





'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   42

     # setable values
     snake_case_						      =   42
     snake_case_						      =   42
     snake_case_						      =   None



     @classmethod
     def      lowerCAmelCase			(   cls							:			int   ,       __snake_case							:			CommonSchedulerState   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			jnp.ndarray						)->	Optional[int]:
        return cls(common=__snake_case   ,       init_noise_sigma=__snake_case   ,       timesteps=__snake_case						)




@dataclass
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     snake_case_						      =   42



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



     @register_to_config
     def __init__(   self							:			List[str]   ,       __snake_case							:			int = 10_00   ,       __snake_case							:			float = 0.00_01   ,       __snake_case							:			float = 0.02   ,       __snake_case							:			str = "linear"   ,       __snake_case							:			Optional[jnp.ndarray] = None   ,       __snake_case							:			str = "fixed_small"   ,       __snake_case							:			bool = True   ,       __snake_case							:			str = "epsilon"   ,       __snake_case							:			jnp.dtype = jnp.floataa   ,       )->	List[str]:
        snake_case 						=   dtype



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Optional[CommonSchedulerState] = None						)->	DDPMSchedulerState:
        if common is None:
           snake_case 						=   CommonSchedulerState.create(self						)

        # standard deviation of the initial noise distribution
        snake_case 						=   jnp.array(1.0   ,       dtype=self.dtype						)

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

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



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			Optional[int] = None						)->	jnp.ndarray:
        return sample



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			int   ,       __snake_case							:			Tuple = ()						)->	DDPMSchedulerState:
        snake_case 						=   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 						=   (jnp.arange(0   ,       __snake_case						) * step_ratio).round()[::-1]

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



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			Dict   ,       __snake_case							:			int=None   ,       __snake_case							:			Dict=None						)->	Union[str, Any]:
        snake_case 						=   state.common.alphas_cumprod[t]
        snake_case 						=   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 						=   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
           snake_case 						=   self.config.variance_type

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

        return variance



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			int   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			Optional[jax.random.KeyArray] = None   ,       __snake_case							:			bool = True   ,       )->	Union[FlaxDDPMSchedulerOutput, Tuple]:
        snake_case 						=   timestep

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

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

        # 1. compute alphas, betas
        snake_case 						=   state.common.alphas_cumprod[t]
        snake_case 						=   jnp.where(t > 0   ,       state.common.alphas_cumprod[t - 1]   ,       jnp.array(1.0   ,       dtype=self.dtype						)						)
        snake_case 						=   1 - alpha_prod_t
        snake_case 						=   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 						=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
           snake_case 						=   model_output
        elif self.config.prediction_type == "v_prediction":
           snake_case 						=   (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 						=   jnp.clip(__snake_case   ,       -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 						=   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        snake_case 						=   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 						=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

        snake_case 						=   pred_prev_sample + variance

        if not return_dict:
           return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=__snake_case   ,       state=__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			jnp.ndarray   ,       )->	jnp.ndarray:
        return add_noise_common(state.common   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			DDPMSchedulerState   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			jnp.ndarray   ,       __snake_case							:			jnp.ndarray   ,       )->	jnp.ndarray:
        return get_velocity_common(state.common   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



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








'''simple docstring'''




from manim import *
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Union[str, Any]:
        snake_case 						=   Rectangle(height=0.5   ,       width=0.5						)
        snake_case 						=   Rectangle(height=0.25   ,       width=0.25						)
        snake_case 						=   Rectangle(height=0.46   ,       width=0.46						).set_stroke(width=0						)

        snake_case 						=   [mem.copy() for i in range(6						)]
        snake_case 						=   [mem.copy() for i in range(6						)]
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   VGroup(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   Text("""CPU"""   ,       font_size=24						)
        snake_case 						=   Group(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0.5   ,       aligned_edge=__snake_case						)
        cpu.move_to([-2.5, -0.5, 0]						)
        self.add(__snake_case						)

        snake_case 						=   [mem.copy() for i in range(4						)]
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   Text("""GPU"""   ,       font_size=24						)
        snake_case 						=   Group(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0.5   ,       aligned_edge=__snake_case						)
        gpu.move_to([-1, -1, 0]						)
        self.add(__snake_case						)

