



"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Any           =  (DDPMScheduler,)



   def    __UpperCAmelCase    (       self    :	Dict							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      {
         '''num_train_timesteps''': 1_0_0_0,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**__lowerCamelCase				)
     return config



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,       [0.0_0_2, 0.0_2, 0.2, 2]				):
       self.check_over_configs(beta_start=__lowerCamelCase							,       beta_end=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     for variance in ["fixed_small", "fixed_large", "other"]:
       self.check_over_configs(variance_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     self.check_over_configs(thresholding=__lowerCamelCase				)
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(
             thresholding=__lowerCamelCase							,       prediction_type=__lowerCamelCase							,       sample_max_value=__lowerCamelCase							,       )



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(prediction_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     for t in [0, 5_0_0, 9_9_9]:
       self.check_over_forward(time_step=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				) - 0.0_0_9_7_9				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				) - 0.0_2				)				) < 1E-5



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      len(__lowerCamelCase				)

     _snake_case   	=      self.dummy_model()
     _snake_case   	=      self.dummy_sample_deter
     _snake_case   	=      torch.manual_seed(0				)

     for t in reversed(range(__lowerCamelCase				)				):
       # 1. predict noise residual
       _snake_case   	=      model(__lowerCamelCase							,       __lowerCamelCase				)

       # 2. predict previous mean of sample x_t-1
       _snake_case   	=      scheduler.step(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       generator=__lowerCamelCase				).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       _snake_case   	=      pred_prev_sample

     _snake_case   	=      torch.sum(torch.abs(__lowerCamelCase				)				)
     _snake_case   	=      torch.mean(torch.abs(__lowerCamelCase				)				)

     assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1E-2
     assert abs(result_mean.item() - 0.3_3_7_2				) < 1E-3



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config(prediction_type='''v_prediction'''				)
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      len(__lowerCamelCase				)

     _snake_case   	=      self.dummy_model()
     _snake_case   	=      self.dummy_sample_deter
     _snake_case   	=      torch.manual_seed(0				)

     for t in reversed(range(__lowerCamelCase				)				):
       # 1. predict noise residual
       _snake_case   	=      model(__lowerCamelCase							,       __lowerCamelCase				)

       # 2. predict previous mean of sample x_t-1
       _snake_case   	=      scheduler.step(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       generator=__lowerCamelCase				).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       _snake_case   	=      pred_prev_sample

     _snake_case   	=      torch.sum(torch.abs(__lowerCamelCase				)				)
     _snake_case   	=      torch.mean(torch.abs(__lowerCamelCase				)				)

     assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1E-2
     assert abs(result_mean.item() - 0.2_6_3_1				) < 1E-3



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 1, 0]

     scheduler.set_timesteps(timesteps=__lowerCamelCase				)

     _snake_case   	=      scheduler.timesteps

     for i, timestep in enumerate(__lowerCamelCase				):
       if i == len(__lowerCamelCase				) - 1:
         _snake_case   	=      -1
       else:
         _snake_case   	=      timesteps[i + 1]

       _snake_case   	=      scheduler.previous_timestep(__lowerCamelCase				)
       _snake_case   	=      prev_t.item()

       self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 5_1, 0]

     with self.assertRaises(__lowerCamelCase							,       msg='''`custom_timesteps` must be in descending order.'''				):
       scheduler.set_timesteps(timesteps=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 1, 0]
     _snake_case   	=      len(__lowerCamelCase				)

     with self.assertRaises(__lowerCamelCase							,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
       scheduler.set_timesteps(num_inference_steps=__lowerCamelCase							,       timesteps=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         __lowerCamelCase							,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,       ):
       scheduler.set_timesteps(timesteps=__lowerCamelCase				)










'''simple docstring'''





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


lowercase_									=    1e-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=16					,  A=13					,  A=7					,  A=14					,  A=10					,  A=19					,  A=5					,  A=4					,  A=True					,  A=16					,  A=2					,  A=4					,  A=4					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=[1, 2, 3, 4, 5]					,  A=25					,  A=5					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    d_model
      _a           =    parent
      _a           =    batch_size
      _a           =    prediction_length
      _a           =    context_length
      _a           =    cardinality
      _a           =    num_time_features
      _a           =    lags_sequence
      _a           =    embedding_dimension
      _a           =    is_training
      _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           =    context_length
      _a           =    prediction_length + label_length
      _a           =    label_length

