


'''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


a_  :				List[Any]						= 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      snake_case    :





  """simple docstring"""




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







   """simple docstring"""
   lowerCamelCase_      =  d_model
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  prediction_length
   lowerCamelCase_      =  context_length
   lowerCamelCase_      =  cardinality
   lowerCamelCase_      =  num_time_features
   lowerCamelCase_      =  lags_sequence
   lowerCamelCase_      =  embedding_dimension
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob

   lowerCamelCase_      =  context_length
   lowerCamelCase_      =  prediction_length + label_length
   lowerCamelCase_      =  label_length

   lowerCamelCase_      =  moving_average
   lowerCamelCase_      =  autocorrelation_factor






  def   snake_case		(    self     ):







   """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   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  config.context_length + max(config.lags_sequence     )

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

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

   lowerCamelCase_      =  {
       "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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_config()
   lowerCamelCase_      =  self.prepare_autoformer_inputs_dict(UpperCamelCase     )
   return config, inputs_dict






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.prepare_config_and_inputs()
   return config, inputs_dict







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoformerModel(config=UpperCamelCase     ).to(UpperCamelCase     ).eval()
   lowerCamelCase_      =  model(**UpperCamelCase     )

   lowerCamelCase_      =  outputs.encoder_last_hidden_state
   lowerCamelCase_      =  outputs.last_hidden_state

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

   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  model.create_network_inputs(**UpperCamelCase     )
   lowerCamelCase_ ,lowerCamelCase_      =  model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]     )

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

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

   lowerCamelCase_      =  torch.cat(
       (
           torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)			,  dim=1     ),
           feature[:, config.context_length - config.label_length :, ...],
       )			,  dim=-1			,  )
   lowerCamelCase_      =  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:
    lowerCamelCase_      =  model.get_decoder()
    decoder.save_pretrained(UpperCamelCase     )
    lowerCamelCase_      =  AutoformerDecoder.from_pretrained(UpperCamelCase     ).to(UpperCamelCase     )

   lowerCamelCase_      =  decoder(
       trend=UpperCamelCase			,  inputs_embeds=UpperCamelCase			,  encoder_hidden_states=UpperCamelCase			,  )[0]

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





@require_torch
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
  _lowerCamelCase		    =  (AutoformerForPrediction,) if is_torch_available() else ()
  _lowerCamelCase		    =  {"feature-extraction": AutoformerModel} if is_torch_available() else {}
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoformerModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
    lowerCamelCase_      =  model_class(UpperCamelCase     )

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(UpperCamelCase     )
     lowerCamelCase_ ,lowerCamelCase_      =  model_class.from_pretrained(UpperCamelCase			,  output_loading_info=UpperCamelCase     )
    self.assertEqual(info["missing_keys"]			,  []     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase     )






  @unittest.skip(reason="Model has no tokens embeddings"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  inspect.signature(getattr(UpperCamelCase			,  "forward"     )     )
   # The main input is the name of the argument after `self`
   lowerCamelCase_      =  list(model_signature.parameters.keys()     )[1]
   self.assertEqual(AutoformerModel.main_input_name			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_      =  [
        "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(UpperCamelCase     )]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_      =  True

   lowerCamelCase_      =  getattr(self.model_tester			,  "seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "decoder_seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "encoder_seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "d_model"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "num_attention_heads"			,  UpperCamelCase     )
   lowerCamelCase_      =  d_model // num_attention_heads

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

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

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

    lowerCamelCase_      =  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(UpperCamelCase			,  UpperCamelCase     )

    # decoder attentions
    lowerCamelCase_      =  outputs.decoder_attentions
    self.assertIsInstance(UpperCamelCase			,  (list, tuple)     )
    self.assertEqual(len(UpperCamelCase     )			,  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
    lowerCamelCase_      =  outputs.cross_attentions
    self.assertIsInstance(UpperCamelCase			,  (list, tuple)     )
    self.assertEqual(len(UpperCamelCase     )			,  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
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  model_class(UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   with torch.no_grad():
    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )     )

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

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

   self.assertEqual(len(UpperCamelCase     )			,  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   snake_case		(    self     ):







   """simple docstring"""
   super().test_retain_grad_hidden_states_attentions()





def __snake_case					(      UpperCAmelCase_						:						int="train-batch.pt"		):
 lowerCamelCase_      =  hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"					,				filename=UpperCAmelCase_					,				repo_type="dataset"		)
 lowerCamelCase_      =  torch.load(UpperCAmelCase_					,				map_location=UpperCAmelCase_		)
 return batch







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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCamelCase     )
   lowerCamelCase_      =  prepare_batch()

   with torch.no_grad():
    lowerCamelCase_      =  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]

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

   lowerCamelCase_      =  torch.tensor(
       [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]			,  device=UpperCamelCase     )
   self.assertTrue(torch.allclose(output[0, :3, :3]			,  UpperCamelCase			,  atol=UpperCamelCase     )     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCamelCase     )
   lowerCamelCase_      =  prepare_batch("val-batch.pt"     )
   with torch.no_grad():
    lowerCamelCase_      =  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
   lowerCamelCase_      =  torch.Size((64, model.config.context_length, model.config.d_model)     )
   self.assertEqual(output.shape			,  UpperCamelCase     )

   lowerCamelCase_      =  torch.tensor(
       [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]			,  device=UpperCamelCase     )
   self.assertTrue(torch.allclose(output[0, :3, :3]			,  UpperCamelCase			,  atol=UpperCamelCase     )     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCamelCase     )
   lowerCamelCase_      =  prepare_batch("val-batch.pt"     )
   with torch.no_grad():
    lowerCamelCase_      =  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"]			,  )
   lowerCamelCase_      =  torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)     )
   self.assertEqual(outputs.sequences.shape			,  UpperCamelCase     )

   lowerCamelCase_      =  torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]			,  device=UpperCamelCase     )
   lowerCamelCase_      =  outputs.sequences.mean(dim=1     )
   self.assertTrue(torch.allclose(mean_prediction[0, -3:]			,  UpperCamelCase			,  rtol=1e-1     )     )







'''simple docstring'''

from __future__ import annotations





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  2
 lowerCamelCase_      =  []
 while i * i <= n:
  if n % i:
   i += 1
  else:
   n //= i
   factors.append(UpperCAmelCase_		)
 if n > 1:
  factors.append(UpperCAmelCase_		)
 return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()



