

'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class       SCREAMING_SNAKE_CASE       :






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					CommonSchedulerState

      # setable values
      SCREAMING_SNAKE_CASE__			:					jnp.ndarray
      SCREAMING_SNAKE_CASE__			:					jnp.ndarray
      SCREAMING_SNAKE_CASE__			:					Optional[int]     =					None




      @classmethod
      def 						__UpperCAmelCase      (       cls			:		Optional[int]  ,       snake_case			:		Dict  ,       snake_case			:		Dict  ,       snake_case			:		Optional[Any] ):



          """simple docstring"""





          return cls(common=lowerCAmelCase_  ,       init_noise_sigma=lowerCAmelCase_  ,       timesteps=lowerCAmelCase_ )



@dataclass
class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					DDPMSchedulerState



class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					List[Any]     =					[e.name for e in FlaxKarrasDiffusionSchedulers]

      SCREAMING_SNAKE_CASE__			:					jnp.dtype




      @property
      def 						__UpperCAmelCase      (       self			:		Union[str, Any] ):



          """simple docstring"""





          return True




      @register_to_config
      def __init__(       self			:		int  ,       snake_case			:		Dict = 1000  ,       snake_case			:		str = 0.0001  ,       snake_case			:		Optional[Any] = 0.02  ,       snake_case			:		List[Any] = "linear"  ,       snake_case			:		List[str] = None  ,       snake_case			:		Tuple = "fixed_small"  ,       snake_case			:		Dict = True  ,       snake_case			:		List[Any] = "epsilon"  ,       snake_case			:		Union[str, Any] = jnp.floataa  ,       ):



          """simple docstring"""





          _snake_case			:			int				       =		dtype




      def 						__UpperCAmelCase      (       self			:		List[str]  ,       snake_case			:		Optional[int] = None ):



          """simple docstring"""





          if common is None:
              _snake_case			:			List[str]				       =		CommonSchedulerState.create(self )

          # standard deviation of the initial noise distribution
          _snake_case			:			Optional[int]				       =		jnp.array(1.0  ,       dtype=self.dtype )

          _snake_case			:			Optional[int]				       =		jnp.arange(0  ,       self.config.num_train_timesteps ).round()[::-1]

          return DDPMSchedulerState.create(
              common=lowerCAmelCase_  ,       init_noise_sigma=lowerCAmelCase_  ,       timesteps=lowerCAmelCase_  ,       )




      def 						__UpperCAmelCase      (       self			:		str  ,       snake_case			:		str  ,       snake_case			:		str  ,       snake_case			:		List[Any] = None ):



          """simple docstring"""





          return sample




      def 						__UpperCAmelCase      (       self			:		Any  ,       snake_case			:		Tuple  ,       snake_case			:		Union[str, Any]  ,       snake_case			:		int = () ):



          """simple docstring"""





          _snake_case			:			Any				       =		self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _snake_case			:			Union[str, Any]				       =		(jnp.arange(0  ,       lowerCAmelCase_ ) * step_ratio).round()[::-1]

          return state.replace(
              num_inference_steps=lowerCAmelCase_  ,       timesteps=lowerCAmelCase_  ,       )




      def 						__UpperCAmelCase      (       self			:		Any  ,       snake_case			:		int  ,       snake_case			:		List[Any]  ,       snake_case			:		Optional[Any]=None  ,       snake_case			:		Optional[int]=None ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		state.common.alphas_cumprod[t]
          _snake_case			:			Any				       =		jnp.where(t > 0  ,       state.common.alphas_cumprod[t - 1]  ,       jnp.array(1.0  ,       dtype=self.dtype ) )

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          _snake_case			:			Tuple				       =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
              _snake_case			:			Any				       =		self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
              _snake_case			:			Optional[Any]				       =		jnp.clip(lowerCAmelCase_  ,       a_min=1e-20 )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
              _snake_case			:			Tuple				       =		jnp.log(jnp.clip(lowerCAmelCase_  ,       a_min=1e-20 ) )
          elif variance_type == "fixed_large":
              _snake_case			:			Dict				       =		state.common.betas[t]
          elif variance_type == "fixed_large_log":
              # Glide max_log
              _snake_case			:			List[Any]				       =		jnp.log(state.common.betas[t] )
          elif variance_type == "learned":
              return predicted_variance
          elif variance_type == "learned_range":
              _snake_case			:			List[Any]				       =		variance
              _snake_case			:			Union[str, Any]				       =		state.common.betas[t]
              _snake_case			:			Dict				       =		(predicted_variance + 1) / 2
              _snake_case			:			Union[str, Any]				       =		frac * max_log + (1 - frac) * min_log

          return variance




      def 						__UpperCAmelCase      (       self			:		str  ,       snake_case			:		Dict  ,       snake_case			:		str  ,       snake_case			:		int  ,       snake_case			:		Optional[Any]  ,       snake_case			:		Optional[int] = None  ,       snake_case			:		Union[str, Any] = True  ,       ):



          """simple docstring"""





          _snake_case			:			Union[str, Any]				       =		timestep

          if key is None:
              _snake_case			:			int				       =		jax.random.PRNGKey(0 )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
              _snake_case			:			List[Any]				       =		jnp.split(lowerCAmelCase_  ,       sample.shape[1]  ,       axis=1 )
          else:
              _snake_case			:			Any				       =		None