      _a           =    moving_average
      _a           =    autocorrelation_factor



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      return AutoformerConfig(
          d_model=self.d_model					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  prediction_length=self.prediction_length					,  context_length=self.context_length					,  label_length=self.label_length					,  lags_sequence=self.lags_sequence					,  num_time_features=self.num_time_features					,  num_static_categorical_features=1					,  cardinality=[self.cardinality]					,  embedding_dimension=[self.embedding_dimension]					,  moving_average=self.moving_average					,  )



 def    a__    (self					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    config.context_length + max(config.lags_sequence			)

      _a           =    ids_tensor([self.batch_size, 1]					,  config.cardinality[0]			)
      _a           =    floats_tensor([self.batch_size, _past_length, config.num_time_features]			)
      _a           =    floats_tensor([self.batch_size, _past_length]			)
      _a           =    floats_tensor([self.batch_size, _past_length]			) > 0.5

      # decoder inputs
      _a           =    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]			)
      _a           =    floats_tensor([self.batch_size, config.prediction_length]			)

      _a           =    {
          '''past_values''': past_values,
          '''static_categorical_features''': static_categorical_features,
          '''past_time_features''': past_time_features,
          '''past_observed_mask''': past_observed_mask,
          '''future_time_features''': future_time_features,
          '''future_values''': future_values,
      }
      return inputs_dict



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.get_config()
      _a           =    self.prepare_autoformer_inputs_dict(A			)
      return config, inputs_dict



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a    ,       _a           =    self.prepare_config_and_inputs()
      return config, inputs_dict



 def    a__    (self					,  A					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    AutoformerModel(config=A			).to(A			).eval()
      _a           =    model(**A			)

      _a           =    outputs.encoder_last_hidden_state
      _a           =    outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
           _a           =    model.get_encoder()
           encoder.save_pretrained(A			)
           _a           =    AutoformerEncoder.from_pretrained(A			).to(A			)

      _a    ,       _a    ,       _a    ,       _a    ,       _a           =    model.create_network_inputs(**A			)
      _a    ,       _a           =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

      _a           =    torch.cat(
          (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])					,  dim=-1					,  )
      _a           =    encoder(inputs_embeds=A			)[0]
      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3			)

      _a           =    (
          torch.mean(transformer_inputs[:, : config.context_length, ...]					,  dim=1			)
          .unsqueeze(1			)
          .repeat(1					,  config.prediction_length					,  1			)
      )
      _a           =    torch.zeros(
          [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]					,  device=enc_input.device					,  )

      _a           =    torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)					,  dim=1			),
              feature[:, config.context_length - config.label_length :, ...],
          )					,  dim=-1					,  )
      _a           =    torch.cat(
          (
              torch.cat((trend_input[:, -config.label_length :, ...], mean)					,  dim=1			),
              feature[:, config.context_length - config.label_length :, ...],
          )					,  dim=-1					,  )

      with tempfile.TemporaryDirectory() as tmpdirname:
           _a           =    model.get_decoder()
           decoder.save_pretrained(A			)
           _a           =    AutoformerDecoder.from_pretrained(A			).to(A			)

      _a           =    decoder(
          trend=A					,  inputs_embeds=A					,  encoder_hidden_states=A					,  )[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3			)






@require_torch
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 __lowerCamelCase       :			Optional[Any]		=			(AutoformerForPrediction,) if is_torch_available() else ()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': AutoformerModel} if is_torch_available() else {}
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			int		=			False
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Optional[int]		=			False
 __lowerCamelCase       :			List[Any]		=			False



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    AutoformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  has_text_modality=A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           _a           =    model_class(A			)

           with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(A			)
                _a    ,       _a           =    model_class.from_pretrained(A					,  output_loading_info=A			)
           self.assertEqual(info['''missing_keys''']					,  []			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*A			)



 @unittest.skip(reason='''Model has no tokens embeddings'''			)
 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      pass



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    inspect.signature(getattr(A					,  '''forward'''			)			)
      # The main input is the name of the argument after `self`
      _a           =    list(model_signature.parameters.keys()			)[1]
      self.assertEqual(AutoformerModel.main_input_name					,  A			)



