

'''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


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

      UpperCAmelCase	:     Union[str, Any]						 =			context_length
      UpperCAmelCase	:     Optional[Any]						 =			prediction_length + label_length
      UpperCAmelCase	:     List[Any]						 =			label_length

      UpperCAmelCase	:     str						 =			moving_average
      UpperCAmelCase	:     str						 =			autocorrelation_factor


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[Any]:
      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			:					Optional[Any]					,      lowercase_			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     List[Any]						 =			config.context_length + max(config.lags_sequence	)

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

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

      UpperCAmelCase	:     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        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			self.get_config()
      UpperCAmelCase	:     Optional[int]						 =			self.prepare_autoformer_inputs_dict(snake_case__	)
      return config, inputs_dict


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     int:
      UpperCAmelCase	:     Union[str, Any]						 =			self.prepare_config_and_inputs()
      return config, inputs_dict




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]	)						->     Optional[int]:
      UpperCAmelCase	:     Tuple						 =			AutoformerModel(config=snake_case__	).to(snake_case__	).eval()
      UpperCAmelCase	:     int						 =			model(**snake_case__	)

      UpperCAmelCase	:     List[str]						 =			outputs.encoder_last_hidden_state
      UpperCAmelCase	:     Union[str, Any]						 =			outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase	:     int						 =			model.get_encoder()
       encoder.save_pretrained(snake_case__	)
       UpperCAmelCase	:     Optional[Any]						 =			AutoformerEncoder.from_pretrained(snake_case__	).to(snake_case__	)

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

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

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

      UpperCAmelCase	:     Any						 =			torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)					,      dim=1	),
              feature[:, config.context_length - config.label_length :, ...],
          )					,      dim=-1					,      )
      UpperCAmelCase	:     Union[str, Any]						 =			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	:     Optional[Any]						 =			model.get_decoder()
       decoder.save_pretrained(snake_case__	)
       UpperCAmelCase	:     Optional[int]						 =			AutoformerDecoder.from_pretrained(snake_case__	).to(snake_case__	)

      UpperCAmelCase	:     Union[str, Any]						 =			decoder(
          trend=snake_case__					,      inputs_embeds=snake_case__					,      encoder_hidden_states=snake_case__					,      )[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							):






     '''simple docstring'''



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


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[str]:
      UpperCAmelCase	:     Dict						 =			AutoformerModelTester(self	)
      UpperCAmelCase	:     Any						 =			ConfigTester(self					,      config_class=snake_case__					,      has_text_modality=snake_case__	)


     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      self.config_tester.run_common_tests()


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[Any]:
      UpperCAmelCase	:     Optional[Any]						 =			self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       UpperCAmelCase	:     Any						 =			model_class(snake_case__	)

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


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     str:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*snake_case__	)


     @unittest.skip(reason='Model has no tokens embeddings'	)
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[Any]:
      pass


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


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[str]:
      UpperCAmelCase	:     List[Any]						 =			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:     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(snake_case__	)]					,      snake_case__	)


     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase	:     Union[str, Any]						 =			True

      UpperCAmelCase	:     str						 =			getattr(self.model_tester					,      'seq_length'					,      snake_case__	)
      UpperCAmelCase	:     str						 =			getattr(self.model_tester					,      'decoder_seq_length'					,      snake_case__	)
      UpperCAmelCase	:     List[str]						 =			getattr(self.model_tester					,      'encoder_seq_length'					,      snake_case__	)
      UpperCAmelCase	:     Tuple						 =			getattr(self.model_tester					,      'd_model'					,      snake_case__	)
      UpperCAmelCase	:     List[str]						 =			getattr(self.model_tester					,      'num_attention_heads'					,      snake_case__	)
      UpperCAmelCase	:     Optional[int]						 =			d_model // num_attention_heads

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

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

       UpperCAmelCase	:     List[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(snake_case__					,      snake_case__	)

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

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

      UpperCAmelCase	:     Optional[Any]						 =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(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        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      super().test_retain_grad_hidden_states_attentions()




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




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






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[int]:
      UpperCAmelCase	:     Optional[Any]						 =			AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'	).to(snake_case__	)
      UpperCAmelCase	:     str						 =			prepare_batch()

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

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


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

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




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

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



'''simple docstring'''

from __future__ import annotations




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if len(UpperCAmelCase_     ) == 0:
  raise ValueError('find_max() arg is an empty sequence'     )
 if (
     left >= len(UpperCAmelCase_     )
     or left < -len(UpperCAmelCase_     )
     or right >= len(UpperCAmelCase_     )
     or right < -len(UpperCAmelCase_     )
 ):
  raise IndexError('list index out of range'     )
 if left == right:
  return nums[left]
 UpperCAmelCase	:     Optional[int]						 =			(left + right) >> 1  # the middle
 UpperCAmelCase	:     Any						 =			find_max(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )  # find max in range[left, mid]
 UpperCAmelCase	:     Union[str, Any]						 =			find_max(UpperCAmelCase_  ,	mid + 1  ,	UpperCAmelCase_     )  # find max in range[mid + 1, right]

 return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)




