"""simple docstring"""





import math

import flax.linen as nn
import jax.numpy as jnp





def 				a_     (      _lowerCAmelCase  : jnp.ndarray   , _lowerCAmelCase  : int   , _lowerCAmelCase  : float = 1   , _lowerCAmelCase  : float = 1   , _lowerCAmelCase  : float = 1.0E4   , _lowerCAmelCase  : bool = False   , _lowerCAmelCase  : float = 1.0   , ):





       '''simple docstring'''

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
       lowercase__			:		Optional[Any]       =	float(embedding_dim // 2     )
       lowercase__			:		Optional[Any]       =	math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
       lowercase__			:		Any       =	min_timescale * jnp.exp(jnp.arange(_lowerCAmelCase   , dtype=jnp.floataa     ) * -log_timescale_increment     )
       lowercase__			:		Dict       =	jnp.expand_dims(_lowerCAmelCase   , 1     ) * jnp.expand_dims(_lowerCAmelCase   , 0     )

       # scale embeddings
       lowercase__			:		List[str]       =	scale * emb

       if flip_sin_to_cos:
              lowercase__			:		Dict       =	jnp.concatenate([jnp.cos(_lowerCAmelCase     ), jnp.sin(_lowerCAmelCase     )]   , axis=1     )
       else:
              lowercase__			:		Optional[int]       =	jnp.concatenate([jnp.sin(_lowerCAmelCase     ), jnp.cos(_lowerCAmelCase     )]   , axis=1     )
       lowercase__			:		List[Any]       =	jnp.reshape(_lowerCAmelCase   , [jnp.shape(_lowerCAmelCase     )[0], embedding_dim]     )
       return signal


class      UpperCAmelCase_	(    nn.Module):
      lowerCamelCase__		:    int   =  3_2
      lowerCamelCase__		:    jnp.dtype   =  jnp.floataa


      @nn.compact
      def __call__(   self		,			a							)       ->					Any:
             lowercase__			:		str       =	nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name='linear_1'							)(a							)
             lowercase__			:		Union[str, Any]       =	nn.silu(a							)
             lowercase__			:		Optional[Any]       =	nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name='linear_2'							)(a							)
             return temb





class      UpperCAmelCase_	(    nn.Module):
      lowerCamelCase__		:    int   =  3_2
      lowerCamelCase__		:    bool   =  False
      lowerCamelCase__		:    float   =  1


      @nn.compact
      def __call__(   self		,			a							)       ->					str:
             return get_sinusoidal_embeddings(
                 a		,			embedding_dim=self.dim		,			flip_sin_to_cos=self.flip_sin_to_cos		,			freq_shift=self.freq_shift							)


"""simple docstring"""




from typing import List, Optional, Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						'time_series_transformer'
						lowerCAmelCase__     :  Dict  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "student_t"						,__UpperCAmelCase = "nll"						,__UpperCAmelCase = 1						,__UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]						,__UpperCAmelCase = "mean"						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 32						,__UpperCAmelCase = 32						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = True						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 64						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> List[str]:
													# time series specific configuration
													A__		   =							prediction_length
													A__		   =							context_length or prediction_length
													A__		   =							distribution_output
													A__		   =							loss
													A__		   =							input_size
													A__		   =							num_time_features
													A__		   =							lags_sequence
													A__		   =							scaling
													A__		   =							num_dynamic_real_features
													A__		   =							num_static_real_features
													A__		   =							num_static_categorical_features
													if cardinality and num_static_categorical_features > 0:
																				if len(__UpperCAmelCase   ) != num_static_categorical_features:
																											raise ValueError(
																											    'The cardinality should be a list of the same length as `num_static_categorical_features`'   )
																				A__		   =							cardinality
													else:
																				A__		   =							[0]
													if embedding_dimension and num_static_categorical_features > 0:
																				if len(__UpperCAmelCase   ) != num_static_categorical_features:
																											raise ValueError(
																											    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'   )
																				A__		   =							embedding_dimension
													else:
																				A__		   =							[min(50						,(cat + 1) // 2   ) for cat in self.cardinality]
													A__		   =							num_parallel_samples

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

													A__		   =							dropout
													A__		   =							attention_dropout
													A__		   =							activation_dropout
													A__		   =							encoder_layerdrop
													A__		   =							decoder_layerdrop

													A__		   =							activation_function
													A__		   =							init_std

													A__		   =							use_cache

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





						@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
													)





