



import unittest

from transformers import YosoConfig, is_torch_available
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     YosoForMaskedLM,
     YosoForMultipleChoice,
     YosoForQuestionAnswering,
     YosoForSequenceClassification,
     YosoForTokenClassification,
     YosoModel,
 )
 from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST

class        _A			:



       def __init__(						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	List[str]=13	,						__SCREAMING_SNAKE_CASE				:	Dict=7	,						__SCREAMING_SNAKE_CASE				:	str=True	,						__SCREAMING_SNAKE_CASE				:	Tuple=True	,						__SCREAMING_SNAKE_CASE				:	Dict=True	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=True	,						__SCREAMING_SNAKE_CASE				:	Any=99	,						__SCREAMING_SNAKE_CASE				:	List[str]=32	,						__SCREAMING_SNAKE_CASE				:	Tuple=5	,						__SCREAMING_SNAKE_CASE				:	Tuple=4	,						__SCREAMING_SNAKE_CASE				:	int=37	,						__SCREAMING_SNAKE_CASE				:	int="gelu"	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.1	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=0.1	,						__SCREAMING_SNAKE_CASE				:	List[Any]=512	,						__SCREAMING_SNAKE_CASE				:	Dict=16	,						__SCREAMING_SNAKE_CASE				:	int=2	,						__SCREAMING_SNAKE_CASE				:	List[str]=0.02	,						__SCREAMING_SNAKE_CASE				:	List[str]=3	,						__SCREAMING_SNAKE_CASE				:	Any=4	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=None	,						):


           '''simple docstring'''

           __a					=							parent
           __a					=							batch_size
           __a					=							seq_length
           __a					=							is_training
           __a					=							use_input_mask
           __a					=							use_token_type_ids
           __a					=							use_labels
           __a					=							vocab_size
           __a					=							hidden_size
           __a					=							num_hidden_layers
           __a					=							num_attention_heads
           __a					=							intermediate_size
           __a					=							hidden_act
           __a					=							hidden_dropout_prob
           __a					=							attention_probs_dropout_prob
           __a					=							max_position_embeddings
           __a					=							type_vocab_size
           __a					=							type_sequence_label_size
           __a					=							initializer_range
           __a					=							num_labels
           __a					=							num_choices
           __a					=							scope



       def 						_lowerCamelCase     (						self				:	Dict):


           '''simple docstring'''

           __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size)

           __a					=							None
           if self.use_input_mask:
               __a					=							random_attention_mask([self.batch_size, self.seq_length])

           __a					=							None
           if self.use_token_type_ids:
               __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.type_vocab_size)

           __a					=							None
           __a					=							None
           __a					=							None
           if self.use_labels:
               __a					=							ids_tensor([self.batch_size]	,						self.type_sequence_label_size)
               __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.num_labels)
               __a					=							ids_tensor([self.batch_size]	,						self.num_choices)

           __a					=							self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           return YosoConfig(
               vocab_size=self.vocab_size	,						hidden_size=self.hidden_size	,						num_hidden_layers=self.num_hidden_layers	,						num_attention_heads=self.num_attention_heads	,						intermediate_size=self.intermediate_size	,						hidden_act=self.hidden_act	,						hidden_dropout_prob=self.hidden_dropout_prob	,						attention_probs_dropout_prob=self.attention_probs_dropout_prob	,						max_position_embeddings=self.max_position_embeddings	,						type_vocab_size=self.type_vocab_size	,						is_decoder=lowercase_	,						initializer_range=self.initializer_range	,						)



       def 						_lowerCamelCase     (						self				:	Dict):


           '''simple docstring'''

           __a					=							self.get_config()
           __a					=							300
           return config



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           (
               (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,   
           )					=							self.prepare_config_and_inputs()

           __a					=							True
           __a					=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           __a					=							ids_tensor([self.batch_size, self.seq_length]	,						vocab_size=2)

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )



       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Any	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           __a					=							YosoModel(config=lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_)
           __a					=							model(lowercase_	,						token_type_ids=lowercase_)
           __a					=							model(lowercase_)
           self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size))



       def 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	int	,						):


           '''simple docstring'''

           __a					=							True
           __a					=							YosoModel(lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(
               lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						encoder_hidden_states=lowercase_	,						encoder_attention_mask=lowercase_	,						)
           __a					=							model(
               lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						encoder_hidden_states=lowercase_	,						)
           __a					=							model(lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_)
           self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size))



       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	List[Any]):


           '''simple docstring'''

           __a					=							YosoForMaskedLM(config=lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						labels=lowercase_)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.seq_length, self.vocab_size))



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							YosoForQuestionAnswering(config=lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(
               lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						start_positions=lowercase_	,						end_positions=lowercase_	,						)
           self.parent.assertEqual(result.start_logits.shape	,						(self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape	,						(self.batch_size, self.seq_length))



       def 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	int):


           '''simple docstring'''

           __a					=							self.num_labels
           __a					=							YosoForSequenceClassification(lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						labels=lowercase_)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.num_labels))



