



'''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       )    ->     Optional[int]:


      """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       )    ->     Tuple:


      """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       )    ->     str:


      """simple docstring"""



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



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


      """simple docstring"""



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



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


      """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    :       Any          =       {'feature-extraction': AutoformerModel} if is_torch_available() else {}
 __lowerCamelCase    :       Dict          =       False
 __lowerCamelCase    :       Any          =       False
 __lowerCamelCase    :       Optional[Any]          =       False
 __lowerCamelCase    :       Dict          =       False
 __lowerCamelCase    :       int          =       False
 __lowerCamelCase    :       int          =       False



 def 					a__  (self       )    ->     str:


      """simple docstring"""



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



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


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """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       )    ->     Optional[int]:


      """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       )    ->     Dict:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Dict:


      """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       )    ->     List[Any]:


      """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       )    ->     Union[str, Any]:


      """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       )    ->     Dict:


      """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       )    ->     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       )    ->     Optional[int]:


      """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       )    ->     int:


      """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       )       )








'''simple docstring'''





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowercase_		       =  datasets.utils.logging.get_logger(__name__)






class __A			(  folder_based_builder.FolderBasedBuilderConfig						):



 '''simple docstring'''







 __lowerCamelCase    :       bool          =       None
 __lowerCamelCase    :       bool          =       None






class __A			(  folder_based_builder.FolderBasedBuilder						):



 '''simple docstring'''







 __lowerCamelCase    :       int          =       datasets.Audio()
 __lowerCamelCase    :       str          =       'audio'
 __lowerCamelCase    :       Optional[Any]          =       AudioFolderConfig
 __lowerCamelCase    :       List[str]  # definition at the bottom of the script
 __lowerCamelCase    :       Union[str, Any]          =       AudioClassification(audio_column='audio'   ,   label_column='label'						)





lowercase_		       =  [
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
lowercase_		       =  AUDIO_EXTENSIONS



