





"""simple docstring"""






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


__UpperCAmelCase						=			'''scheduler_config.json'''



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				1
				__lowerCamelCase				:	str				     =				2
				__lowerCamelCase				:	List[str]				     =				3
				__lowerCamelCase				:	Union[str, Any]				     =				4
				__lowerCamelCase				:	str				     =				5



@dataclass
class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	jnp.ndarray



class 					__UpperCAmelCase							:
				__lowerCamelCase				:	List[Any]				     =				SCHEDULER_CONFIG_NAME
				__lowerCamelCase				:	Union[str, Any]				     =				["dtype"]
				__lowerCamelCase				:	Any				     =				[]
				__lowerCamelCase				:	List[Any]				     =				True
				@classmethod
				def       UpperCAmelCase      (		cls    :			str	, a_    :			Dict[str, Any] = None	, a_    :			Optional[str] = None	, a_    :			Dict=False	, **a_    :			str	, )	->	Any:



							'''simple docstring'''



							a__ ,	a__  :	Tuple		 =			cls.load_config(
							    pretrained_model_name_or_path=a_	, subfolder=a_	, return_unused_kwargs=a_	, **a_	, )
							a__ ,	a__  :	int		 =			cls.from_config(a_	, return_unused_kwargs=a_	, **a_    )

							if hasattr(a_	, "create_state"    ) and getattr(a_	, "has_state"	, a_    ):
										a__  :	Any		 =			scheduler.create_state()

							if return_unused_kwargs:
										return scheduler, state, unused_kwargs

							return scheduler, state
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Union[str, os.PathLike]	, a_    :			bool = False	, **a_    :			int    )	->	List[str]:



							'''simple docstring'''



							self.save_config(save_directory=a_	, push_to_hub=a_	, **a_    )
				@property
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Any:



							'''simple docstring'''



							return self._get_compatibles()






				@classmethod
				def       UpperCAmelCase      (		cls    :			List[Any]    )	->	Optional[Any]:



							'''simple docstring'''



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






def      lowercase__ (					lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			Tuple[int]			)    ->			jnp.ndarray:



			'''simple docstring'''
			assert len(lowerCAmelCase__			) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__			) - x.ndim)			)							,			lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			str=0.999							,			lowerCAmelCase__   :			int=jnp.floataa			)    ->			jnp.ndarray:



			'''simple docstring'''

			def alpha_bar(lowerCAmelCase__   :			str			):
						return math.cos((time_step + 0.008) / 1.008 * math.pi / 2			) ** 2

			a__  :	int		 =			[]
			for i in range(lowerCAmelCase__			):
						a__  :	List[Any]		 =			i / num_diffusion_timesteps
						a__  :	int		 =			(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 					__UpperCAmelCase							:
				__lowerCamelCase				:	jnp.ndarray
				__lowerCamelCase				:	jnp.ndarray
				__lowerCamelCase				:	jnp.ndarray
				@classmethod
				def       UpperCAmelCase      (		cls    :			List[Any]	, a_    :			Union[str, Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			scheduler.config

							if config.trained_betas is not None:
										a__  :	Union[str, Any]		 =			jnp.asarray(config.trained_betas	, dtype=scheduler.dtype    )
							elif config.beta_schedule == "linear":
										a__  :	Dict		 =			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.
										a__  :	Any		 =			(
										    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
										a__  :	int		 =			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__}"    )

							a__  :	List[str]		 =			1.0 - betas

							a__  :	int		 =			jnp.cumprod(a_	, axis=0    )

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






def      lowercase__ (					lowerCAmelCase__   :			CommonSchedulerState							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray			)    ->			str:



			'''simple docstring'''
			a__  :	int		 =			state.alphas_cumprod

			a__  :	Dict		 =			alphas_cumprod[timesteps] ** 0.5
			a__  :	Optional[int]		 =			sqrt_alpha_prod.flatten()
			a__  :	List[Any]		 =			broadcast_to_shape_from_left(lowerCAmelCase__							,			original_samples.shape			)

			a__  :	List[Any]		 =			(1 - alphas_cumprod[timesteps]) ** 0.5
			a__  :	Any		 =			sqrt_one_minus_alpha_prod.flatten()
			a__  :	List[str]		 =			broadcast_to_shape_from_left(lowerCAmelCase__							,			original_samples.shape			)

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def      lowercase__ (					lowerCAmelCase__   :			CommonSchedulerState							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray			)    ->			str:



			'''simple docstring'''
			a__ ,	a__  :	int		 =			get_sqrt_alpha_prod(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Dict		 =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples






def      lowercase__ (					lowerCAmelCase__   :			CommonSchedulerState							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray							,			lowerCAmelCase__   :			jnp.ndarray			)    ->			Dict:



			'''simple docstring'''
			a__ ,	a__  :	Optional[int]		 =			get_sqrt_alpha_prod(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Any		 =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity






"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)