
'''simple docstring'''
# 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      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		42
		lowerCamelCase							=		42

		# setable values
		lowerCamelCase							=		42
		lowerCamelCase							=		42
		lowerCamelCase							=		None



		@classmethod
		def 	lowerCAmelCase							(	cls							:       str   ,     UpperCamelCase__							:       CommonSchedulerState   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     )		->       List[Any]:






				"""simple docstring"""







				return cls(
				    common=__A   ,     final_alpha_cumprod=__A   ,     init_noise_sigma=__A   ,     timesteps=__A   ,     )







@dataclass
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		42







class      snake_case__    (	UpperCamelCase__  , UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		[e.name for e in FlaxKarrasDiffusionSchedulers]

		lowerCamelCase							=		42



		@property
		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Optional[Any]:






				"""simple docstring"""







				return True



		@register_to_config
		def __init__(	self							:       int   ,     UpperCamelCase__							:       int = 1000   ,     UpperCamelCase__							:       float = 0.0_001   ,     UpperCamelCase__							:       float = 0.02   ,     UpperCamelCase__							:       str = "linear"   ,     UpperCamelCase__							:       Optional[jnp.ndarray] = None   ,     UpperCamelCase__							:       bool = True   ,     UpperCamelCase__							:       int = 0   ,     UpperCamelCase__							:       str = "epsilon"   ,     UpperCamelCase__							:       jnp.dtype = jnp.floataa   ,     )		->       str:






				"""simple docstring"""







				snake_case   :    Dict			       =		dtype



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[CommonSchedulerState] = None	)		->       DDIMSchedulerState:






				"""simple docstring"""







				if common is None:
						snake_case   :    Optional[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.
				snake_case   :    Dict			       =		(
				    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
				snake_case   :    Tuple			       =		jnp.array(1.0   ,     dtype=self.dtype	)

				snake_case   :    Dict			       =		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							:       Optional[Any]   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       Optional[int] = None	)		->       jnp.ndarray:






				"""simple docstring"""







				return sample



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Tuple = ()	)		->       DDIMSchedulerState:






				"""simple docstring"""







				snake_case   :    Dict			       =		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   :    Dict			       =		(jnp.arange(0   ,     __A	) * step_ratio).round()[::-1] + self.config.steps_offset

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



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       str	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		state.common.alphas_cumprod[timestep]
				snake_case   :    Tuple			       =		jnp.where(
				    prev_timestep >= 0   ,     state.common.alphas_cumprod[prev_timestep]   ,     state.final_alpha_cumprod	)
				snake_case   :    Any			       =		1 - alpha_prod_t
				snake_case   :    Optional[Any]			       =		1 - alpha_prod_t_prev

				snake_case   :    str			       =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       float = 0.0   ,     UpperCamelCase__							:       bool = True   ,     )		->       Union[FlaxDDIMSchedulerOutput, Tuple]:






				"""simple docstring"""







				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)
				snake_case   :    Union[str, Any]			       =		timestep - self.config.num_train_timesteps // state.num_inference_steps

				snake_case   :    Dict			       =		state.common.alphas_cumprod
				snake_case   :    Tuple			       =		state.final_alpha_cumprod

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

				snake_case   :    str			       =		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":
						snake_case   :    List[Any]			       =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						snake_case   :    Optional[Any]			       =		model_output
				elif self.config.prediction_type == "sample":
						snake_case   :    Dict			       =		model_output
						snake_case   :    str			       =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
				elif self.config.prediction_type == "v_prediction":
						snake_case   :    Optional[Any]			       =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
						snake_case   :    Optional[int]			       =		(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)
				snake_case   :    Optional[int]			       =		self._get_variance(__A   ,     __A   ,     __A	)
				snake_case   :    List[Any]			       =		eta * variance ** 0.5

				# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case   :    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
				snake_case   :    List[Any]			       =		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							:       str   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     )		->       jnp.ndarray:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       DDIMSchedulerState   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     UpperCamelCase__							:       jnp.ndarray   ,     )		->       jnp.ndarray:






				"""simple docstring"""







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



		def __len__(	self							:       Tuple	)		->       str:






				"""simple docstring"""







				return self.config.num_train_timesteps









from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					Queue()
		queue.put(UpperCamelCase__			)
		while not queue.empty():
				SCREAMING_SNAKE_CASE__								=					queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.put(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					deque([start]			)
		while queue:
				SCREAMING_SNAKE_CASE__								=					queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.append(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					f'''from __main__ import G, {name}'''
		SCREAMING_SNAKE_CASE__								=					10_000
		SCREAMING_SNAKE_CASE__								=					timeit(f'''{name}(G, \'A\')'''    ,      setup=UpperCamelCase__    ,      number=UpperCamelCase__			)
		print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds