(
  				    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
  				    f''' {value.shape} for {full_name}'''						)
  
  		if weight_type == "weight":
  				_snake_case			 = value
  		elif weight_type == "weight_g":
  				_snake_case			 = value
  		elif weight_type == "weight_v":
  				_snake_case			 = value
  		elif weight_type == "bias":
  				_snake_case			 = value
  		else:
  				_snake_case			 = value
  
  		logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = []
  		_snake_case			 = fairseq_model.state_dict()
  
  		if not is_headless:
  				_snake_case			 = hf_model.dataavec_audio.feature_extractor
  				_snake_case			 = hf_model.dataavec_audio.encoder.pos_conv_embed
  
  		else:
  				_snake_case			 = hf_model.feature_extractor
  				_snake_case			 = hf_model.encoder.pos_conv_embed
  
  		for name, value in fairseq_dict.items():
  				_snake_case			 = False
  				if "conv_layers" in name:
  						load_conv_layer(
  						    lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							)
  						_snake_case			 = True
  				elif "pos_conv" in name:
  						load_pos_conv_layer(
  						    lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							)
  						_snake_case			 = True
  				else:
  						for key, mapped_key in MAPPING.items():
  								if not is_headless:
  										_snake_case			 = "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
  								if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
  										_snake_case			 = True
  										if "*" in mapped_key:
  												_snake_case			 = name.split(lowercase_						)[0].split("."						)[-2]
  												_snake_case			 = mapped_key.replace("*"		,							lowercase_						)
  										if "weight_g" in name:
  												_snake_case			 = "weight_g"
  										elif "weight_v" in name:
  												_snake_case			 = "weight_v"
  										elif "bias" in name:
  												_snake_case			 = "bias"
  										elif "weight" in name:
  												# TODO: don't match quantizer.weight_proj
  												_snake_case			 = "weight"
  										else:
  												_snake_case			 = None
  										set_recursively(lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						)
  								continue
  				if not is_used:
  						unused_weights.append(lowercase_						)
  
  		logger.warning(f'''Unused weights: {unused_weights}'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = path.split("."						)
  		return reduce(lowercase_		,							lowercase_		,							lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = access_by_string(lowercase_		,							lowercase_						)
  		_snake_case			 = hf_weight.data
  
  		if fsq_value.shape != hf_value.shape:
  				raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''						)
  		_snake_case			 = fsq_value
  		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = full_name.split("conv_layers."						)[-1]
  		_snake_case			 = name.split("."						)
  		_snake_case			 = int(items[0]						)
  		_snake_case			 = int(items[1]						)
  
  		_snake_case			 = name.split("."						)[-1]
  		if type_id == 0:
  				_snake_case			 = "conv"
  		elif type_id == 2:
  				_snake_case			 = "layer_norm"
  		else:
  				unused_weights.append(lowercase_						)
  				return
  
  		set_weights(lowercase_		,							lowercase_		,							lowercase_		,							f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = full_name.split("pos_conv."						)[-1]
  		_snake_case			 = name.split("."						)
  		_snake_case			 = int(items[0]						)
  		_snake_case			 = int(items[1]						)
  
  		_snake_case			 = name.split("."						)[-1]
  		if type_id != 0:
  				unused_weights.append(lowercase_						)
  				return
  		else:
  				_snake_case			 = "conv"
  
  		set_weights(lowercase_		,							lowercase_		,							lowercase_		,							f'''layers.{layer_id}.{layer_type}.{weight_type}'''						)
  
  
  
  
  
  
  @torch.no_grad()
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_=None		,							lowercase_=None		,							lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if config_path is not None:
  				_snake_case			 = DataaVecAudioConfig.from_pretrained(lowercase_						)
  		else:
  				_snake_case			 = DataaVecAudioConfig()
  
  		if not is_finetuned:
  				# Modify final_proj layer name
  				_snake_case			 = DataaVecAudioModel(lowercase_						)
  				_snake_case			 = os.path.dirname(lowercase_						)
  
