




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  A		(  snake_case_     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5







@dataclass
class  A		(  snake_case_     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   ['dtype']
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__,	)
								lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )

								if hasattr(UpperCamelCase__,	'''create_state'''   ) and getattr(UpperCamelCase__,	'''has_state''',	UpperCamelCase__   ):
														lowerCAmelCase_      			=  scheduler.create_state()

								if return_unused_kwargs:
														return scheduler, state, unused_kwargs

								return scheduler, state



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



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







def __UpperCamelCase			(       _A ,  _A  ):
						assert len(a_  ) >= x.ndim
						return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a_  ) - x.ndim)  ) ,  a_  )





def __UpperCamelCase			(       _A ,  _A=0.9_9_9 ,  _A=jnp.floataa  ):

						def alpha_bar(_A  ):
												return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

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


@flax.struct.dataclass
class  A		:
		__snake_case									=   42
		__snake_case									=   42
		__snake_case									=   42



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  scheduler.config

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

								lowerCAmelCase_      			=  1.0 - betas

								lowerCAmelCase_      			=  jnp.cumprod(UpperCamelCase__,	axis=0   )

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







def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  state.alphas_cumprod

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

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

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  get_sqrt_alpha_prod(a_ ,  a_ ,  a_ ,  a_  )
						lowerCAmelCase_      			=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples






def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  get_sqrt_alpha_prod(a_ ,  a_ ,  a_ ,  a_  )
						lowerCAmelCase_      			=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity












lowercase  							=					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase  							=					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase  							=					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





