





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowercase    : str    	=  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,       A,       A=16,       A=13,       A=7,       A=14,       A=10,       A=19,       A=5,       A=4,       A=True,       A=16,       A=2,       A=4,       A=4,       A="gelu",       A=0.1,       A=0.1,       A=[1, 2, 3, 4, 5],       A=25,       A=5,       ):





            """simple docstring"""

            _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      ):





            """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 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_config()
            _UpperCamelCase              =						self.prepare_autoformer_inputs_dict(_a      )
            return config, inputs_dict

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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



     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _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(						__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE						,			unittest.TestCase ):
     __A	  =       (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
     __A	  =       (AutoformerForPrediction,) if is_torch_available() else ()
     __A	  =       {'feature-extraction': AutoformerModel} if is_torch_available() else {}
     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						AutoformerModelTester(self      )
            _UpperCamelCase              =						ConfigTester(self,       config_class=_a,       has_text_modality=_a      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            _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      ):





            """simple docstring"""

            super().test_retain_grad_hidden_states_attentions()






def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase="train-batch.pt"					):

       '''simple docstring'''





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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(
                [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]],       device=_a      )
            self.assertTrue(torch.allclose(output[0, :3, :3],       _a,       atol=_a      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(
                [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]],       device=_a      )
            self.assertTrue(torch.allclose(output[0, :3, :3],       _a,       atol=_a      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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([3_130.6_763, 4_056.5_293, 7_053.0_786],       device=_a      )
            _UpperCamelCase              =						outputs.sequences.mean(dim=1      )
            self.assertTrue(torch.allclose(mean_prediction[0, -3:],       _a,       rtol=1E-1      )      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






