"""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


SCREAMING_SNAKE_CASE__					  =     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_			:



				"""simple docstring"""

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






										"""simple docstring"""

										snake_case	    =	d_model
										snake_case	    =	parent
										snake_case	    =	batch_size
										snake_case	    =	prediction_length
										snake_case	    =	context_length
										snake_case	    =	cardinality
										snake_case	    =	num_time_features
										snake_case	    =	lags_sequence
										snake_case	    =	embedding_dimension
										snake_case	    =	is_training
										snake_case	    =	hidden_size
										snake_case	    =	num_hidden_layers
										snake_case	    =	num_attention_heads
										snake_case	    =	intermediate_size
										snake_case	    =	hidden_act
										snake_case	    =	hidden_dropout_prob
										snake_case	    =	attention_probs_dropout_prob

										snake_case	    =	context_length
										snake_case	    =	prediction_length + label_length
										snake_case	    =	label_length

										snake_case	    =	moving_average
										snake_case	    =	autocorrelation_factor

				def 						snake_case       ( 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 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	config.context_length + max(config.lags_sequence			)

										snake_case	    =	ids_tensor([self.batch_size, 1]						,			config.cardinality[0]			)
										snake_case	    =	floats_tensor([self.batch_size, _past_length, config.num_time_features]			)
										snake_case	    =	floats_tensor([self.batch_size, _past_length]			)
										snake_case	    =	floats_tensor([self.batch_size, _past_length]			) > 0.5

										# decoder inputs
										snake_case	    =	floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]			)
										snake_case	    =	floats_tensor([self.batch_size, config.prediction_length]			)

										snake_case	    =	{
										    '''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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.get_config()
										snake_case	    =	self.prepare_autoformer_inputs_dict(__lowercase			)
										return config, inputs_dict

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.prepare_config_and_inputs()
										return config, inputs_dict

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	AutoformerModel(config=__lowercase			).to(__lowercase			).eval()
										snake_case	    =	model(**__lowercase			)

										snake_case	    =	outputs.encoder_last_hidden_state
										snake_case	    =	outputs.last_hidden_state

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

										snake_case	    =	model.create_network_inputs(**__lowercase			)
										snake_case	    =	model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

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

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

										snake_case	    =	torch.cat(
										    (
										        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)						,			dim=1			),
										        feature[:, config.context_length - config.label_length :, ...],
										    )						,			dim=-1						,			)
										snake_case	    =	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:
																snake_case	    =	model.get_decoder()
																decoder.save_pretrained(__lowercase			)
																snake_case	    =	AutoformerDecoder.from_pretrained(__lowercase			).to(__lowercase			)

										snake_case	    =	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      lowerCAmelCase_			(	__lowerCamelCase   ,			__lowerCamelCase   ,			unittest.TestCase    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Optional[int]	     =  (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
				_lowerCAmelCase   :			List[Any]	     =  (AutoformerForPrediction,) if is_torch_available() else ()
				_lowerCAmelCase   :			str	     =  {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
				_lowerCAmelCase   :			Dict	     =  False
				_lowerCAmelCase   :			List[str]	     =  False
				_lowerCAmelCase   :			Optional[int]	     =  False
				_lowerCAmelCase   :			Tuple	     =  False
				_lowerCAmelCase   :			Dict	     =  False
				_lowerCAmelCase   :			Tuple	     =  False

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	AutoformerModelTester(self			)
										snake_case	    =	ConfigTester(self						,			config_class=__lowercase						,			has_text_modality=__lowercase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																snake_case	    =	model_class(__lowercase			)

