
from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=    logging.get_logger(__name__)

_lowercase											=    {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"autoformer"
       __lowerCamelCase				       =						{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }


       def __init__(    self						,       __A = None						,       __A = None						,       __A = "student_t"						,       __A = "nll"						,       __A = 1						,       __A = [1, 2, 3, 4, 5, 6, 7]						,       __A = True						,       __A = 0						,       __A = 0						,       __A = 0						,       __A = 0						,       __A = None						,       __A = None						,       __A = 64						,       __A = 2						,       __A = 2						,       __A = 2						,       __A = 2						,       __A = 32						,       __A = 32						,       __A = "gelu"						,       __A = 0.1						,       __A = 0.1						,       __A = 0.1						,       __A = 0.1						,       __A = 0.1						,       __A = 100						,       __A = 0.02						,       __A = True						,       __A=True						,       __A = 10						,       __A = 25						,       __A = 3						,       **__A						,       )						->    int:
              # time series specific configuration
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=prediction_length
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=context_length if context_length is not None else prediction_length
              SCREAMING_SNAKE_CASE_							:				Dict		=distribution_output
              SCREAMING_SNAKE_CASE_							:				Tuple		=loss
              SCREAMING_SNAKE_CASE_							:				List[Any]		=input_size
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=num_time_features
              SCREAMING_SNAKE_CASE_							:				List[str]		=lags_sequence
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scaling
              SCREAMING_SNAKE_CASE_							:				int		=num_dynamic_real_features
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_static_real_features
              SCREAMING_SNAKE_CASE_							:				List[str]		=num_static_categorical_features
              if cardinality is not None and num_static_categorical_features > 0:
                     if len(__A				) != num_static_categorical_features:
                            raise ValueError(
                                '''The cardinality should be a list of the same length as `num_static_categorical_features`'''				)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=cardinality
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[0]
              if embedding_dimension is not None and num_static_categorical_features > 0:
                     if len(__A				) != num_static_categorical_features:
                            raise ValueError(
                                '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''				)
                     SCREAMING_SNAKE_CASE_							:				str		=embedding_dimension
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[min(50						,       (cat + 1) // 2				) for cat in self.cardinality]
              SCREAMING_SNAKE_CASE_							:				Tuple		=num_parallel_samples

              # Transformer architecture configuration
              SCREAMING_SNAKE_CASE_							:				Dict		=input_size * len(self.lags_sequence				) + self._number_of_features
              SCREAMING_SNAKE_CASE_							:				Tuple		=d_model
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=encoder_attention_heads
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=decoder_attention_heads
              SCREAMING_SNAKE_CASE_							:				int		=encoder_ffn_dim
              SCREAMING_SNAKE_CASE_							:				Any		=decoder_ffn_dim
              SCREAMING_SNAKE_CASE_							:				Tuple		=encoder_layers
              SCREAMING_SNAKE_CASE_							:				Any		=decoder_layers

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=dropout
              SCREAMING_SNAKE_CASE_							:				List[Any]		=attention_dropout
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=activation_dropout
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=encoder_layerdrop
              SCREAMING_SNAKE_CASE_							:				List[str]		=decoder_layerdrop

              SCREAMING_SNAKE_CASE_							:				List[str]		=activation_function
              SCREAMING_SNAKE_CASE_							:				List[str]		=init_std

              SCREAMING_SNAKE_CASE_							:				Dict		=use_cache

              # Autoformer
              SCREAMING_SNAKE_CASE_							:				Dict		=label_length
              SCREAMING_SNAKE_CASE_							:				int		=moving_average
              SCREAMING_SNAKE_CASE_							:				List[str]		=autocorrelation_factor

              super().__init__(is_encoder_decoder=__A						,       **__A				)





       @property
       def 	_snake_case				(    self				)						->    int:
              return (
                  sum(self.embedding_dimension				)
                  + self.num_dynamic_real_features
                  + self.num_time_features
                  + self.num_static_real_features
                  + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
              )


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
