

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 		UpperCamelCase_(    snake_case__:						str     ) ->				Optional[Any]:
 random.seed(snake_case__     )
 np.random.seed(snake_case__     )
 torch.manual_seed(snake_case__     )
 torch.cuda.manual_seed_all(snake_case__     )
def 		UpperCamelCase_(    snake_case__:						Dict						,					snake_case__:						Optional[int]						,					snake_case__:						int     ) ->				Tuple:
 model.eval()
 UpperCAmelCase__           =       next(model.parameters()     ).device
 UpperCAmelCase__           =       1
 UpperCAmelCase__           =       torch.zeros((eval_batch_size, context_len)						,					dtype=torch.long						,					device=snake_case__     )
 UpperCAmelCase__           =       DataLoader(snake_case__						,					shuffle=snake_case__						,					batch_size=snake_case__     )
 UpperCAmelCase__           =       torch.zeros(1						,					device=snake_case__     )
 UpperCAmelCase__           =       0
 for batch in eval_dataloader:
  batch.to(snake_case__     )
  # pad
  context.zero_()
  for i in range(snake_case__     ):
   UpperCAmelCase__           =       batch[i]
  UpperCAmelCase__           =       model(snake_case__						,					labels=snake_case__     )
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0     )
 UpperCAmelCase__           =       eval_loss / nb_eval_examples
 UpperCAmelCase__           =       torch.exp(snake_case__     )
 model.train()
 return perplexity
def 		UpperCamelCase_(    snake_case__:						Tuple="gpt2"     ) ->				List[Any]:
 UpperCAmelCase__           =       GPTaLMHeadModel.from_pretrained(snake_case__						,					output_hidden_states=snake_case__     )
 torch.save(model.state_dict()						,					model_name + 'local.pt'     )
 return model
def 		UpperCamelCase_(    snake_case__:						Union[str, Any]						,					snake_case__:						str						,					snake_case__:						Tuple     ) ->				Union[str, Any]:
 UpperCAmelCase__           =       copy.deepcopy(snake_case__     )
 model.to(snake_case__     )

 UpperCAmelCase__           =       ['bias', 'LayerNorm.weight']
 UpperCAmelCase__           =       [
     {
         '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},
 ]
 UpperCAmelCase__           =       AdamW(snake_case__						,					lr=5e-5						,					eps=1e-8     )
 UpperCAmelCase__           =       get_linear_schedule_with_warmup(snake_case__						,					0						,					snake_case__     )
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler
def 		UpperCamelCase_(    snake_case__:						List[Any]						,					snake_case__:						Dict						,					snake_case__:						str						,					snake_case__:						Union[str, Any]     ) ->				Optional[int]:
 UpperCAmelCase__           =       np.array(snake_case__     ).mean()
 UpperCAmelCase__           =       np.array(snake_case__     ).std()
 UpperCAmelCase__           =       (real_perps - avg) / std
 UpperCAmelCase__           =       list(zip(snake_case__						,					snake_case__						,					snake_case__     )     )
 joblib.dump(snake_case__						,					snake_case__     )
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						int						,					snake_case__:						Optional[Any]						,					snake_case__:						Tuple						,					snake_case__:						Any						,					snake_case__:						List[Any]						,					snake_case__:						Union[str, Any]						,					snake_case__:						Tuple="dev.jbl"						,					snake_case__:						Tuple=recopy_gpta						,					) ->				Union[str, Any]:
 UpperCAmelCase__           =       []
 UpperCAmelCase__           =       []
 UpperCAmelCase__           =       []

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

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

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

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

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

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

 intermittent_save(snake_case__						,					snake_case__						,					snake_case__						,					snake_case__     )
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						List[Any]="data/tokenized_stories_train_wikitext103.jbl"						,					snake_case__:						int=1_00						,					snake_case__:						Optional[Any]=10_26						,					snake_case__:						int=True     ) ->				List[str]:
 UpperCAmelCase__           =       joblib.load(snake_case__     )
 print('data loaded'     )
 UpperCAmelCase__           =       []
 if trim:
  for i, example in enumerate(snake_case__     ):
   if len(example[0]     ) > min_len:
    UpperCAmelCase__           =       random.randint(0						,					len(example[0]     ) - context_len - 1     )
    objective_set.append(example[0, start : start + context_len]     )
   if len(snake_case__     ) >= number:
    break
  UpperCAmelCase__           =       []
  for j in range(i + 1						,					len(snake_case__     )     ):
   if len(data[j][0]     ) > min_len:
    train_data.append(data[j]     )
 else:
  UpperCAmelCase__           =       data[0:number]
  UpperCAmelCase__           =       data[number:]

 joblib.dump(snake_case__						,					'objective_set.jbl'     )
 print('objective set saved'     )
 return train_data, objective_set
def 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						Dict						,					snake_case__:						Any						,					snake_case__:						List[str]						,					snake_case__:						Dict=50						,					snake_case__:						Dict="secondary_learner.pt"     ) ->				List[str]:
 UpperCAmelCase__           =       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
 UpperCAmelCase__           =       train_dataset[:5_12]
 UpperCAmelCase__           =       train_dataset[5_12:]
 UpperCAmelCase__           =       DataLoader(snake_case__						,					shuffle=snake_case__						,					batch_size=snake_case__     )
 UpperCAmelCase__           =       DataLoader(snake_case__						,					shuffle=snake_case__						,					batch_size=snake_case__     )

