



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 	lowerCamelCase_    ( __SCREAMING_SNAKE_CASE						):






						'''simple docstring'''
						a__   =  1
						a__   =  2
						a__   =  3
						a__   =  4
						a__   =  5







@dataclass
class 	lowerCamelCase_    ( __SCREAMING_SNAKE_CASE						):






						'''simple docstring'''
						a__   =  42







class 	lowerCamelCase_    :






						'''simple docstring'''
						a__   =  SCHEDULER_CONFIG_NAME
						a__   =  ['dtype']
						a__   =  []
						a__   =  True







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			str = None	,	__lowerCamelCase      :			Optional[int]=False	,	**__lowerCamelCase      :			List[Any]	,	)      ->				Tuple:
									A       , A							:					Optional[int]              =						cls.load_config(
									    pretrained_model_name_or_path=_a	,	subfolder=_a	,	return_unused_kwargs=_a	,	**_a	,	)
									A       , A							:					List[str]              =						cls.from_config(_a	,	return_unused_kwargs=_a	,	**_a )

									if hasattr(_a	,	"create_state" ) and getattr(_a	,	"has_state"	,	_a ):
												A							:					Dict              =						scheduler.create_state()

									if return_unused_kwargs:
												return scheduler, state, unused_kwargs

									return scheduler, state







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Optional[Any] = False	,	**__lowerCamelCase      :			Dict )      ->				Any:
									self.save_config(save_directory=_a	,	push_to_hub=_a	,	**_a )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				List[Any]:
									return self._get_compatibles()







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Tuple )      ->				Tuple:
									A							:					Optional[Any]              =						list(set([cls.__name__] + cls._compatibles ) )
									A							:					int              =						importlib.import_module(__name__.split("." )[0] )
									A							:					Union[str, Any]              =						[
									    getattr(_a	,	_a ) for c in compatible_classes_str if hasattr(_a	,	_a )
									]
									return compatible_classes







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			assert len(lowerCAmelCase__    ) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__    ) - x.ndim)    ) ,    lowerCAmelCase__    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=0.9_99 ,    _lowerCamelCase=jnp.floataa    ):

			def alpha_bar(_lowerCamelCase    ):
						return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

			A							:					Any              =						[]
			for i in range(lowerCAmelCase__    ):
						A							:					Optional[Any]              =						i / num_diffusion_timesteps
						A							:					Optional[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 	lowerCamelCase_    :






						'''simple docstring'''
						a__   =  42
						a__   =  42
						a__   =  42







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			List[str]	,	__lowerCamelCase      :			Optional[int] )      ->				str:
									A							:					Union[str, Any]              =						scheduler.config

									if config.trained_betas is not None:
												A							:					Dict              =						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							:					Tuple              =						(
												    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							:					Dict              =						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							:					str              =						1.0 - betas

									A							:					int              =						jnp.cumprod(_a	,	axis=0 )

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







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[Any]              =						state.alphas_cumprod

			A							:					Any              =						alphas_cumprod[timesteps] ** 0.5
			A							:					str              =						sqrt_alpha_prod.flatten()
			A							:					Any              =						broadcast_to_shape_from_left(lowerCAmelCase__ ,    original_samples.shape    )

			A							:					Dict              =						(1 - alphas_cumprod[timesteps]) ** 0.5
			A							:					Any              =						sqrt_one_minus_alpha_prod.flatten()
			A							:					Tuple              =						broadcast_to_shape_from_left(lowerCAmelCase__ ,    original_samples.shape    )

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A       , A							:					str              =						get_sqrt_alpha_prod(lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__    )
			A							:					str              =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A       , A							:					str              =						get_sqrt_alpha_prod(lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__    )
			A							:					Dict              =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()