



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class     UpperCAmelCase_       (			a  ,   unittest.TestCase):
							lowerCamelCase__       =						DebertaTokenizer
							lowerCamelCase__       =						True
							lowerCamelCase__       =						DebertaTokenizerFast







							def   snake_case__ (  self):

														'''simple docstring'''

														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														_lowerCAmelCase	:				List[str]					  =       [
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "\u0120",
														    "\u0120l",
														    "\u0120n",
														    "\u0120lo",
														    "\u0120low",
														    "er",
														    "\u0120lowest",
														    "\u0120newer",
														    "\u0120wider",
														    "[UNK]",
														]
														_lowerCAmelCase	:				Tuple					  =       dict(zip(__a,     range(len(__a))))
														_lowerCAmelCase	:				List[Any]					  =       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
														_lowerCAmelCase	:				Tuple					  =       {"unk_token": "[UNK]"}

														_lowerCAmelCase	:				Union[str, Any]					  =       os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase	:				Optional[int]					  =       os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES["merges_file"])
														with open(self.vocab_file,     "w",     encoding="utf-8") as fp:
																					fp.write(json.dumps(__a) + "\n")
														with open(self.merges_file,     "w",     encoding="utf-8") as fp:
																					fp.write("\n".join(__a))







							def   snake_case__ (  self,     **__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return self.tokenizer_class.from_pretrained(self.tmpdirname,     **__a)







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Any					  =       "lower newer"
														_lowerCAmelCase	:				Union[str, Any]					  =       "lower newer"
														return input_text, output_text







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Union[str, Any]					  =       self.get_tokenizer()
														_lowerCAmelCase	:				Optional[Any]					  =       "lower newer"
														_lowerCAmelCase	:				Optional[int]					  =       ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
														_lowerCAmelCase	:				str					  =       tokenizer.tokenize(__a)
														self.assertListEqual(__a,     __a)

														_lowerCAmelCase	:				Optional[int]					  =       tokens + [tokenizer.unk_token]
														_lowerCAmelCase	:				List[str]					  =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),     __a)







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Tuple					  =       self.get_tokenizer()
														_lowerCAmelCase	:				str					  =       tokenizer("Hello",     "World")
														_lowerCAmelCase	:				Dict					  =       [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
														self.assertListEqual(tokd["token_type_ids"],     __a)







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       self.tokenizer_class.from_pretrained("microsoft/deberta-base")

														_lowerCAmelCase	:				int					  =       tokenizer.encode("sequence builders",     add_special_tokens=__a)
														_lowerCAmelCase	:				Any					  =       tokenizer.encode("multi-sequence build",     add_special_tokens=__a)

														_lowerCAmelCase	:				Tuple					  =       tokenizer.encode(
														    "sequence builders",     add_special_tokens=__a,     add_prefix_space=__a)
														_lowerCAmelCase	:				int					  =       tokenizer.encode(
														    "sequence builders",     "multi-sequence build",     add_special_tokens=__a,     add_prefix_space=__a)

														_lowerCAmelCase	:				int					  =       tokenizer.build_inputs_with_special_tokens(__a)
														_lowerCAmelCase	:				Any					  =       tokenizer.build_inputs_with_special_tokens(__a,     __a)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[int]					  =       [self.tokenizer_class]
														if self.test_rust_tokenizer:
																					tokenizer_classes.append(self.rust_tokenizer_class)

														for tokenizer_class in tokenizer_classes:
																					_lowerCAmelCase	:				int					  =       tokenizer_class.from_pretrained("microsoft/deberta-base")

																					_lowerCAmelCase	:				Union[str, Any]					  =       [
																					    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																					    "ALBERT incorporates two parameter reduction techniques",
																					    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																					    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																					    " vocabulary embedding.",
																					]

																					_lowerCAmelCase	:				Optional[Any]					  =       tokenizer(__a,     padding=__a)
																					_lowerCAmelCase	:				str					  =       [tokenizer.decode(__a,     skip_special_tokens=__a) for seq in encoding["input_ids"]]

																					# fmt: off
																					_lowerCAmelCase	:				Dict					  =       {
																					    "input_ids": [
																					        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
																					    ],
																					    "token_type_ids": [
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																					    ],
																					    "attention_mask": [
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																					    ]
																					}
																					# fmt: on

																					_lowerCAmelCase	:				str					  =       [
																					    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																					    "ALBERT incorporates two parameter reduction techniques",
																					    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																					    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																					    " vocabulary embedding.",
																					]

																					self.assertDictEqual(encoding.data,     __a)

																					for expected, decoded in zip(__a,     __a):
																												self.assertEqual(__a,     __a)





'''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			)			)



