





"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




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




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




				def 					__lowercase  (     self  :Any			):
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

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

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




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

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

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

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

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

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :str = "student_t"						,    __lowercase  :str = "nll"						,    __lowercase  :int = 1						,    __lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]						,    __lowercase  :Optional[Union[str, bool]] = "mean"						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :int = 32						,    __lowercase  :int = 32						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :bool = True						,    __lowercase  :str = "gelu"						,    __lowercase  :int = 64						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :int = 100						,    __lowercase  :float = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[Any]         =num_static_categorical_features
											if cardinality and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      str         =[0]
											if embedding_dimension and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

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

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

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

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




				@property
				def 					__lowercase  (     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
											)