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

           _a           =    [
               '''past_values''',
               '''past_time_features''',
               '''past_observed_mask''',
               '''static_categorical_features''',
               '''static_real_features''',
               '''future_values''',
               '''future_time_features''',
           ]

           if model.__class__.__name__ in ["AutoformerForPrediction"]:
                expected_arg_names.append('''future_observed_mask'''			)

           expected_arg_names.extend(
               [
                   '''decoder_attention_mask''',
                   '''head_mask''',
                   '''decoder_head_mask''',
                   '''cross_attn_head_mask''',
                   '''encoder_outputs''',
                   '''past_key_values''',
                   '''output_hidden_states''',
                   '''output_attentions''',
                   '''use_cache''',
                   '''return_dict''',
               ]			)

           self.assertListEqual(arg_names[: len(A			)]					,  A			)



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    True

      _a           =    getattr(self.model_tester					,  '''seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''decoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''encoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''d_model'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''num_attention_heads'''					,  A			)
      _a           =    d_model // num_attention_heads

      for model_class in self.all_model_classes:
           _a           =    True
           _a           =    False
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    outputs.encoder_attentions
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)

           self.assertListEqual(
               list(attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]					,  )
           _a           =    len(A			)

           _a           =    7

           if "last_hidden_state" in outputs:
                correct_outlen += 1

           if "trend" in outputs:
                correct_outlen += 1

           if "past_key_values" in outputs:
                correct_outlen += 1  # past_key_values have been returned

           if "loss" in outputs:
                correct_outlen += 1

           if "params" in outputs:
                correct_outlen += 1

           self.assertEqual(A					,  A			)

           # decoder attentions
           _a           =    outputs.decoder_attentions
           self.assertIsInstance(A					,  (list, tuple)			)
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]					,  )

           # cross attentions
           _a           =    outputs.cross_attentions
           self.assertIsInstance(A					,  (list, tuple)			)
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(cross_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]					,  )

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

      self.assertEqual(out_len + 2					,  len(A			)			)

      _a           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]					,  )



 @is_flaky()
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      super().test_retain_grad_hidden_states_attentions()





def lowerCAmelCase    (__A="train-batch.pt"):



     """simple docstring"""



     _a           =    hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''    ,      filename=__A    ,      repo_type='''dataset''')
     _a           =    torch.load(__A    ,      map_location=__A)
     return batch






@require_torch
@slow
class __A     (    unittest.TestCase		):



 '''simple docstring'''







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


      """simple docstring"""



      _a           =    AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch()

      with torch.no_grad():
           _a           =    model(
               past_values=batch['''past_values''']					,  past_time_features=batch['''past_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  static_categorical_features=batch['''static_categorical_features''']					,  future_values=batch['''future_values''']					,  future_time_features=batch['''future_time_features''']					,  )[0]

      _a           =    torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)			)
      self.assertEqual(output.shape					,  A			)

      _a           =    torch.tensor(
          [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]					,  device=A			)
      self.assertTrue(torch.allclose(output[0, :3, :3]					,  A					,  atol=A			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch('''val-batch.pt'''			)
      with torch.no_grad():
           _a           =    model(
               past_values=batch['''past_values''']					,  past_time_features=batch['''past_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  static_categorical_features=batch['''static_categorical_features''']					,  ).encoder_last_hidden_state
      _a           =    torch.Size((64, model.config.context_length, model.config.d_model)			)
      self.assertEqual(output.shape					,  A			)

      _a           =    torch.tensor(
          [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]					,  device=A			)
      self.assertTrue(torch.allclose(output[0, :3, :3]					,  A					,  atol=A			)			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch('''val-batch.pt'''			)
      with torch.no_grad():
           _a           =    model.generate(
               static_categorical_features=batch['''static_categorical_features''']					,  past_time_features=batch['''past_time_features''']					,  past_values=batch['''past_values''']					,  future_time_features=batch['''future_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  )
      _a           =    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)			)
      self.assertEqual(outputs.sequences.shape					,  A			)

      _a           =    torch.tensor([3130.6763, 4056.5293, 7053.0786]					,  device=A			)
      _a           =    outputs.sequences.mean(dim=1			)
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]					,  A					,  rtol=1E-1			)			)



