

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 					A__:
						"""simple docstring"""






						_A						:							Optional[jnp.ndarray]									= None
						_A						:							Optional[jnp.ndarray]									= None
						_A						:							Optional[jnp.ndarray]									= None





						@classmethod
						def  UpperCamelCase__				(						cls						)    ->   Optional[Any]:
									return cls()







@dataclass
class 					A__(lowerCAmelCase__       ):
						"""simple docstring"""






						_A						:							ScoreSdeVeSchedulerState
						_A						:							jnp.ndarray
						_A						:							Optional[jnp.ndarray]									= None







class 					A__(lowerCAmelCase__,     lowerCAmelCase__       ):
						"""simple docstring"""






						@property
						def  UpperCamelCase__				(						self						)    ->   Dict:
									return True





						@register_to_config
						def __init__(						self     ,			_lowercase = 2_000     ,			_lowercase = 0.1_5     ,			_lowercase = 0.0_1     ,			_lowercase = 1_348.0     ,			_lowercase = 1e-5     ,			_lowercase = 1     ,			)    ->   Optional[Any]:
									pass





						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									a_	:	int          =   ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    lowerCAmelCase_     ,			self.config.num_train_timesteps     ,			self.config.sigma_min     ,			self.config.sigma_max     ,			self.config.sampling_eps     ,			)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = ()     ,			_lowercase = None						)    ->   ScoreSdeVeSchedulerState:
									a_	:	int          =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

									a_	:	int          =   jnp.linspace(1     ,			lowerCAmelCase_     ,			lowerCAmelCase_						)
									return state.replace(timesteps=lowerCAmelCase_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			)    ->   ScoreSdeVeSchedulerState:
									a_	:	int          =   sigma_min if sigma_min is not None else self.config.sigma_min
									a_	:	List[Any]          =   sigma_max if sigma_max is not None else self.config.sigma_max
									a_	:	List[Any]          =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
												a_	:	Tuple          =   self.set_timesteps(lowerCAmelCase_     ,			lowerCAmelCase_     ,			lowerCAmelCase_						)

									a_	:	Union[str, Any]          =   jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase_						)     ,			jnp.log(lowerCAmelCase_						)     ,			lowerCAmelCase_						)						)
									a_	:	List[str]          =   jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)

									return state.replace(discrete_sigmas=lowerCAmelCase_     ,			sigmas=lowerCAmelCase_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase						)    ->   List[Any]:
									return jnp.where(timesteps == 0     ,			jnp.zeros_like(lowerCAmelCase_						)     ,			state.discrete_sigmas[timesteps - 1]						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase = True     ,			)    ->   Union[FlaxSdeVeOutput, Tuple]:






									if state.timesteps is None:
												raise ValueError(
												    """`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""						)

									a_	:	Optional[Any]          =   timestep * jnp.ones(
									    sample.shape[0]     ,			)
									a_	:	Dict          =   (timestep * (len(state.timesteps						) - 1)).long()

									a_	:	int          =   state.discrete_sigmas[timesteps]
									a_	:	Optional[int]          =   self.get_adjacent_sigma(lowerCAmelCase_     ,			lowerCAmelCase_     ,			lowerCAmelCase_						)
									a_	:	Optional[int]          =   jnp.zeros_like(lowerCAmelCase_						)
									a_	:	Optional[int]          =   (sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									a_	:	str          =   diffusion.flatten()
									a_	:	str          =   broadcast_to_shape_from_left(lowerCAmelCase_     ,			sample.shape						)
									a_	:	Tuple          =   drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									a_	:	Optional[Any]          =   random.split(lowerCAmelCase_     ,			num=1						)
									a_	:	Dict          =   random.normal(key=lowerCAmelCase_     ,			shape=sample.shape						)
									a_	:	Tuple          =   sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									a_	:	Tuple          =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
												return (prev_sample, prev_sample_mean, state)

									return FlaxSdeVeOutput(prev_sample=lowerCAmelCase_     ,			prev_sample_mean=lowerCAmelCase_     ,			state=lowerCAmelCase_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase = True     ,			)    ->   Union[FlaxSdeVeOutput, Tuple]:






									if state.timesteps is None:
												raise ValueError(
												    """`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""						)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									a_	:	Any          =   random.split(lowerCAmelCase_     ,			num=1						)
									a_	:	Optional[int]          =   random.normal(key=lowerCAmelCase_     ,			shape=sample.shape						)

									# compute step size from the model_output, the noise, and the snr
									a_	:	int          =   jnp.linalg.norm(lowerCAmelCase_						)
									a_	:	Optional[int]          =   jnp.linalg.norm(lowerCAmelCase_						)
									a_	:	Union[str, Any]          =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
									a_	:	str          =   step_size * jnp.ones(sample.shape[0]						)

									# compute corrected sample: model_output term and noise term
									a_	:	List[str]          =   step_size.flatten()
									a_	:	str          =   broadcast_to_shape_from_left(lowerCAmelCase_     ,			sample.shape						)
									a_	:	Any          =   sample + step_size * model_output
									a_	:	int          =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
												return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=lowerCAmelCase_     ,			state=lowerCAmelCase_						)





						def __len__(						self						)    ->   Dict:
									return self.config.num_train_timesteps





from collections import defaultdict


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			a_	:	defaultdict[str, int]          =   defaultdict(a__)
			# Creating a dictionary containing count of each word
			for word in sentence.split():
						occurrence[word] += 1
			return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(F"""{word}: {count}""")


