
'''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   UpperCAmelCase_					:






						'''simple docstring'''






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

												"""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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_config()
												_lowerCAmelCase  						=			self.prepare_autoformer_inputs_dict(_lowercase				)
												return config, inputs_dict




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoformerModel(config=_lowercase				).to(_lowercase				).eval()
												_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			outputs.encoder_last_hidden_state
												_lowerCAmelCase  						=			outputs.last_hidden_state

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

												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			model.create_network_inputs(**_lowercase				)
												_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=_lowercase				)[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(_lowercase				)
																		_lowerCAmelCase  						=			AutoformerDecoder.from_pretrained(_lowercase				).to(_lowercase				)

												_lowerCAmelCase  						=			decoder(
												    trend=_lowercase   ,       inputs_embeds=_lowercase   ,       encoder_hidden_states=_lowercase   ,       )[0]

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






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Union[str, Any]									=       (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
						_lowercase		:				str									=       (AutoformerForPrediction,) if is_torch_available() else ()
						_lowercase		:				Tuple									=       {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
						_lowercase		:				Optional[int]									=       False
						_lowercase		:				int									=       False
						_lowercase		:				int									=       False
						_lowercase		:				Union[str, Any]									=       False
						_lowercase		:				List[Any]									=       False
						_lowercase		:				Optional[int]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoformerModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       has_text_modality=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(_lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*_lowercase				)




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

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			inspect.signature(getattr(_lowercase   ,       """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   ,       _lowercase				)




						def  _lowercase     ( 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(_lowercase				)
																		_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(_lowercase				)]   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												_lowerCAmelCase  						=			True

												_lowerCAmelCase  						=			getattr(self.model_tester   ,       """seq_length"""   ,       _lowercase				)
												_lowerCAmelCase  						=			getattr(self.model_tester   ,       """decoder_seq_length"""   ,       _lowercase				)
												_lowerCAmelCase  						=			getattr(self.model_tester   ,       """encoder_seq_length"""   ,       _lowercase				)
												_lowerCAmelCase  						=			getattr(self.model_tester   ,       """d_model"""   ,       _lowercase				)
												_lowerCAmelCase  						=			getattr(self.model_tester   ,       """num_attention_heads"""   ,       _lowercase				)
												_lowerCAmelCase  						=			d_model // num_attention_heads

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

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

																		_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(_lowercase   ,       _lowercase				)

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

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

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

												self.assertEqual(len(_lowercase				)   ,       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  _lowercase     ( self				):

												"""simple docstring"""



												super().test_retain_grad_hidden_states_attentions()






def A				(__lowerCamelCase				:Tuple="train-batch.pt"		):
						_lowerCAmelCase  						=			hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""			,     filename=__lowerCamelCase			,     repo_type="""dataset"""		)
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location=__lowerCamelCase		)
						return batch





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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(_lowercase				)
												_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   ,       _lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(_lowercase				)
												_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   ,       _lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(_lowercase				)
												_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   ,       _lowercase				)

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


'''simple docstring'''



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase    =		10


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						for i in range(__lowerCamelCase			,     __lowerCamelCase		):
												if array[i] == target:
																		return i
						return -1


def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			len(__lowerCamelCase		)
						while left <= right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		_lowerCAmelCase  						=			one_third - 1
												elif array[two_third] < target:
																		_lowerCAmelCase  						=			two_third + 1

												else:
																		_lowerCAmelCase  						=			one_third + 1
																		_lowerCAmelCase  						=			two_third - 1
						else:
												return -1


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						if left < right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		return rec_ternary_search(__lowerCamelCase			,     one_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
												elif array[two_third] < target:
																		return rec_ternary_search(two_third + 1			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												else:
																		return rec_ternary_search(one_third + 1			,     two_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_lowercase    =		input("""Enter numbers separated by comma:\n""").strip()
						_lowercase    =		[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), F"List must be ordered.\n{collection}."
						_lowercase    =		int(input("""Enter the number to be found in the list:\n""").strip())
						_lowercase    =		ite_ternary_search(collection, target)
						_lowercase    =		rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(F"""Iterative search: {target} found at positions: {resulta}""")
												print(F"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")
