
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


lowercase__          ='scheduler_config.json'



class 		UpperCamelCase__  (	__lowercase   ):
			_SCREAMING_SNAKE_CASE				:							List[str]     	=				1
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				2
			_SCREAMING_SNAKE_CASE				:							Dict     	=				3
			_SCREAMING_SNAKE_CASE				:							int     	=				4
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				5





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
			_SCREAMING_SNAKE_CASE				:							jnp.ndarray





class 		UpperCamelCase__  :
			_SCREAMING_SNAKE_CASE				:							int     	=				SCHEDULER_CONFIG_NAME
			_SCREAMING_SNAKE_CASE				:							int     	=				["dtype"]
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				[]
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				True
			@classmethod
			def 				lowerCAmelCase      (cls			: Union[str, Any] ,    snake_case_			: Dict[str, Any] = None ,    snake_case_			: Optional[str] = None ,    snake_case_			: Any=False ,    **snake_case_			: List[Any] ,    ):
				__a			:  Tuple		    =  cls.load_config(
				    pretrained_model_name_or_path=snake_case_ ,    subfolder=snake_case_ ,    return_unused_kwargs=snake_case_ ,    **snake_case_ ,    )
				__a			:  Dict		    =  cls.from_config(snake_case_ ,    return_unused_kwargs=snake_case_ ,    **snake_case_       )

				if hasattr(snake_case_ ,    '''create_state'''       ) and getattr(snake_case_ ,    '''has_state''' ,    snake_case_       ):
					__a			:  str		    =  scheduler.create_state()

				if return_unused_kwargs:
					return scheduler, state, unused_kwargs

				return scheduler, state
			def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Union[str, os.PathLike] ,    snake_case_			: bool = False ,    **snake_case_			: Optional[int]       ):
				self.save_config(save_directory=snake_case_ ,    push_to_hub=snake_case_ ,    **snake_case_       )
			@property
			def 				lowerCAmelCase      (self			: List[str]       ):
				return self._get_compatibles()







			@classmethod
			def 				lowerCAmelCase      (cls			: int       ):
				__a			:  Tuple		    =  list(set([cls.__name__] + cls._compatibles       )       )
				__a			:  Union[str, Any]		    =  importlib.import_module(__name__.split('''.'''       )[0]       )
				__a			:  List[Any]		    =  [
				    getattr(snake_case_ ,    snake_case_       ) for c in compatible_classes_str if hasattr(snake_case_ ,    snake_case_       )
				]
				return compatible_classes





def        __UpperCamelCase					(    lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    Tuple[int]  ):
	assert len(lowerCAmelCase__  ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__  ) - x.ndim)  )  ,       lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int=0.9_99  ,       lowerCAmelCase__							:    Any=jnp.floataa  ):

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

	__a			:  Optional[Any]		    =  []
	for i in range(lowerCAmelCase__  ):
		__a			:  List[str]		    =  i / num_diffusion_timesteps
		__a			:  List[Any]		    =  (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__  :
			_SCREAMING_SNAKE_CASE				:							jnp.ndarray
			_SCREAMING_SNAKE_CASE				:							jnp.ndarray
			_SCREAMING_SNAKE_CASE				:							jnp.ndarray
			@classmethod
			def 				lowerCAmelCase      (cls			: Union[str, Any] ,    snake_case_			: str       ):
				__a			:  int		    =  scheduler.config

				if config.trained_betas is not None:
					__a			:  Tuple		    =  jnp.asarray(config.trained_betas ,    dtype=scheduler.dtype       )
				elif config.beta_schedule == "linear":
					__a			:  int		    =  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			:  Union[str, 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			:  Any		    =  jnp.cumprod(snake_case_ ,    axis=0       )

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





def        __UpperCamelCase					(    lowerCAmelCase__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
	__a			:  Optional[Any]		    =  state.alphas_cumprod

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

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

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def        __UpperCamelCase					(    lowerCAmelCase__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
	__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__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
	__a			:  Union[str, Any]		    =  get_sqrt_alpha_prod(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
	__a			:  List[Any]		    =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