  				_snake_case			 = torch.load(lowercase_						)
  				_snake_case			 = state_dict["model"].pop("final_proj.0.weight"						)
  				_snake_case			 = state_dict["model"].pop("final_proj.0.bias"						)
  				_snake_case			 = os.path.join(lowercase_		,							"converted.pt"						)
  				torch.save(lowercase_		,							lowercase_						)
  		else:
  				_snake_case			 = DataaVecAudioForCTC(lowercase_						)
  				_snake_case			 = checkpoint_path
  
  		def load_dataavec(lowercase_						):
  				_snake_case ,					_snake_case ,					_snake_case			 = fairseq.checkpoint_utils.load_model_ensemble_and_task([path]						)
  				return model[0].eval()
  
  		_snake_case			 = load_dataavec(lowercase_						)
  
  		recursively_load_weights(lowercase_		,							lowercase_		,							not is_finetuned						)
  
  		_snake_case			 = WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"						)
  
  		_snake_case			 = load_dataset("patrickvonplaten/librispeech_asr_dummy"		,							"clean"		,							split="validation"						)
  		_snake_case			 = [x["array"] for x in ds[:4]["audio"]]
  
  		_snake_case			 = processor(lowercase_		,							return_tensors="pt"		,							padding=lowercase_						)
  
  		_snake_case			 = inputs.input_values
  		_snake_case			 = inputs.attention_mask
  		#    input_values = inputs.input_values[:, :-1]
  		#    attention_mask = inputs.attention_mask[:, :-1]
  
  		hf_wavavec.eval()
  		model.eval()
  		if is_finetuned:
  				_snake_case			 = model(source=lowercase_		,							padding_mask=(1 - attention_mask)		,							mask=lowercase_		,							features_only=lowercase_						)[
  				    "encoder_out"
  				].transpose(0		,							1						)
  				_snake_case			 = hf_wavavec(lowercase_		,							attention_mask=lowercase_						)["logits"]
  
  				_snake_case			 = torch.argmax(lowercase_		,							dim=-1						)
  				_snake_case			 = processor.batch_decode(lowercase_						)
  
  				print(f'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''						)
  		else:
  				_snake_case			 = model(source=lowercase_		,							padding_mask=(1 - attention_mask)		,							mask=lowercase_		,							features_only=lowercase_						)[
  				    "layer_results"
  				][-1][0].transpose(0		,							1						)
  				_snake_case			 = hf_wavavec(lowercase_		,							attention_mask=lowercase_						)["last_hidden_state"]
  
  		print(our_output.shape		,							their_output.shape						)
  		_snake_case			 = torch.max(torch.abs(our_output - their_output						)						).item()
  		print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
  		_snake_case			 = torch.allclose(lowercase_		,							lowercase_		,							atol=1e-3						)
  		print("Do both models output the same tensors?"		,							"🔥" if success else "💩"						)
  		if not success:
  				raise Exception("Something went wRoNg"						)
  
  		hf_wavavec.save_pretrained(lowercase_						)
  
  		if is_finetuned:
  				processor.save_pretrained(lowercase_						)
  		else:
  				processor.feature_extractor.save_pretrained(lowercase_						)
  
  
  if __name__ == "__main__":
  						_lowerCamelCase							:		Any										=						argparse.ArgumentParser()
  						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
  						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
  						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  						parser.add_argument(
  						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
  						)
  						_lowerCamelCase							:		Optional[Any]										=						parser.parse_args()
  						convert_wavaveca_checkpoint(
  						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  						)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''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
  
