
"""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						=			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 	_lowercase       :




	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		,			)		->							Union[str, Any]:
			lowerCamelCase					:      List[str]          =   d_model
			lowerCamelCase					:      Optional[Any]          =   parent
			lowerCamelCase					:      Union[str, Any]          =   batch_size
			lowerCamelCase					:      List[str]          =   prediction_length
			lowerCamelCase					:      Tuple          =   context_length
			lowerCamelCase					:      int          =   cardinality
			lowerCamelCase					:      Dict          =   num_time_features
			lowerCamelCase					:      Tuple          =   lags_sequence
			lowerCamelCase					:      Union[str, Any]          =   embedding_dimension
			lowerCamelCase					:      Tuple          =   is_training
			lowerCamelCase					:      int          =   hidden_size
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      Any          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   intermediate_size
			lowerCamelCase					:      Dict          =   hidden_act
			lowerCamelCase					:      int          =   hidden_dropout_prob
			lowerCamelCase					:      str          =   attention_probs_dropout_prob

			lowerCamelCase					:      str          =   context_length
			lowerCamelCase					:      Optional[Any]          =   prediction_length + label_length
			lowerCamelCase					:      Tuple          =   label_length

			lowerCamelCase					:      Dict          =   moving_average
			lowerCamelCase					:      List[str]          =   autocorrelation_factor




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   config.context_length + max(config.lags_sequence )

			lowerCamelCase					:      Dict          =   ids_tensor([self.batch_size, 1]		,			config.cardinality[0] )
			lowerCamelCase					:      Union[str, Any]          =   floats_tensor([self.batch_size, _past_length, config.num_time_features] )
			lowerCamelCase					:      int          =   floats_tensor([self.batch_size, _past_length] )
			lowerCamelCase					:      Tuple          =   floats_tensor([self.batch_size, _past_length] ) > 0.5

			# decoder inputs
			lowerCamelCase					:      Dict          =   floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
			lowerCamelCase					:      Optional[Any]          =   floats_tensor([self.batch_size, config.prediction_length] )

			lowerCamelCase					:      Union[str, Any]          =   {
			    '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       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      int          =   self.get_config()
			lowerCamelCase					:      Any          =   self.prepare_autoformer_inputs_dict(UpperCAmelCase_ )
			return config, inputs_dict




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   self.prepare_config_and_inputs()
			return config, inputs_dict




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   AutoformerModel(config=UpperCAmelCase_ ).to(UpperCAmelCase_ ).eval()
			lowerCamelCase					:      Union[str, Any]          =   model(**UpperCAmelCase_ )

			lowerCamelCase					:      List[str]          =   outputs.encoder_last_hidden_state
			lowerCamelCase					:      Optional[int]          =   outputs.last_hidden_state

			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase					:      str          =   model.get_encoder()
					encoder.save_pretrained(UpperCAmelCase_ )
					lowerCamelCase					:      List[str]          =   AutoformerEncoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )

			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model.create_network_inputs(**UpperCAmelCase_ )
			lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )

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

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

			lowerCamelCase					:      str          =   torch.cat(
			    (
			        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)		,			dim=1 ),
			        feature[:, config.context_length - config.label_length :, ...],
			    )		,			dim=-1		,			)
			lowerCamelCase					:      Optional[Any]          =   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					:      Any          =   model.get_decoder()
					decoder.save_pretrained(UpperCAmelCase_ )
					lowerCamelCase					:      Optional[Any]          =   AutoformerDecoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )

			lowerCamelCase					:      Any          =   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 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
	lowercase_				=     (AutoformerForPrediction,) if is_torch_available() else ()
	lowercase_				=     {'feature-extraction': AutoformerModel} if is_torch_available() else {}
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Any          =   AutoformerModelTester(self )
			lowerCamelCase					:      int          =   ConfigTester(self		,			config_class=UpperCAmelCase_		,			has_text_modality=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			self.config_tester.run_common_tests()




