
"""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__      :    Tuple    							= 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   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=13   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=14   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=19   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=[1, 2, 3, 4, 5]   ,       SCREAMING_SNAKE_CASE__=25   ,       SCREAMING_SNAKE_CASE__=5   ,       )					->		Dict:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   context_length
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction_length + label_length
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   label_length

											SCREAMING_SNAKE_CASE__						:       Tuple           =   moving_average
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   autocorrelation_factor



				def 	__magic_name__    (self    )					->		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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   config.context_length + max(config.lags_sequence    )

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

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

											SCREAMING_SNAKE_CASE__						:       str           =   {
											    """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 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_config()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.prepare_autoformer_inputs_dict(SCREAMING_SNAKE_CASE__    )
											return config, inputs_dict



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.prepare_config_and_inputs()
											return config, inputs_dict



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   AutoformerModel(config=SCREAMING_SNAKE_CASE__    ).to(SCREAMING_SNAKE_CASE__    ).eval()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   outputs.encoder_last_hidden_state
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   outputs.last_hidden_state

											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model.get_encoder()
																		encoder.save_pretrained(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   AutoformerEncoder.from_pretrained(SCREAMING_SNAKE_CASE__    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   model.create_network_inputs(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]    )

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

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

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

											SCREAMING_SNAKE_CASE__						:       str           =   decoder(
											    trend=SCREAMING_SNAKE_CASE__   ,       inputs_embeds=SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__   ,       )[0]

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






@require_torch
class 	lowerCAmelCase_						(a__ ,					a__ ,					unittest.TestCase  ):


				"""simple docstring"""



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



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   AutoformerModelTester(self    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ConfigTester(self   ,       config_class=SCREAMING_SNAKE_CASE__   ,       has_text_modality=SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											self.config_tester.run_common_tests()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model_class(SCREAMING_SNAKE_CASE__    )

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



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE__    )



				@unittest.skip(reason="""Model has no tokens embeddings"""    )
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model_class(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       int           =   inspect.signature(model.forward    )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [*signature.parameters.keys()]

																		SCREAMING_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(SCREAMING_SNAKE_CASE__    )]   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   True

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   getattr(self.model_tester   ,       """seq_length"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   getattr(self.model_tester   ,       """decoder_seq_length"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   getattr(self.model_tester   ,       """encoder_seq_length"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   getattr(self.model_tester   ,       """d_model"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   getattr(self.model_tester   ,       """num_attention_heads"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   d_model // num_attention_heads

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   True
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   False
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   True
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model_class(SCREAMING_SNAKE_CASE__    )
																		model.to(SCREAMING_SNAKE_CASE__    )
																		model.eval()
																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )
																		SCREAMING_SNAKE_CASE__						:       str           =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       self.model_tester.num_hidden_layers    )

																		# check that output_attentions also work using config
																		del inputs_dict["output_attentions"]
																		SCREAMING_SNAKE_CASE__						:       Dict           =   True
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   model_class(SCREAMING_SNAKE_CASE__    )
																		model.to(SCREAMING_SNAKE_CASE__    )
																		model.eval()
																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   outputs.encoder_attentions
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       self.model_tester.num_hidden_layers    )

																		self.assertListEqual(
																		    list(attentions[0].shape[-3:]    )   ,       [self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,       )
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       str           =   7

																		if "last_hidden_state" in outputs:
																									correct_outlen += 1

																		if "trend" in outputs:
																									correct_outlen += 1

																		if "past_key_values" in outputs:
																									correct_outlen += 1  # past_key_values have been returned

																		if "loss" in outputs:
																									correct_outlen += 1

																		if "params" in outputs:
																									correct_outlen += 1

																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

																		# decoder attentions
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   outputs.decoder_attentions
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       (list, tuple)    )
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       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
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   outputs.cross_attentions
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       (list, tuple)    )
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       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
											SCREAMING_SNAKE_CASE__						:       Dict           =   True
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   True
											SCREAMING_SNAKE_CASE__						:       str           =   model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       str           =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )

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

											SCREAMING_SNAKE_CASE__						:       Any           =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

											self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											super().test_retain_grad_hidden_states_attentions()






def lowercase_  (   _snake_case="train-batch.pt"						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""   ,filename=_snake_case   ,repo_type="""dataset"""						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.load(_snake_case   ,map_location=_snake_case						)
							return batch



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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   prepare_batch()

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       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"""]   ,       future_values=batch["""future_values"""]   ,       future_time_features=batch["""future_time_features"""]   ,       )[0]

											SCREAMING_SNAKE_CASE__						:       int           =   torch.Size(
											    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)    )
											self.assertEqual(output.shape   ,       SCREAMING_SNAKE_CASE__    )

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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   prepare_batch("""val-batch.pt"""    )
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       List[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"""]   ,       ).encoder_last_hidden_state
											SCREAMING_SNAKE_CASE__						:       Dict           =   torch.Size((64, model.config.context_length, model.config.d_model)    )
											self.assertEqual(output.shape   ,       SCREAMING_SNAKE_CASE__    )

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



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



											"""simple docstring"""




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

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






"""simple docstring"""




import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


UpperCAmelCase__      :    Optional[int]    							= {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

UpperCAmelCase__      :    List[Any]    							= logging.WARNING

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   os.getenv("""DATASETS_VERBOSITY"""   ,_snake_case						)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
																					    f'''has to be one of: { ', '.join(log_levels.keys()						) }'''						)
							return _default_log_level

def lowercase_  (   ):
							return __name__.split("""."""						)[0]

def lowercase_  (   ):
							return logging.getLogger(_get_library_name()						)

def lowercase_  (   ):
							# Apply our default configuration to the library root logger.
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET						)

def lowercase_  (   _snake_case = None						):
							if name is None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_name()
							return logging.getLogger(_snake_case						)

def lowercase_  (   ):
							return _get_library_root_logger().getEffectiveLevel()

def lowercase_  (   _snake_case						):
							_get_library_root_logger().setLevel(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Tuple           =   False

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		int:  # pylint: disable=unused-argument



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   args[0] if args else None



				def __iter__(self    )					->		int:



											"""simple docstring"""




											return iter(self._iterator    )



				def __getattr__(self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											def empty_fn(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    ):  # pylint: disable=unused-argument
																		return

											return empty_fn



				def __enter__(self    )					->		Dict:



											"""simple docstring"""




											return self



				def __exit__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											return






UpperCAmelCase__      :    str    							= True



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __call__(self   ,       *SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											if _tqdm_active and not disable:
																		return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											else:
																		return EmptyTqdm(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   None
											if _tqdm_active:
																		return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if _tqdm_active:
																		return tqdm_lib.tqdm.get_lock()






UpperCAmelCase__      :    Tuple    							= _tqdm_cls()

def lowercase_  (   ):
							global _tqdm_active
							return bool(_tqdm_active						)

def lowercase_  (   ):
							global _tqdm_active
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   True

def lowercase_  (   ):
							global _tqdm_active
							SCREAMING_SNAKE_CASE__						:       str           =   False




