

from typing import List, Optional

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

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

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"autoformer"
						_lowercase    						=			{
						    "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_						,					):







										'''simple docstring'''


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

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

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

										_UpperCAmelCase       :		Optional[int] 		=    activation_function
										_UpperCAmelCase       :		Union[str, Any] 		=    init_std

										_UpperCAmelCase       :		int 		=    use_cache

										# Autoformer
										_UpperCAmelCase       :		List[Any] 		=    label_length
										_UpperCAmelCase       :		str 		=    moving_average
										_UpperCAmelCase       :		str 		=    autocorrelation_factor

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





						@property
						def        _UpperCAmelCase				(       self	):







										'''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
										)





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