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					lowerCamelCase					:      Dict          =   model_class(UpperCAmelCase_ )

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




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      int          =   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       _UpperCamelCase   (			self )		->							Optional[int]:
			pass




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Dict          =   inspect.signature(getattr(UpperCAmelCase_		,			'forward' ) )
			# The main input is the name of the argument after `self`
			lowerCamelCase					:      Tuple          =   list(model_signature.parameters.keys() )[1]
			self.assertEqual(AutoformerModel.main_input_name		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase					:      List[Any]          =   [
					    '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       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   self.model_tester.prepare_config_and_inputs_for_common()
			lowerCamelCase					:      Union[str, Any]          =   True

			lowerCamelCase					:      Optional[int]          =   getattr(self.model_tester		,			'seq_length'		,			UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   getattr(self.model_tester		,			'decoder_seq_length'		,			UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   getattr(self.model_tester		,			'encoder_seq_length'		,			UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   getattr(self.model_tester		,			'd_model'		,			UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   getattr(self.model_tester		,			'num_attention_heads'		,			UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   d_model // num_attention_heads

			for model_class in self.all_model_classes:
					lowerCamelCase					:      int          =   True
					lowerCamelCase					:      int          =   False
					lowerCamelCase					:      List[Any]          =   True
					lowerCamelCase					:      Union[str, Any]          =   model_class(UpperCAmelCase_ )
					model.to(UpperCAmelCase_ )
					model.eval()
					with torch.no_grad():
							lowerCamelCase					:      Union[str, Any]          =   model(**self._prepare_for_class(UpperCAmelCase_		,			UpperCAmelCase_ ) )
					lowerCamelCase					:      Dict          =   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					:      Any          =   True
					lowerCamelCase					:      List[str]          =   model_class(UpperCAmelCase_ )
					model.to(UpperCAmelCase_ )
					model.eval()
					with torch.no_grad():
							lowerCamelCase					:      int          =   model(**self._prepare_for_class(UpperCAmelCase_		,			UpperCAmelCase_ ) )
					lowerCamelCase					:      str          =   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					:      List[str]          =   len(UpperCAmelCase_ )

					lowerCamelCase					:      List[str]          =   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					:      int          =   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					:      Tuple          =   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					:      List[str]          =   True
			lowerCamelCase					:      Any          =   True
			lowerCamelCase					:      Union[str, Any]          =   model_class(UpperCAmelCase_ )
			model.to(UpperCAmelCase_ )
			model.eval()
			with torch.no_grad():
					lowerCamelCase					:      str          =   model(**self._prepare_for_class(UpperCAmelCase_		,			UpperCAmelCase_ ) )

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

			lowerCamelCase					:      List[Any]          =   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       _UpperCamelCase   (			self )		->							Union[str, Any]:
			super().test_retain_grad_hidden_states_attentions()


def 		UpperCAmelCase (		a_="train-batch.pt"      ):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch',						filename=a_,						repo_type='dataset'      )
		lowerCamelCase					:      Union[str, Any]          =   torch.load(a_,						map_location=a_      )
		return batch






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




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[int]          =   prepare_batch()

			with torch.no_grad():
					lowerCamelCase					:      List[str]          =   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					:      List[str]          =   torch.Size(
			    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
			self.assertEqual(output.shape		,			UpperCAmelCase_ )

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




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Tuple          =   AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   prepare_batch('val-batch.pt' )
			with torch.no_grad():
					lowerCamelCase					:      Optional[int]          =   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					:      List[Any]          =   torch.Size((64, model.config.context_length, model.config.d_model) )
			self.assertEqual(output.shape		,			UpperCAmelCase_ )

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




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      int          =   AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   prepare_batch('val-batch.pt' )
			with torch.no_grad():
					lowerCamelCase					:      str          =   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					:      str          =   torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
			self.assertEqual(outputs.sequences.shape		,			UpperCAmelCase_ )

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








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