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

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										pass

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	inspect.signature(getattr(__lowercase						,			'forward'			)			)
										# The main input is the name of the argument after `self`
										snake_case	    =	list(model_signature.parameters.keys()			)[1]
										self.assertEqual(AutoformerModel.main_input_name						,			__lowercase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case	    =	[
																    '''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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()
										snake_case	    =	True

										snake_case	    =	getattr(self.model_tester						,			'seq_length'						,			__lowercase			)
										snake_case	    =	getattr(self.model_tester						,			'decoder_seq_length'						,			__lowercase			)
										snake_case	    =	getattr(self.model_tester						,			'encoder_seq_length'						,			__lowercase			)
										snake_case	    =	getattr(self.model_tester						,			'd_model'						,			__lowercase			)
										snake_case	    =	getattr(self.model_tester						,			'num_attention_heads'						,			__lowercase			)
										snake_case	    =	d_model // num_attention_heads

										for model_class in self.all_model_classes:
																snake_case	    =	True
																snake_case	    =	False
																snake_case	    =	True
																snake_case	    =	model_class(__lowercase			)
																model.to(__lowercase			)
																model.eval()
																with torch.no_grad():
																						snake_case	    =	model(**self._prepare_for_class(__lowercase						,			__lowercase			)			)
																snake_case	    =	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"]
																snake_case	    =	True
																snake_case	    =	model_class(__lowercase			)
																model.to(__lowercase			)
																model.eval()
																with torch.no_grad():
																						snake_case	    =	model(**self._prepare_for_class(__lowercase						,			__lowercase			)			)
																snake_case	    =	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]						,			)
																snake_case	    =	len(__lowercase			)

																snake_case	    =	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
																snake_case	    =	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
																snake_case	    =	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
										snake_case	    =	True
										snake_case	    =	True
										snake_case	    =	model_class(__lowercase			)
										model.to(__lowercase			)
										model.eval()
										with torch.no_grad():
																snake_case	    =	model(**self._prepare_for_class(__lowercase						,			__lowercase			)			)

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

										snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										super().test_retain_grad_hidden_states_attentions()





def       lowerCAmelCase__			(	_UpperCamelCase				: Any="train-batch.pt"    )    -> int:





						"""simple docstring"""






						snake_case	    =	hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'       ,	filename=__lowerCAmelCase       ,	repo_type='dataset'    )
						snake_case	    =	torch.load(__lowerCAmelCase       ,	map_location=__lowerCAmelCase    )
						return batch



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



				"""simple docstring"""

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'			).to(__lowercase			)
										snake_case	    =	prepare_batch()

										with torch.no_grad():
																snake_case	    =	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]

										snake_case	    =	torch.Size(
										    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)			)
										self.assertEqual(output.shape						,			__lowercase			)

										snake_case	    =	torch.tensor(
										    [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]						,			device=__lowercase			)
										self.assertTrue(torch.allclose(output[0, :3, :3]						,			__lowercase						,			atol=__lowercase			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'			).to(__lowercase			)
										snake_case	    =	prepare_batch('val-batch.pt'			)
										with torch.no_grad():
																snake_case	    =	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
										snake_case	    =	torch.Size((64, model.config.context_length, model.config.d_model)			)
										self.assertEqual(output.shape						,			__lowercase			)

										snake_case	    =	torch.tensor(
										    [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]						,			device=__lowercase			)
										self.assertTrue(torch.allclose(output[0, :3, :3]						,			__lowercase						,			atol=__lowercase			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'			).to(__lowercase			)
										snake_case	    =	prepare_batch('val-batch.pt'			)
										with torch.no_grad():
																snake_case	    =	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']						,			)
										snake_case	    =	torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)			)
										self.assertEqual(outputs.sequences.shape						,			__lowercase			)

										snake_case	    =	torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]						,			device=__lowercase			)
										snake_case	    =	outputs.sequences.mean(dim=1			)
										self.assertTrue(torch.allclose(mean_prediction[0, -3:]						,			__lowercase						,			rtol=1E-1			)			)







from math import ceil, sqrt



def 			_lowerCAmelCase       (					__lowerCAmelCase = 1000000      )   ->					int:


				"""simple docstring"""

				snake_case__	:      Dict     		=	0

				for outer_width in range(3  ,  (limit // 4) + 2      ):
								if outer_width**2 > limit:
												snake_case__	:      Any     		=	max(ceil(sqrt(outer_width**2 - limit      )      )  ,  1      )
								else:
												snake_case__	:      int     		=	1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


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

