


"""simple docstring"""


from typing import List, Optional, Union

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


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality 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`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension 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`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

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

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

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







     @property
     def 		a__						(	self:						int	)->    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
           )








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(KDPMaDiscreteScheduler,)
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**__a	)
           return config

     def 		a__						(	self:						Union[str, Any]	)->    Any:
           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=__a	)

     def 		a__						(	self:						str	)->    int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						int	)->    Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					List[Any]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Optional[Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					Optional[Any]        =	output.prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 0.00_02	) < 1e-3

     def 		a__						(	self:						Any	)->    Any:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					int        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					List[Any]        =	self.dummy_model()
           lowerCamelCase					:					Optional[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					Optional[int]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Dict        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[Any]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	output.prev_sample

           lowerCamelCase					:					Tuple        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3







     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps     ,		device=__a	)

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase					:					Union[str, Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					int        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					int        =	output.prev_sample

           lowerCamelCase					:					Union[str, Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					int        =	torch.mean(torch.abs(__a	)	)

           if str(__a	).startswith("""cpu"""	):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3




