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


A_      =      "scheduler_config.json"






class      __lowercase					(    lowerCAmelCase__    ):
						lowercase					 =     1
						lowercase					 =     2
						lowercase					 =     3
						lowercase					 =     4
						lowercase					 =     5






@dataclass
class      __lowercase					(    lowerCAmelCase__    ):
						lowercase					 =     42






class      __lowercase					:
						lowercase					 =     SCHEDULER_CONFIG_NAME
						lowercase					 =     ["dtype"]
						lowercase					 =     []
						lowercase					 =     True

						@classmethod
						def        __a		(	cls				:      List[Any]							,							__lowerCamelCase				:      List[Any] = None							,							__lowerCamelCase				:      Dict = None							,							__lowerCamelCase				:      Any=False							,							**__lowerCamelCase				:      str							,							)							->		Tuple:


									'''simple docstring'''

									lowercase		,lowercase							=		cls.load_config(
									    pretrained_model_name_or_path=_lowerCamelCase							,							subfolder=_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							**_lowerCamelCase							,							)
									lowercase		,lowercase							=		cls.from_config(_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							**_lowerCamelCase   )

									if hasattr(_lowerCamelCase							,							'''create_state'''   ) and getattr(_lowerCamelCase							,							'''has_state'''							,							_lowerCamelCase   ):
												lowercase							=		scheduler.create_state()

									if return_unused_kwargs:
												return scheduler, state, unused_kwargs

									return scheduler, state

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int = False							,							**__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

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

						@property
						def        __a		(	self				:      List[Any]   )							->		List[Any]:


									'''simple docstring'''

									return self._get_compatibles()



						@classmethod
						def        __a		(	cls				:      List[Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		list(set([cls.__name__] + cls._compatibles   )   )
									lowercase							=		importlib.import_module(__name__.split('''.'''   )[0]   )
									lowercase							=		[
									    getattr(_lowerCamelCase							,							_lowerCamelCase   ) for c in compatible_classes_str if hasattr(_lowerCamelCase							,							_lowerCamelCase   )
									]
									return compatible_classes






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			assert len(lowerCamelCase_       ) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_       ) - x.ndim)       ),		lowerCamelCase_       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=0.999,		UpperCAmelCase=jnp.floataa       )->       str:



			"""simple docstring"""







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

			lowercase							=		[]
			for i in range(lowerCamelCase_       ):
						lowercase							=		i / num_diffusion_timesteps
						lowercase							=		(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      __lowercase					:
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42

						@classmethod
						def        __a		(	cls				:      Optional[int]							,							__lowerCamelCase				:      List[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		scheduler.config

									if config.trained_betas is not None:
												lowercase							=		jnp.asarray(config.trained_betas							,							dtype=scheduler.dtype   )
									elif config.beta_schedule == "linear":
												lowercase							=		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.
												lowercase							=		(
												    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
												lowercase							=		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__}'   )

									lowercase							=		1.0 - betas

									lowercase							=		jnp.cumprod(_lowerCamelCase							,							axis=0   )

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		state.alphas_cumprod

			lowercase							=		alphas_cumprod[timesteps] ** 0.5
			lowercase							=		sqrt_alpha_prod.flatten()
			lowercase							=		broadcast_to_shape_from_left(lowerCamelCase_,		original_samples.shape       )

			lowercase							=		(1 - alphas_cumprod[timesteps]) ** 0.5
			lowercase							=		sqrt_one_minus_alpha_prod.flatten()
			lowercase							=		broadcast_to_shape_from_left(lowerCamelCase_,		original_samples.shape       )

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase		,lowercase							=		get_sqrt_alpha_prod(lowerCamelCase_,		lowerCamelCase_,		lowerCamelCase_,		lowerCamelCase_       )
			lowercase							=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase		,lowercase							=		get_sqrt_alpha_prod(lowerCamelCase_,		lowerCamelCase_,		lowerCamelCase_,		lowerCamelCase_       )
			lowercase							=		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