  
  _lowerCamelCase							:		str										=						logging.getLogger(__name__)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		random.seed(lowercase_						)
  		np.random.seed(lowercase_						)
  		torch.manual_seed(lowercase_						)
  		torch.cuda.manual_seed_all(lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		model.eval()
  		_snake_case			 = next(model.parameters()						).device
  		_snake_case			 = 1
  		_snake_case			 = torch.zeros((eval_batch_size, context_len)		,							dtype=torch.long		,							device=lowercase_						)
  		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  		_snake_case			 = torch.zeros(1		,							device=lowercase_						)
  		_snake_case			 = 0
  		for batch in eval_dataloader:
  				batch.to(lowercase_						)
  				# pad
  				context.zero_()
  				for i in range(lowercase_						):
  						_snake_case			 = batch[i]
  				_snake_case			 = model(lowercase_		,							labels=lowercase_						)
  				eval_loss += outputs[0].sum().item()
  				nb_eval_examples += batch.size(0						)
  		_snake_case			 = eval_loss / nb_eval_examples
  		_snake_case			 = torch.exp(lowercase_						)
  		model.train()
  		return perplexity
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_="gpt2"						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = GPTaLMHeadModel.from_pretrained(lowercase_		,							output_hidden_states=lowercase_						)
  		torch.save(model.state_dict()		,							model_name + "local.pt"						)
  		return model
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = copy.deepcopy(lowercase_						)
  		model.to(lowercase_						)
  
  		_snake_case			 = ["bias", "LayerNorm.weight"]
  		_snake_case			 = [
  		    {
  		        "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},
  		]
  		_snake_case			 = AdamW(lowercase_		,							lr=5e-5		,							eps=1e-8						)
  		_snake_case			 = get_linear_schedule_with_warmup(lowercase_		,							0		,							lowercase_						)
  		torch.cuda.empty_cache()
  		return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = np.array(lowercase_						).mean()
  		_snake_case			 = np.array(lowercase_						).std()
  		_snake_case			 = (real_perps - avg) / std
  		_snake_case			 = list(zip(lowercase_		,							lowercase_		,							lowercase_						)						)
  		joblib.dump(lowercase_		,							lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_="dev.jbl"		,							lowercase_=recopy_gpta		,							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = []
  		_snake_case			 = []
  		_snake_case			 = []
  
  		# Initialize the transformer model
  		_snake_case			 = copy.deepcopy(lowercase_						)
  		orig_model.to(device="cpu"						)
  		torch.cuda.empty_cache()
  
  		# Compute perplexity of initial transformer model for comparison
  		model.train()
  		_snake_case ,					_snake_case ,					_snake_case			 = recopy_model(lowercase_		,							lowercase_		,							lowercase_						)
  
  		for step in tqdm(range(lowercase_						)						):
  				_snake_case			 = torch.zeros((1, context_len)		,							dtype=torch.long		,							device=lowercase_						)
  				_snake_case			 = random.choice(lowercase_						)
  				_snake_case			 = random.randint(0		,							len(story[0]						) - context_len - 1						)
  				_snake_case			 = story[0][start : start + context_len]
  				lm_optimizer.zero_grad()
  				_snake_case			 = model(lowercase_		,							labels=lowercase_						)
  				_snake_case			 = outputs[0]
  				_snake_case			 = compute_perplexity(lowercase_		,							lowercase_		,							lowercase_						)
  				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
  				_snake_case			 = compute_perplexity(lowercase_		,							lowercase_		,							lowercase_						)
  
  				# Periodically save the stored (X, IG(X)) pairs
  				if step % 1000 == 0 and step > 1:
  						intermittent_save(lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						)
  
  				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  				_snake_case ,					_snake_case ,					_snake_case			 = recopy_model(lowercase_		,							lowercase_		,							lowercase_						)
  
  				past_perps.append(past_perp.item()						)
  				real_perps.append(orig_perp - real_perp.item()						)
  				contexts.append(np.array(context.cpu()						)						)
  
  		intermittent_save(lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_="data/tokenized_stories_train_wikitext103.jbl"		,							lowercase_=100		,							lowercase_=1026		,							lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = joblib.load(lowercase_						)
  		print("data loaded"						)
  		_snake_case			 = []
  		if trim:
  				for i, example in enumerate(lowercase_						):
  						if len(example[0]						) > min_len:
  								_snake_case			 = random.randint(0		,							len(example[0]						) - context_len - 1						)
  								objective_set.append(example[0, start : start + context_len]						)
  						if len(lowercase_						) >= number:
  								break
  				_snake_case			 = []
  				for j in range(i + 1		,							len(lowercase_						)						):
  						if len(data[j][0]						) > min_len:
  								train_data.append(data[j]						)
  		else:
  				_snake_case			 = data[0:number]
  				_snake_case			 = data[number:]
  