          # 1. compute alphas, betas
          _snake_case			:			List[str]				       =		state.common.alphas_cumprod[t]
          _snake_case			:			Optional[int]				       =		jnp.where(t > 0  ,       state.common.alphas_cumprod[t - 1]  ,       jnp.array(1.0  ,       dtype=self.dtype ) )
          _snake_case			:			List[Any]				       =		1 - alpha_prod_t
          _snake_case			:			Any				       =		1 - alpha_prod_t_prev

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
              _snake_case			:			str				       =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
              _snake_case			:			Dict				       =		model_output
          elif self.config.prediction_type == "v_prediction":
              _snake_case			:			Optional[int]				       =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          else:
              raise ValueError(
                  F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
                  ' for the FlaxDDPMScheduler.' )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
              _snake_case			:			Optional[Any]				       =		jnp.clip(lowerCAmelCase_  ,       -1  ,       1 )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          _snake_case			:			int				       =		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          _snake_case			:			Dict				       =		state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          _snake_case			:			Any				       =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
              _snake_case			:			int				       =		jax.random.split(lowerCAmelCase_  ,       num=1 )
              _snake_case			:			int				       =		jax.random.normal(lowerCAmelCase_  ,       shape=model_output.shape  ,       dtype=self.dtype )
              return (self._get_variance(lowerCAmelCase_  ,       lowerCAmelCase_  ,       predicted_variance=lowerCAmelCase_ ) ** 0.5) * noise

          _snake_case			:			Dict				       =		jnp.where(t > 0  ,       random_variance()  ,       jnp.zeros(model_output.shape  ,       dtype=self.dtype ) )

          _snake_case			:			Tuple				       =		pred_prev_sample + variance

          if not return_dict:
              return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_  ,       state=lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		List[str]  ,       snake_case			:		Union[str, Any]  ,       snake_case			:		str  ,       snake_case			:		str  ,       snake_case			:		Dict  ,       ):



          """simple docstring"""





          return add_noise_common(state.common  ,       lowerCAmelCase_  ,       lowerCAmelCase_  ,       lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		Optional[Any]  ,       snake_case			:		Tuple  ,       snake_case			:		Optional[Any]  ,       snake_case			:		int  ,       snake_case			:		Optional[Any]  ,       ):



          """simple docstring"""





          return get_velocity_common(state.common  ,       lowerCAmelCase_  ,       lowerCAmelCase_  ,       lowerCAmelCase_ )




      def __len__(       self			:		Union[str, Any] ):



          """simple docstring"""





          return self.config.num_train_timesteps











"""simple docstring"""
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None				):
		A_		:    Dict						     = data
		A_		:    Dict						     = previous
		A_		:    int						     = next_node





	def __str__(self				):
		return f"""{self.data}"""





	def  lowerCamelCase(self				):
		return self.data





	def  lowerCamelCase(self				):
		return self.next





	def  lowerCamelCase(self				):
		return self.previous






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = head





	def __iter__(self				):
		return self





	def  lowerCamelCase(self				):
		if not self.current:
			raise StopIteration
		else:
			A_		:    str						     = self.current.get_data()
			A_		:    List[str]						     = self.current.get_next()
			return value






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self				):
		A_		:    str						     = None  # First node in list
		A_		:    Tuple						     = None  # Last node in list





	def __str__(self				):
		A_		:    int						     = self.head
		A_		:    str						     = []
		while current is not None:
			nodes.append(current.get_data()				)
			A_		:    Any						     = current.get_next()
		return " ".join(str(lowerCAmelCase_				) for node in nodes				)





	def __contains__(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.head
		while current:
			if current.get_data() == value:
				return True
			A_		:    Union[str, Any]						     = current.get_next()
		return False





	def __iter__(self				):
		return LinkedListIterator(self.head				)





	def  lowerCamelCase(self				):
		if self.head:
			return self.head.get_data()
		return None





	def  lowerCamelCase(self				):
		if self.tail:
			return self.tail.get_data()
		return None





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			A_		:    str						     = node
			A_		:    Optional[int]						     = node
		else:
			self.insert_before_node(self.head  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = Node(lowerCAmelCase_				)
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.set_tail(lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = node
		A_		:    Union[str, Any]						     = node.previous

		if node.get_previous() is None:
			A_		:    Dict						     = node_to_insert
		else:
			A_		:    Optional[Any]						     = node_to_insert

		A_		:    Any						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = node
		A_		:    Optional[Any]						     = node.next

		if node.get_next() is None:
			A_		:    Optional[int]						     = node_to_insert
		else:
			A_		:    List[Any]						     = node_to_insert

		A_		:    Optional[Any]						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = 1
		A_		:    Tuple						     = Node(lowerCAmelCase_				)
		A_		:    Dict						     = self.head
		while node:
			if current_position == position:
				self.insert_before_node(lowerCAmelCase_  ,  lowerCAmelCase_				)
				return
			current_position += 1
			A_		:    List[str]						     = node.next
		self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Dict						     = self.head
		while node:
			if node.get_data() == item:
				return node
			A_		:    Optional[int]						     = node.get_next()
		raise Exception("""Node not found"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if (node := self.get_node(lowerCAmelCase_				)) is not None:
			if node == self.head:
				A_		:    Tuple						     = self.head.get_next()

			if node == self.tail:
				A_		:    Dict						     = self.tail.get_previous()

			self.remove_node_pointers(lowerCAmelCase_				)





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		if node.get_next():
			A_		:    Any						     = node.previous

		if node.get_previous():
			A_		:    int						     = node.next

		A_		:    str						     = None
		A_		:    int						     = None





	def  lowerCamelCase(self				):
		return self.head is None






def       __UpperCamelCase						(		):
	pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()