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           __a					=							self.num_labels
           __a					=							YosoForTokenClassification(config=lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							model(lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						labels=lowercase_)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.seq_length, self.num_labels))



       def 						_lowerCamelCase     (						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.num_choices
           __a					=							YosoForMultipleChoice(config=lowercase_)
           model.to(lowercase_)
           model.eval()
           __a					=							input_ids.unsqueeze(1).expand(-1	,						self.num_choices	,						-1).contiguous()
           __a					=							token_type_ids.unsqueeze(1).expand(-1	,						self.num_choices	,						-1).contiguous()
           __a					=							input_mask.unsqueeze(1).expand(-1	,						self.num_choices	,						-1).contiguous()
           __a					=							model(
               lowercase_	,						attention_mask=lowercase_	,						token_type_ids=lowercase_	,						labels=lowercase_	,						)
           self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.num_choices))





       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							self.prepare_config_and_inputs()
           (
               (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,  (
               __a 
           )			,   
           )					=							config_and_inputs
           __a					=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


@require_torch
class        _A			(      lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase       ):
       UpperCamelCase__    :       int		   =		(
           (
               YosoModel,
               YosoForMaskedLM,
               YosoForMultipleChoice,
               YosoForQuestionAnswering,
               YosoForSequenceClassification,
               YosoForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase__    :       Optional[int]		   =		False
       UpperCamelCase__    :       Optional[int]		   =		False
       UpperCamelCase__    :       List[str]		   =		False

       UpperCamelCase__    :       int		   =		()
       UpperCamelCase__    :       Any		   =		(
           {
               'feature-extraction': YosoModel,
               'fill-mask': YosoForMaskedLM,
               'question-answering': YosoForQuestionAnswering,
               'text-classification': YosoForSequenceClassification,
               'token-classification': YosoForTokenClassification,
               'zero-shot': YosoForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							YosoModelTester(self)
           __a					=							ConfigTester(self	,						config_class=lowercase_	,						hidden_size=37)



       def 						_lowerCamelCase     (						self				:	Dict):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase_)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               __a					=							type
               self.model_tester.create_and_check_model(*lowercase_)



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowercase_)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowercase_)



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowercase_)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowercase_)



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowercase_)



       @slow
       def 						_lowerCamelCase     (						self				:	List[Any]):


           '''simple docstring'''

           for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a					=							YosoModel.from_pretrained(lowercase_)
               self.assertIsNotNone(lowercase_)





       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           return


@require_torch
class        _A			(      unittest.TestCase       ):



       @slow
       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							YosoModel.from_pretrained('''uw-madison/yoso-4096''')
           __a					=							torch.tensor([[0, 1, 2, 3, 4, 5]])

           with torch.no_grad():
               __a					=							model(lowercase_)[0]

           __a					=							torch.Size((1, 6, 768))
           self.assertEqual(output.shape	,						lowercase_)

           __a					=							torch.tensor(
               [[[-0.06_11, 0.12_42, 0.08_40], [0.02_80, -0.00_48, 0.11_25], [0.01_06, 0.02_26, 0.07_51]]])

           self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=1E-4))



       @slow
       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''')
           __a					=							torch.tensor([[0, 1, 2, 3, 4, 5]])

           with torch.no_grad():
               __a					=							model(lowercase_)[0]

           __a					=							50_265

           __a					=							torch.Size((1, 6, vocab_size))
           self.assertEqual(output.shape	,						lowercase_)

           __a					=							torch.tensor(
               [[[-2.13_13, -3.72_85, -2.24_07], [-2.70_47, -3.33_14, -2.64_08], [0.06_29, -2.51_66, -0.33_56]]])

           self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=1E-4))





       @slow
       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''')
           __a					=							torch.arange(4_096).unsqueeze(0)

           with torch.no_grad():
               __a					=							model(lowercase_)[0]

           __a					=							50_265

           __a					=							torch.Size((1, 4_096, vocab_size))
           self.assertEqual(output.shape	,						lowercase_)

           __a					=							torch.tensor(
               [[[-2.39_14, -4.37_42, -5.09_56], [-4.09_88, -4.23_84, -7.04_06], [-3.14_27, -3.71_92, -6.68_00]]])

           self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=1E-4))








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 	=   logging.getLogger(__name__)




def      UpperCAmelCase__							(     _A :					Optional[Any]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Tuple      , _A :					Union[str, Any]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict="gpt2"				):





  '''simple docstring'''







  a__    =GPTaLMHeadModel.from_pretrained(_A      , output_hidden_states=_A				)
  torch.save(model.state_dict()      , model_name + '''local.pt'''				)
  return model




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					Dict				):





  '''simple docstring'''







  a__    =copy.deepcopy(_A				)
  model.to(_A				)

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




def      UpperCAmelCase__							(     _A :					List[str]      , _A :					Optional[Any]      , _A :					Tuple      , _A :					List[str]				):





