




"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						(CMStochasticIterativeScheduler,)
						_a    						=						10


						def   __lowercase (							self				:			Dict						,						**lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				{
										    """num_train_timesteps""": 201,
										    """sigma_min""": 0.002,
										    """sigma_max""": 80.0,
										}

										config.update(**lowerCAmelCase					)
										return config


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				10

										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				self.scheduler_classes[0](**lowerCAmelCase					)

										scheduler.set_timesteps(lowerCAmelCase					)

										lowerCAmelCase  =				scheduler.timesteps[0]
										lowerCAmelCase  =				scheduler.timesteps[1]

										lowerCAmelCase  =				self.dummy_sample
										lowerCAmelCase  =				0.1 * sample

										lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					).prev_sample
										lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					).prev_sample

										self.assertEqual(output_a.shape						,						sample.shape					)
										self.assertEqual(output_a.shape						,						output_a.shape					)


						def   __lowercase (							self				:			Dict					):
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowerCAmelCase					)


						def   __lowercase (							self				:			str					):
										for clip_denoised in [True, False]:
														self.check_over_configs(clip_denoised=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				1
										scheduler.set_timesteps(lowerCAmelCase					)
										lowerCAmelCase  =				scheduler.timesteps

										lowerCAmelCase  =				torch.manual_seed(0					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(lowerCAmelCase					):
														# 1. scale model input
														lowerCAmelCase  =				scheduler.scale_model_input(lowerCAmelCase						,						lowerCAmelCase					)

														# 2. predict noise residual
														lowerCAmelCase  =				model(lowerCAmelCase						,						lowerCAmelCase					)

														# 3. predict previous sample x_t-1
														lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						generator=lowerCAmelCase					).prev_sample

														lowerCAmelCase  =				pred_prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(lowerCAmelCase					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(lowerCAmelCase					)					)

										assert abs(result_sum.item() - 192.7614					) < 1e-2
										assert abs(result_mean.item() - 0.2510					) < 1e-3


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[106, 0]
										scheduler.set_timesteps(timesteps=lowerCAmelCase					)
										lowerCAmelCase  =				scheduler.timesteps

										lowerCAmelCase  =				torch.manual_seed(0					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
														# 1. scale model input
														lowerCAmelCase  =				scheduler.scale_model_input(lowerCAmelCase						,						lowerCAmelCase					)

														# 2. predict noise residual
														lowerCAmelCase  =				model(lowerCAmelCase						,						lowerCAmelCase					)

														# 3. predict previous sample x_t-1
														lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						generator=lowerCAmelCase					).prev_sample

														lowerCAmelCase  =				pred_prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(lowerCAmelCase					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(lowerCAmelCase					)					)

										assert abs(result_sum.item() - 347.6357					) < 1e-2
										assert abs(result_mean.item() - 0.4527					) < 1e-3


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[39, 30, 12, 15, 0]

										with self.assertRaises(lowerCAmelCase						,						msg="""`timesteps` must be in descending order."""					):
														scheduler.set_timesteps(timesteps=lowerCAmelCase					)


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[39, 30, 12, 1, 0]
										lowerCAmelCase  =				len(lowerCAmelCase					)

										with self.assertRaises(lowerCAmelCase						,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""					):
														scheduler.set_timesteps(num_inference_steps=lowerCAmelCase						,						timesteps=lowerCAmelCase					)




						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    lowerCAmelCase						,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""						,						):
														scheduler.set_timesteps(timesteps=lowerCAmelCase					)











"""simple docstring"""







from typing import List, Optional

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


a      =							logging.get_logger(__name__)

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






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


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

										# Transformer architecture configuration
										lowerCAmelCase  =				input_size * len(self.lags_sequence					) + self._number_of_features
										lowerCAmelCase  =				d_model
										lowerCAmelCase  =				encoder_attention_heads
										lowerCAmelCase  =				decoder_attention_heads
										lowerCAmelCase  =				encoder_ffn_dim
										lowerCAmelCase  =				decoder_ffn_dim
										lowerCAmelCase  =				encoder_layers
										lowerCAmelCase  =				decoder_layers

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

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




						@property
						def   __lowercase (							self				:			Tuple					):
										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
										)





