

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

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




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= CommonSchedulerState.create(self       )

		# At every step in ddim, we are looking into the previous alphas_cumprod
		# For the final step, there is no previous alphas_cumprod because we are already at 0
		# `set_alpha_to_one` decides whether we set this parameter simply to one or
		# whether we use the final alpha of the "non-previous" one.
		__A   : Tuple								= (
		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
		)

		# standard deviation of the initial noise distribution
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]

		return DDIMSchedulerState.create(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, 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
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

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




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		if state.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		if self.config.prediction_type == "epsilon":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
			    """ `v_prediction`"""       )

		# 4. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps








import math






class     lowerCamelCase_			:




	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
		__A   : Union[str, Any]								= x
		__A   : Union[str, Any]								= y
		__A   : Optional[int]								= step_size
		__A   : Dict								= function_to_optimize




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		return self.function(self.x				,			self.y       )




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Dict								= self.step_size
		return [
		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
		    for x, y in (
		        (self.x - step_size, self.y - step_size),
		        (self.x - step_size, self.y),
		        (self.x - step_size, self.y + step_size),
		        (self.x, self.y - step_size),
		        (self.x, self.y + step_size),
		        (self.x + step_size, self.y - step_size),
		        (self.x + step_size, self.y),
		        (self.x + step_size, self.y + step_size),
		    )
		]




	def __hash__(    self	:    str       ):
		return hash(str(self       )       )




	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
		if isinstance(__A				,			__A       ):
			return hash(str(self       )       ) == hash(str(__A       )       )
		return False




	def __str__(    self	:    List[Any]       ):
		return F"""x: {self.x} y: {self.y}"""






def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
	__A   : int								= search_prob
	__A   : List[str]								= []  # list to store the current score at each iteration
	__A   : Optional[Any]								= 0
	__A   : Any								= False
	__A   : Dict								= set()
	while not solution_found and iterations < max_iter:
		visited.add(a__					)
		iterations += 1
		__A   : Union[str, Any]								= current_state.score()
		scores.append(a__					)
		__A   : List[Any]								= current_state.get_neighbors()
		__A   : Tuple								= -math.inf
		__A   : str								= math.inf
		__A   : Optional[int]								= None  # to hold the next best neighbor
		for neighbor in neighbors:
			if neighbor in visited:
				continue  # do not want to visit the same state again
			if (
			    neighbor.x > max_x
			    or neighbor.x < min_x
			    or neighbor.y > max_y
			    or neighbor.y < min_y
			):
				continue  # neighbor outside our bounds
			__A   : int								= neighbor.score() - current_score
			if find_max:  # finding max
				# going to direction with greatest ascent
				if change > max_change and change > 0:
					__A   : str								= change
					__A   : Optional[int]								= neighbor
			else:  # finding min
				# to direction with greatest descent
				if change < min_change and change < 0:
					__A   : List[Any]								= change
					__A   : List[str]								= neighbor
		if next_state is not None:
			# we found at least one neighbor which improved the current state
			__A   : Tuple								= next_state
		else:
			# since we have no neighbor that improves the solution we stop the search
			__A   : str								= True

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(a__					) ,a__					)
		plt.xlabel("""Iterations"""					)
		plt.ylabel("""Function values"""					)
		plt.show()

	return current_state


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
				return (x**2) + (y**2)

			# starting the problem with initial coordinates (3, 4)
			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)

			# starting the problem with initial coordinates (12, 47)
			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(
			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
			)
			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
				return (3 * x**2) - (6 * y)

			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)







			print(
			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)