 # secondary learner model set up
 UpperCAmelCase__           =       nn.MSELoss()
 UpperCAmelCase__           =       nn.MSELoss(reduction='sum'     )
 secondary_learner.to(snake_case__     )
 UpperCAmelCase__           =       torch.optim.Adam(secondary_learner.parameters()						,					lr=0.0_0_0_0_1     )
 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
 UpperCAmelCase__           =       float('inf'     )
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(snake_case__     )     ):
  UpperCAmelCase__           =       0.0
  secondary_learner.train()
  for step, batch in enumerate(snake_case__     ):
   UpperCAmelCase__           =       batch[0].to(snake_case__     )
   UpperCAmelCase__           =       batch[1].to(snake_case__     )
   UpperCAmelCase__           =       secondary_learner(snake_case__     )
   q_optimizer.zero_grad()
   UpperCAmelCase__           =       loss(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(snake_case__     )):
    UpperCAmelCase__           =       tr_q_loss / (step + 1)

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

   secondary_learner.train()
 return secondary_learner







class 					lowercase		( nn.Module      ):



 '''simple docstring'''


 def __init__(self , __a				)				->      int:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      str:






  """simple docstring"""





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




 @classmethod
 def 						UpperCamelCase__							(cls , __a , __a				)				->      Optional[int]:






  """simple docstring"""





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






import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return (4, 14, 16)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_output()




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'block_out_channels': (32, 64, 128, 256),
      'in_channels': 14,
      'out_channels': 14,
      'time_embedding_type': 'positional',
      'use_timestep_embedding': True,
      'flip_sin_to_cos': False,
      'freq_shift': 1.0,
      'out_block_type': 'OutConv1DBlock',
      'mid_block_type': 'MidResTemporalBlock1D',
      'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
      'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='unet'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(loading_info['missing_keys']				) , 0				)

  model.to(__a				)
  UpperCAmelCase__           =       model(**self.dummy_input				)

  assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32' , subfolder='unet'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       model.config.in_channels
  UpperCAmelCase__           =       16
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample.permute(0 , 2 , 1				)

  UpperCAmelCase__           =       output[0, -3:, -3:].flatten()
  # fmt: off
  UpperCAmelCase__           =       torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  pass




 @slow
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       'harmonai/maestro-150k'
  UpperCAmelCase__           =       UNetaDModel.from_pretrained(__a , subfolder='unet'				)
  model.to(__a				)

  UpperCAmelCase__           =       65536
  UpperCAmelCase__           =       torch.sin(torch.arange(__a				)[None, None, :].repeat(1 , 2 , 1				)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([1]				).to(__a				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample

  UpperCAmelCase__           =       output.abs().sum()
  UpperCAmelCase__           =       output.abs().max()

  assert (output_sum - 2_24.08_96).abs() < 0.5
  assert (output_max - 0.06_07).abs() < 4E-4







class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (4, 14, 1)




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.prepare_init_args_and_inputs_for_common()
  UpperCAmelCase__           =       self.model_class(**__a				)
  model.to(__a				)
  model.eval()

  with torch.no_grad():
   UpperCAmelCase__           =       model(**__a				)

   if isinstance(__a , __a				):
    UpperCAmelCase__           =       output.sample

  self.assertIsNotNone(__a				)
  UpperCAmelCase__           =       torch.Size((inputs_dict['sample'].shape[0], 1)				)
  self.assertEqual(output.shape , __a , 'Input and output shapes do not match'				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'in_channels': 14,
      'out_channels': 14,
      'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
      'up_block_types': [],
      'out_block_type': 'ValueFunction',
      'mid_block_type': 'ValueFunctionMidBlock1D',
      'block_out_channels': [32, 64, 128, 256],
      'layers_per_block': 1,
      'downsample_each_block': True,
      'use_timestep_embedding': True,
      'freq_shift': 1.0,
      'flip_sin_to_cos': False,
      'time_embedding_type': 'positional',
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(vf_loading_info['missing_keys']				) , 0				)

  value_function.to(__a				)
  UpperCAmelCase__           =       value_function(**self.dummy_input				)

  assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       value_function.config.in_channels
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       value_function(__a , __a				).sample

  # fmt: off
  UpperCAmelCase__           =       torch.tensor([1_65.25] * seq_len				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  pass



