



"""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


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       A_			:



						"""simple docstring"""






						def __init__(					self :Union[str, Any]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[Any]=16   ,  lowercase_ :str=13   ,  lowercase_ :str=7   ,  lowercase_ :Optional[int]=14   ,  lowercase_ :Tuple=10   ,  lowercase_ :Dict=19   ,  lowercase_ :Optional[Any]=5   ,  lowercase_ :Any=4   ,  lowercase_ :int=True   ,  lowercase_ :List[str]=16   ,  lowercase_ :Tuple=2   ,  lowercase_ :Any=4   ,  lowercase_ :Dict=4   ,  lowercase_ :Optional[Any]="gelu"   ,  lowercase_ :Tuple=0.1   ,  lowercase_ :Any=0.1   ,  lowercase_ :Optional[int]=[1, 2, 3, 4, 5]   ,  lowercase_ :str=25   ,  lowercase_ :Optional[Any]=5   ,  )				->			Dict:
												UpperCAmelCase             = d_model
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = prediction_length
												UpperCAmelCase             = context_length
												UpperCAmelCase             = cardinality
												UpperCAmelCase             = num_time_features
												UpperCAmelCase             = lags_sequence
												UpperCAmelCase             = embedding_dimension
												UpperCAmelCase             = is_training
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob

												UpperCAmelCase             = context_length
												UpperCAmelCase             = prediction_length + label_length
												UpperCAmelCase             = label_length

												UpperCAmelCase             = moving_average
												UpperCAmelCase             = autocorrelation_factor






						def 	UpperCAmelCase__     (					self :str					)				->			Dict:
												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 :Any   ,  lowercase_ :Union[str, Any]					)				->			Any:
												UpperCAmelCase             = config.context_length + max(config.lags_sequence					)

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

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

												UpperCAmelCase             = {
												    '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 :Tuple					)				->			Dict:
												UpperCAmelCase             = self.get_config()
												UpperCAmelCase             = self.prepare_autoformer_inputs_dict(a__					)
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Any:
												UpperCAmelCase      ,    UpperCAmelCase             = self.prepare_config_and_inputs()
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :int   ,  lowercase_ :List[str]					)				->			Any:
												UpperCAmelCase             = AutoformerModel(config=a__					).to(a__					).eval()
												UpperCAmelCase             = model(**a__					)

												UpperCAmelCase             = outputs.encoder_last_hidden_state
												UpperCAmelCase             = outputs.last_hidden_state

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

												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = model.create_network_inputs(**a__					)
												UpperCAmelCase      ,    UpperCAmelCase             = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]					)

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

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

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

												UpperCAmelCase             = 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_			(     _UpperCAmelCase	,   _UpperCAmelCase	,   unittest.TestCase				):



						"""simple docstring"""
						__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 	UpperCAmelCase__     (					self :Optional[Any]					)				->			List[Any]:
												UpperCAmelCase             = AutoformerModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=a__   ,  has_text_modality=a__					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Tuple:
												self.config_tester.run_common_tests()






						def 	UpperCAmelCase__     (					self :int					)				->			str:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		UpperCAmelCase             = model_class(a__					)

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






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Union[str, Any]:
												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :str					)				->			Dict:
												pass






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






						def 	UpperCAmelCase__     (					self :Any					)				->			Dict:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase             = [
																		    '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 	UpperCAmelCase__     (					self :str					)				->			Optional[int]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = True

												UpperCAmelCase             = getattr(self.model_tester   ,  'seq_length'   ,  a__					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'decoder_seq_length'   ,  a__					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'encoder_seq_length'   ,  a__					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'd_model'   ,  a__					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'num_attention_heads'   ,  a__					)
												UpperCAmelCase             = d_model // num_attention_heads

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

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

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

												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :str					)				->			Dict:
												super().test_retain_grad_hidden_states_attentions()


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







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



						"""simple docstring"""






						def 	UpperCAmelCase__     (					self :Dict					)				->			Optional[int]:
												UpperCAmelCase             = AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(a__					)
												UpperCAmelCase             = prepare_batch()

												with torch.no_grad():
																		UpperCAmelCase             = 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]

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

												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :List[Any]					)				->			Any:
												UpperCAmelCase             = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'					).to(a__					)
												UpperCAmelCase             = prepare_batch('val-batch.pt'					)
												with torch.no_grad():
																		UpperCAmelCase             = 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
												UpperCAmelCase             = torch.Size((64, model.config.context_length, model.config.d_model)					)
												self.assertEqual(output.shape   ,  a__					)

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

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












from math import pi






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				float:
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))