  		joblib.dump(lowercase_		,							"objective_set.jbl"						)
  		print("objective set saved"						)
  		return train_data, objective_set
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_=50		,							lowercase_="secondary_learner.pt"						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = 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
  		_snake_case			 = train_dataset[:512]
  		_snake_case			 = train_dataset[512:]
  		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  
  		# secondary learner model set up
  		_snake_case			 = nn.MSELoss()
  		_snake_case			 = nn.MSELoss(reduction="sum"						)
  		secondary_learner.to(lowercase_						)
  		_snake_case			 = torch.optim.Adam(secondary_learner.parameters()		,							lr=0.00001						)
  		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
  		_snake_case			 = float("inf"						)
  		# Iterate through batches until we've used max_steps batches
  		for epoch in range(int(lowercase_						)						):
  				_snake_case			 = 0.0
  				secondary_learner.train()
  				for step, batch in enumerate(lowercase_						):
  						_snake_case			 = batch[0].to(lowercase_						)
  						_snake_case			 = batch[1].to(lowercase_						)
  						_snake_case			 = secondary_learner(lowercase_						)
  						q_optimizer.zero_grad()
  						_snake_case			 = loss(lowercase_		,							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(lowercase_						)):
  								_snake_case			 = tr_q_loss / (step + 1)
  
  								secondary_learner.eval()
  								_snake_case			 = 0.0
  								_snake_case			 = 0.0
  								_snake_case			 = []
  								_snake_case			 = []
  								# Compute performance of the secondary learner after this batch
  								for stepa, batcha in enumerate(lowercase_						):
  										_snake_case			 = batcha[0].to(lowercase_						)
  										_snake_case			 = batcha[1].to(lowercase_						)
  										_snake_case			 = secondary_learner(lowercase_						)
  										q_lossa += test_loss(lowercase_		,							lowercase_						).item()
  										sum_qa += torch.sum(lowercase_						).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(lowercase_						)
  								print(
  								    "Epoch: "		,							lowercase_		,							"step: "		,							lowercase_		,							"Avg. q:"		,							sum_qa / len(lowercase_						)		,							"Train Loss: "		,							lowercase_		,							"Test Loss: "		,							lowercase_		,							)
  								if q_lossa < best_test_loss:
  										joblib.dump((predicted, actual)		,							"pred_vs_actual.jbl"						)
  										torch.save(secondary_learner.state_dict()		,							lowercase_						)
  										_snake_case			 = q_lossa
  
  						secondary_learner.train()
  		return secondary_learner
  
  
  
  
  
  class   lowerCamelCase_							( nn.Module							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							super(__UpperCamelCase,						self				).__init__()
  							_snake_case			 = model.transformer.wte
  							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
  							_snake_case			 = nn.Convad(self.embeddings.weight.size(1				),						256,						3,						padding=1				)
  							_snake_case			 = nn.Sequential(nn.Linear(256,						32				),						nn.Dropout(p=0.1				),						nn.Linear(32,						32				),						nn.Linear(32,						1				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = torch.max(self.conv(self.embeddings(__UpperCamelCase				).squeeze(1				).transpose(1,						2				)				),						2				)[0]
  							_snake_case			 = self.fc(__UpperCamelCase				)
  							return qs.squeeze(1				)
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = cls(__UpperCamelCase				)  # this calls __init__
  							_snake_case			 = torch.load(__UpperCamelCase				)
  							secondary_learner.load_state_dict(__UpperCamelCase				)
  							_snake_case			 = model.transformer.wte
  							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
  							return secondary_learner
  
  
  
  
  
  