



# 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 				a_    :
						UpperCamelCase__ :		CommonSchedulerState
						UpperCamelCase__ :		jnp.ndarray

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

						@classmethod
						def       __a			(       cls      :Tuple  ,		_lowercase      :CommonSchedulerState  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		)						->   Optional[Any]:
												return cls(
												    common=_lowercase  ,		final_alpha_cumprod=_lowercase  ,		init_noise_sigma=_lowercase  ,		timesteps=_lowercase  ,		)







@dataclass
class 				a_    (						_snake_case							):
						UpperCamelCase__ :		DDIMSchedulerState







class 				a_    (						_snake_case   ,					_snake_case							):
						UpperCamelCase__ :		List[str]										=[e.name for e in FlaxKarrasDiffusionSchedulers]

						UpperCamelCase__ :		jnp.dtype

						@property
						def       __a			(       self      :Any)						->   str:
												return True

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

						def       __a			(       self      :Optional[int]  ,		_lowercase      :Optional[CommonSchedulerState] = None)						->   DDIMSchedulerState:
												if common is None:
																		UpperCAmelCase_					=	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.
												UpperCAmelCase_					=	(
												    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
												UpperCAmelCase_					=	jnp.array(1.0  ,		dtype=self.dtype)

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

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

						def       __a			(       self      :Tuple  ,		_lowercase      :DDIMSchedulerState  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :Optional[int] = None)						->   jnp.ndarray:
												return sample

						def       __a			(       self      :Any  ,		_lowercase      :DDIMSchedulerState  ,		_lowercase      :int  ,		_lowercase      :Tuple = ())						->   DDIMSchedulerState:
												UpperCAmelCase_					=	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
												UpperCAmelCase_					=	(jnp.arange(0  ,		_lowercase) * step_ratio).round()[::-1] + self.config.steps_offset

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

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

												UpperCAmelCase_					=	(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance

						def       __a			(       self      :List[Any]  ,		_lowercase      :DDIMSchedulerState  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :int  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :float = 0.0  ,		_lowercase      :bool = True  ,		)						->   Union[FlaxDDIMSchedulerOutput, Tuple]:
												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)
												UpperCAmelCase_					=	timestep - self.config.num_train_timesteps // state.num_inference_steps

												UpperCAmelCase_					=	state.common.alphas_cumprod
												UpperCAmelCase_					=	state.final_alpha_cumprod

												# 2. compute alphas, betas
												UpperCAmelCase_					=	alphas_cumprod[timestep]
												UpperCAmelCase_					=	jnp.where(prev_timestep >= 0  ,		alphas_cumprod[prev_timestep]  ,		_lowercase)

												UpperCAmelCase_					=	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":
																		UpperCAmelCase_					=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		UpperCAmelCase_					=	model_output
												elif self.config.prediction_type == "sample":
																		UpperCAmelCase_					=	model_output
																		UpperCAmelCase_					=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																		UpperCAmelCase_					=	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		UpperCAmelCase_					=	(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)
												UpperCAmelCase_					=	self._get_variance(_lowercase  ,		_lowercase  ,		_lowercase)
												UpperCAmelCase_					=	eta * variance ** 0.5

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												UpperCAmelCase_					=	(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
												UpperCAmelCase_					=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																		return (prev_sample, state)

												return FlaxDDIMSchedulerOutput(prev_sample=_lowercase  ,		state=_lowercase)

						def       __a			(       self      :List[str]  ,		_lowercase      :DDIMSchedulerState  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		)						->   jnp.ndarray:
												return add_noise_common(state.common  ,		_lowercase  ,		_lowercase  ,		_lowercase)

						def       __a			(       self      :Dict  ,		_lowercase      :DDIMSchedulerState  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		_lowercase      :jnp.ndarray  ,		)						->   jnp.ndarray:
												return get_velocity_common(state.common  ,		_lowercase  ,		_lowercase  ,		_lowercase)




						def __len__(       self      :str)						->   List[str]:
												return self.config.num_train_timesteps










import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def 							A     (						__UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						class 				a_    :

												def __init__(       self      :Optional[int]  ,		_lowercase      :Any)						->   None:
																		assert isinstance(_lowercase  ,		(bytes, str))
																		UpperCAmelCase_					=	content




												def       __a			(       self      :List[str])						->   Tuple:
																		return json.loads(self.content)

						def mock_response(*__UpperCAmelCase				,   **__UpperCAmelCase      ):
												assert args[0] == AUTHENTICATED_USER_ENDPOINT
												assert "Authorization" in kwargs["headers"]
												assert kwargs["headers"]["Authorization"].startswith('''token '''      )
												assert "Accept" in kwargs["headers"]
												return FakeResponse(b'''{"login":"test","id":1}'''      )

						monkeypatch.setattr(__UpperCAmelCase				,   '''get'''				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	fetch_github_info('''token'''      )
						assert result["login"] == "test"
						assert result["id"] == 1





