




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


__UpperCamelCase					:       Dict    					=      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 		__lowerCAmelCase	:
							def __init__(						self						:Dict		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:List[Any]=16		,      __magic_name__						:List[Any]=13		,      __magic_name__						:Tuple=7		,      __magic_name__						:Dict=14		,      __magic_name__						:Any=10		,      __magic_name__						:str=19		,      __magic_name__						:Optional[int]=5		,      __magic_name__						:List[str]=4		,      __magic_name__						:Optional[int]=True		,      __magic_name__						:int=16		,      __magic_name__						:List[Any]=2		,      __magic_name__						:Optional[Any]=4		,      __magic_name__						:Optional[int]=4		,      __magic_name__						:Tuple="gelu"		,      __magic_name__						:str=0.1		,      __magic_name__						:Optional[int]=0.1		,      __magic_name__						:List[str]=[1, 2, 3, 4, 5]		,      __magic_name__						:List[str]=25		,      __magic_name__						:Tuple=5		,      ):







								'''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       lowerCamelCase__							(						self						: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       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:int				):







								'''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       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							self.get_config()
								a										=							self.prepare_autoformer_inputs_dict(__magic_name__				)
								return config, inputs_dict
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:List[Any]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							AutoformerModel(config=__magic_name__				).to(__magic_name__				).eval()
								a										=							model(**__magic_name__				)

								a										=							outputs.encoder_last_hidden_state
								a										=							outputs.last_hidden_state

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

								a				, a				, a				, a				, a										=							model.create_network_inputs(**__magic_name__				)
								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=__magic_name__				)[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(__magic_name__				)
									a										=							AutoformerDecoder.from_pretrained(__magic_name__				).to(__magic_name__				)

								a										=							decoder(
								    trend=__magic_name__		,      inputs_embeds=__magic_name__		,      encoder_hidden_states=__magic_name__		,      )[0]

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







@require_torch
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							UpperCamelCase__					     =  (AutoformerForPrediction,) if is_torch_available() else ()
							UpperCamelCase__					     =  {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							AutoformerModelTester(self				)
								a										=							ConfigTester(self		,      config_class=__magic_name__		,      has_text_modality=__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									a										=							model_class(__magic_name__				)

									with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__magic_name__				)
										a				, a										=							model_class.from_pretrained(__magic_name__		,      output_loading_info=__magic_name__				)
									self.assertEqual(info["""missing_keys"""]		,      []				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_encoder_decoder_model_standalone(*__magic_name__				)
							@unittest.skip(reason="""Model has no tokens embeddings"""				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							inspect.signature(getattr(__magic_name__		,      """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		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a										=							model_class(__magic_name__				)
									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(__magic_name__				)]		,      __magic_name__				)
							def       lowerCamelCase__							(						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"""		,      __magic_name__				)
								a										=							getattr(self.model_tester		,      """decoder_seq_length"""		,      __magic_name__				)
								a										=							getattr(self.model_tester		,      """encoder_seq_length"""		,      __magic_name__				)
								a										=							getattr(self.model_tester		,      """d_model"""		,      __magic_name__				)
								a										=							getattr(self.model_tester		,      """num_attention_heads"""		,      __magic_name__				)
								a										=							d_model // num_attention_heads

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

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

									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(__magic_name__		,      __magic_name__				)

									# decoder attentions
									a										=							outputs.decoder_attentions
									self.assertIsInstance(__magic_name__		,      (list, tuple)				)
									self.assertEqual(len(__magic_name__				)		,      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(__magic_name__		,      (list, tuple)				)
									self.assertEqual(len(__magic_name__				)		,      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(__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								with torch.no_grad():
									a										=							model(**self._prepare_for_class(__magic_name__		,      __magic_name__				)				)

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

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

								self.assertEqual(len(__magic_name__				)		,      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       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_retain_grad_hidden_states_attentions()







def 		__A		(	__lowerCamelCase="train-batch.pt"    )      ->       Union[str, Any]:
	a										=							hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,			filename=__lowerCamelCase							,			repo_type="""dataset"""    )
	a										=							torch.load(__lowerCamelCase							,			map_location=__lowerCamelCase    )
	return batch



@require_torch
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(__magic_name__				)
								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		,      __magic_name__				)

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







								'''simple docstring'''







								a										=							AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(__magic_name__				)
								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		,      __magic_name__				)

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





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(__magic_name__				)
								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		,      __magic_name__				)

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







def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							0
	while num > 0:
		digit_sum += num % 10
		num //= 10
	return digit_sum





def 		__A		(	__lowerCamelCase = 100    )      ->       int:
	a										=							1
	a										=							2

	for i in range(2							,			max_n + 1    ):
		a										=							pre_numerator
		a										=							2 * i // 3 if i % 3 == 0 else 1
		a										=							cur_numerator
		a										=							e_cont * pre_numerator + temp

	return sum_digits(__lowerCamelCase    )


if __name__ == "__main__":
		print(F'{solution() = }')