  '''simple docstring'''







  a__    =np.array(_A				).mean()
  a__    =np.array(_A				).std()
  a__    =(real_perps - avg) / std
  a__    =list(zip(_A      , _A      , _A				)				)
  joblib.dump(_A      , _A				)




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Dict      , _A :					Optional[Any]      , _A :					Union[str, Any]      , _A :					Dict      , _A :					str      , _A :					List[Any]      , _A :					Optional[Any]="dev.jbl"      , _A :					Optional[int]=recopy_gpta      , ):





  '''simple docstring'''







  a__    =[]
  a__    =[]
  a__    =[]

  # Initialize the transformer model
  a__    =copy.deepcopy(_A				)
  orig_model.to(device='''cpu'''				)
  torch.cuda.empty_cache()

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

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

    # Periodically save the stored (X, IG(X)) pairs
    if step % 10_00 == 0 and step > 1:
      intermittent_save(_A      , _A      , _A      , _A				)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    a__, a__, a__    =recopy_model(_A      , _A      , _A				)

    past_perps.append(past_perp.item()				)
    real_perps.append(orig_perp - real_perp.item()				)
    contexts.append(np.array(context.cpu()				)				)

  intermittent_save(_A      , _A      , _A      , _A				)




def      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"      , _A :					Any=1_00      , _A :					Dict=10_26      , _A :					Any=True				):





  '''simple docstring'''







  a__    =joblib.load(_A				)
  print('''data loaded'''				)
  a__    =[]
  if trim:
    for i, example in enumerate(_A				):
      if len(example[0]				) > min_len:
        a__    =random.randint(0      , len(example[0]				) - context_len - 1				)
        objective_set.append(example[0, start : start + context_len]				)
      if len(_A				) >= number:
        break
    a__    =[]
    for j in range(i + 1      , len(_A				)				):
      if len(data[j][0]				) > min_len:
        train_data.append(data[j]				)
  else:
    a__    =data[0:number]
    a__    =data[number:]

  joblib.dump(_A      , '''objective_set.jbl'''				)
  print('''objective set saved'''				)
  return train_data, objective_set




def      UpperCAmelCase__							(     _A :					str      , _A :					Dict      , _A :					Union[str, Any]      , _A :					Dict      , _A :					Dict=50      , _A :					int="secondary_learner.pt"				):





  '''simple docstring'''







  a__    =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
  a__    =train_dataset[:5_12]
  a__    =train_dataset[5_12:]
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)

  # secondary learner model set up
  a__    =nn.MSELoss()
  a__    =nn.MSELoss(reduction='''sum'''				)
  secondary_learner.to(_A				)
  a__    =torch.optim.Adam(secondary_learner.parameters()      , lr=0.00_001				)
  secondary_learner.train()

  # TODO in original code this is written as number of actual batches seen
  # not number of items seen but other places it is number of items instead.
  # improve consistency! changed this to epochs for clarity
  a__    =float('''inf'''				)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_A				)				):
    a__    =0.0
    secondary_learner.train()
    for step, batch in enumerate(_A				):
      a__    =batch[0].to(_A				)
      a__    =batch[1].to(_A				)
      a__    =secondary_learner(_A				)
      q_optimizer.zero_grad()
      a__    =loss(_A      , 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(_A				)):
        a__    =tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__    =0.0
        a__    =0.0
        a__    =[]
        a__    =[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_A				):
          a__    =batcha[0].to(_A				)
          a__    =batcha[1].to(_A				)
          a__    =secondary_learner(_A				)
          q_lossa += test_loss(_A      , _A				).item()
          sum_qa += torch.sum(_A				).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(_A				)
        print(
            '''Epoch: '''      , _A      , '''step: '''      , _A      , '''Avg. q:'''      , sum_qa / len(_A				)      , '''Train Loss: '''      , _A      , '''Test Loss: '''      , _A      , )
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)      , '''pred_vs_actual.jbl'''				)
          torch.save(secondary_learner.state_dict()      , _A				)
          a__    =q_lossa

      secondary_learner.train()
  return secondary_learner







class 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       str:






     """simple docstring"""






     super(lowercase_,       self			).__init__()
     a__    =model.transformer.wte
     a__    =copy.deepcopy(model.transformer.wte.weight			)
     a__    =nn.Convad(self.embeddings.weight.size(1			),       256,       3,       padding=1			)
     a__    =nn.Sequential(nn.Linear(256,       32			),       nn.Dropout(p=0.1			),       nn.Linear(32,       32			),       nn.Linear(32,       1			)			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =torch.max(self.conv(self.embeddings(lowercase_			).squeeze(1			).transpose(1,       2			)			),       2			)[0]
     a__    =self.fc(lowercase_			)
     return qs.squeeze(1			)



   @classmethod
   def 					_UpperCAmelCase					(							cls,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =cls(lowercase_			)  # this calls __init__
     a__    =torch.load(lowercase_			)
     secondary_learner.load_state_dict(lowercase_			)
     a__    =model.transformer.wte
     a__    =copy.deepcopy(model.transformer.wte.weight			)
     return secondary_learner
