

"""simple docstring"""


from typing import List, Optional

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


a					:Dict    =       logging.get_logger(__name__)

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

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """autoformer"""
     _SCREAMING_SNAKE_CASE						:Tuple       = {
         """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	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	prediction_length
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	context_length if context_length is not None else prediction_length
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	distribution_output
           SCREAMING_SNAKE_CASE__   :      str      		=	loss
           SCREAMING_SNAKE_CASE__   :      str      		=	input_size
           SCREAMING_SNAKE_CASE__   :      str      		=	num_time_features
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	lags_sequence
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scaling
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_dynamic_real_features
           SCREAMING_SNAKE_CASE__   :      int      		=	num_static_real_features
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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__   :      Dict      		=	[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__   :      Union[str, Any]      		=	embedding_dimension
           else:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	[min(50	,    (cat + 1) // 2  ) for cat in self.cardinality]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	num_parallel_samples

           # Transformer architecture configuration
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	input_size * len(self.lags_sequence  ) + self._number_of_features
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	d_model
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	encoder_attention_heads
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decoder_attention_heads
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	encoder_ffn_dim
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	decoder_ffn_dim
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	encoder_layers
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	decoder_layers

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	activation_function
           SCREAMING_SNAKE_CASE__   :      Dict      		=	init_std

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_cache

           # Autoformer
           SCREAMING_SNAKE_CASE__   :      Dict      		=	label_length
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	moving_average
           SCREAMING_SNAKE_CASE__   :      Any      		=	autocorrelation_factor

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





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           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
           )







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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


           """simple docstring"""


           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a  )

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


           """simple docstring"""


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      str      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler.timesteps

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[100, 87, 50, 51, 0]

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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

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





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

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




