




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


_lowerCamelCase      :						Union[str, Any]							=  """scheduler_config.json"""


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = 1
    UpperCAmelCase__				    = 2
    UpperCAmelCase__				    = 3
    UpperCAmelCase__				    = 4
    UpperCAmelCase__				    = 5


@dataclass
class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = 42


class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = SCHEDULER_CONFIG_NAME
    UpperCAmelCase__				    = ['''dtype''']
    UpperCAmelCase__				    = []
    UpperCAmelCase__				    = True


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      List[Any]   ,					UpperCAmelCase__ :      Dict[str, Any] = None   ,					UpperCAmelCase__ :      Optional[str] = None   ,					UpperCAmelCase__ :      int=False   ,					**UpperCAmelCase__ :      Union[str, Any]   ,					)		->Union[str, Any]:





           '''simple docstring'''

           A__					,				A__ 					=       cls.load_config(
               pretrained_model_name_or_path=UpperCAmelCase__   ,					subfolder=UpperCAmelCase__   ,					return_unused_kwargs=UpperCAmelCase__   ,					**UpperCAmelCase__   ,					)
           A__					,				A__ 					=       cls.from_config(UpperCAmelCase__   ,					return_unused_kwargs=UpperCAmelCase__   ,					**UpperCAmelCase__)

           if hasattr(UpperCAmelCase__   ,					'''create_state''') and getattr(UpperCAmelCase__   ,					'''has_state'''   ,					UpperCAmelCase__):
                  A__ 					=       scheduler.create_state()

           if return_unused_kwargs:
                  return scheduler, state, unused_kwargs

           return scheduler, state


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any]   ,					UpperCAmelCase__ :      Union[str, os.PathLike]   ,					UpperCAmelCase__ :      bool = False   ,					**UpperCAmelCase__ :      Optional[Any])		->List[Any]:





           '''simple docstring'''

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


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Dict:





           '''simple docstring'''

           return self._get_compatibles()





    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      int)		->Dict:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			jnp.ndarray:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=0.9_99					,							lowercase_=jnp.floataa		)  ->			jnp.ndarray:



       """simple docstring"""

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

       A__ 					=       []
       for i in range(lowercase_		):
              A__ 					=       i / num_diffusion_timesteps
              A__ 					=       (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar(lowercase_		) / alpha_bar(lowercase_		)					,							lowercase_		)		)
       return jnp.array(lowercase_					,							dtype=lowercase_		)


@flax.struct.dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = 42
    UpperCAmelCase__				    = 42
    UpperCAmelCase__				    = 42


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Union[str, Any]   ,					UpperCAmelCase__ :      List[str])		->Any:





           '''simple docstring'''

           A__ 					=       scheduler.config

           if config.trained_betas is not None:
                  A__ 					=       jnp.asarray(config.trained_betas   ,					dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
                  A__ 					=       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__ 					=       (
                      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__ 					=       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__ 					=       1.0 - betas

           A__ 					=       jnp.cumprod(UpperCAmelCase__   ,					axis=0)

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       A__ 					=       state.alphas_cumprod

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

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

       return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       A__					,				A__ 					=       get_sqrt_alpha_prod(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)
       A__ 					=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       A__					,				A__ 					=       get_sqrt_alpha_prod(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)
       A__ 					=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
       return velocity








from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")