





'''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


__UpperCamelCase					    =	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 :

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


					"""simple docstring"""

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

					__snake_case					:      str							      =     context_length
					__snake_case					:      int							      =     prediction_length + label_length
					__snake_case					:      Optional[Any]							      =     label_length

					__snake_case					:      List[str]							      =     moving_average
					__snake_case					:      str							      =     autocorrelation_factor

		def 	lowercase__     (   self					:   Tuple			)					->    Union[str, Any]:


					"""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					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     config.context_length + max(config.lags_sequence			)

					__snake_case					:      Any							      =     ids_tensor([self.batch_size, 1]  ,      config.cardinality[0]			)
					__snake_case					:      int							      =     floats_tensor([self.batch_size, _past_length, config.num_time_features]			)
					__snake_case					:      List[str]							      =     floats_tensor([self.batch_size, _past_length]			)
					__snake_case					:      List[Any]							      =     floats_tensor([self.batch_size, _past_length]			) > 0.5

					# decoder inputs
					__snake_case					:      List[Any]							      =     floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]			)
					__snake_case					:      Tuple							      =     floats_tensor([self.batch_size, config.prediction_length]			)

					__snake_case					:      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 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.get_config()
					__snake_case					:      Any							      =     self.prepare_autoformer_inputs_dict(__magic_name__			)
					return config, inputs_dict

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.prepare_config_and_inputs()
					return config, inputs_dict

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   int  ,      __magic_name__					:   List[str]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     AutoformerModel(config=__magic_name__			).to(__magic_name__			).eval()
					__snake_case					:      Tuple							      =     model(**__magic_name__			)

					__snake_case					:      Optional[int]							      =     outputs.encoder_last_hidden_state
					__snake_case					:      Any							      =     outputs.last_hidden_state

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

					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     model.create_network_inputs(**__magic_name__			)
					__snake_case      ,     __snake_case					:      List[str]							      =     model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

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

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

					__snake_case					:      Optional[Any]							      =     torch.cat(
					    (
					        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)  ,      dim=1			),
					        feature[:, config.context_length - config.label_length :, ...],
					    )  ,      dim=-1  ,      )
					__snake_case					:      Optional[int]							      =     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					:      List[Any]							      =     model.get_decoder()
								decoder.save_pretrained(__magic_name__			)
								__snake_case					:      Dict							      =     AutoformerDecoder.from_pretrained(__magic_name__			).to(__magic_name__			)

					__snake_case					:      str							      =     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  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   Tuple				     =   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
		lowercase__:   int				     =   (AutoformerForPrediction,) if is_torch_available() else ()
		lowercase__:   int				     =   {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
		lowercase__:   int				     =   False
		lowercase__:   Optional[int]				     =   False
		lowercase__:   Dict				     =   False
		lowercase__:   List[str]				     =   False
		lowercase__:   int				     =   False
		lowercase__:   List[str]				     =   False

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     AutoformerModelTester(self			)
					__snake_case					:      Dict							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      has_text_modality=__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   Optional[int]			)					->    int:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								__snake_case					:      int							      =     model_class(__magic_name__			)

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

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     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 	lowercase__     (   self					:   Optional[Any]			)					->    str:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     inspect.signature(getattr(__magic_name__  ,      """forward"""			)			)
					# The main input is the name of the argument after `self`
					__snake_case					:      List[Any]							      =     list(model_signature.parameters.keys()			)[1]
					self.assertEqual(AutoformerModel.main_input_name  ,      __magic_name__			)

		def 	lowercase__     (   self					:   int			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.model_tester.prepare_config_and_inputs_for_common()

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

								__snake_case					:      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(__magic_name__			)]  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case					:      Any							      =     True

					__snake_case					:      Union[str, Any]							      =     getattr(self.model_tester  ,      """seq_length"""  ,      __magic_name__			)
					__snake_case					:      str							      =     getattr(self.model_tester  ,      """decoder_seq_length"""  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     getattr(self.model_tester  ,      """encoder_seq_length"""  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     getattr(self.model_tester  ,      """d_model"""  ,      __magic_name__			)
					__snake_case					:      str							      =     getattr(self.model_tester  ,      """num_attention_heads"""  ,      __magic_name__			)
					__snake_case					:      Optional[int]							      =     d_model // num_attention_heads

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

								__snake_case					:      Dict							      =     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
								__snake_case					:      Union[str, Any]							      =     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
								__snake_case					:      str							      =     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
					__snake_case					:      str							      =     True
					__snake_case					:      int							      =     True
					__snake_case					:      Any							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					with torch.no_grad():
								__snake_case					:      Optional[int]							      =     model(**self._prepare_for_class(__magic_name__  ,      __magic_name__			)			)

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

					__snake_case					:      List[Any]							      =     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 	lowercase__     (   self					:   List[str]			)					->    int:


					"""simple docstring"""

					super().test_retain_grad_hidden_states_attentions()







def 					_a						(       _lowerCamelCase="train-batch.pt" ) ->				Dict:



			"""simple docstring"""


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




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

		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      int							      =     AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      str							      =     prepare_batch()

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

					__snake_case					:      Any							      =     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 	lowercase__     (   self					:   str			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     prepare_batch("""val-batch.pt"""			)
					with torch.no_grad():
								__snake_case					:      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
					__snake_case					:      Optional[int]							      =     torch.Size((64, model.config.context_length, model.config.d_model)			)
					self.assertEqual(output.shape  ,      __magic_name__			)

					__snake_case					:      Tuple							      =     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 	lowercase__     (   self					:   List[Any]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      Tuple							      =     prepare_batch("""val-batch.pt"""			)
					with torch.no_grad():
								__snake_case					:      Any							      =     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					:      List[Any]							      =     torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)			)
					self.assertEqual(outputs.sequences.shape  ,      __magic_name__			)

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





'''simple docstring'''



import math







def   a__			(    a__		):




     """simple docstring"""


     return math.sqrt(a__		) * math.sqrt(a__		) == num







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = n
     while left <= right:
          __SCREAMING_SNAKE_CASE      = (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               __SCREAMING_SNAKE_CASE      = mid - 1
          else:
               __SCREAMING_SNAKE_CASE      = mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()