        snake_case 						=   [mem.copy() for i in range(6						)]
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)

        snake_case 						=   Text("""Model"""   ,       font_size=24						)
        snake_case 						=   Group(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0.5   ,       aligned_edge=__snake_case						)
        model.move_to([3, -1.0, 0]						)
        self.add(__snake_case						)

        snake_case 						=   []
        snake_case 						=   []
        snake_case 						=   []

        for i, rect in enumerate(__snake_case						):
           rect.set_stroke(__snake_case						)

           snake_case 						=   Rectangle(height=0.46 / 4   ,       width=0.46 / 3						).set_stroke(width=0.0						).set_fill(__snake_case   ,       opacity=0.7						)

           if i == 0:
              cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)   ,       buff=0.02   ,       direction=__snake_case						)
              cpu_target.set_x(cpu_target.get_x() + 0.1						)
           elif i == 3:
              cpu_target.next_to(model_cpu_arr[0]   ,       direction=__snake_case   ,       buff=0.0						)
           else:
              cpu_target.next_to(model_cpu_arr[i - 1]   ,       direction=__snake_case   ,       buff=0.0						)
           self.add(__snake_case						)
           model_cpu_arr.append(__snake_case						)

        self.add(*__snake_case   ,       *__snake_case   ,       *__snake_case						)

        snake_case 						=   [mem.copy() for i in range(6						)]
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)

        snake_case 						=   Text("""Loaded Checkpoint"""   ,       font_size=24						)
        snake_case 						=   Group(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0.5   ,       aligned_edge=__snake_case						)
        checkpoint.move_to([3, 0.5, 0]						)

        self.add(__snake_case						)

        snake_case 						=   []
        snake_case 						=   []

        for i, rect in enumerate(__snake_case						):
           snake_case 						=   fill.copy().set_fill(__snake_case   ,       opacity=0.7						)
           target.move_to(__snake_case						)
           ckpt_arr.append(__snake_case						)

           snake_case 						=   target.copy()
           if i < 5:
              cpu_target.move_to(cpu_left_col_base[i + 1]						)
           else:
              cpu_target.move_to(cpu_right_col_base[i - 5]						)
           ckpt_cpu_arr.append(__snake_case						)
        self.add(*__snake_case   ,       *__snake_case						)

        snake_case 						=   Square(side_length=2.2						)
        key.move_to([-5, 2, 0]						)

        snake_case 						=   MarkupText(
            f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   ,       font_size=18   ,       )

        key_text.move_to([-5, 2.4, 0]						)

        self.add(__snake_case   ,       __snake_case						)

        snake_case 						=   MarkupText(
            f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''   ,       font_size=18   ,       )

        blue_text.next_to(__snake_case   ,       DOWN * 2.4   ,       aligned_edge=key_text.get_left()						)
        self.add(__snake_case						)

        snake_case 						=   MarkupText(
            f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''   ,       font_size=24   ,       )
        step_a.move_to([2, 2, 0]						)

        snake_case 						=   [meta_mem.copy() for i in range(6						)]
        snake_case 						=   [meta_mem.copy() for i in range(6						)]
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   VGroup(*__snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   VGroup(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0						)
        snake_case 						=   Text("""Disk"""   ,       font_size=24						)
        snake_case 						=   Group(__snake_case   ,       __snake_case						).arrange(__snake_case   ,       buff=0.5   ,       aligned_edge=__snake_case						)
        disk.move_to([-4.0, -1.25, 0]						)
        self.play(Write(__snake_case   ,       run_time=3						)   ,       Write(__snake_case   ,       run_time=1						)   ,       Create(__snake_case   ,       run_time=1						)						)

        snake_case 						=   []
        for i, rect in enumerate(__snake_case						):
           snake_case 						=   rect.copy()
           target.generate_target()
           target.target.move_to(disk_left_col_base[i]						).scale(0.5						)
           animations.append(MoveToTarget(__snake_case   ,       run_time=1.5						)						)
        self.play(*__snake_case						)

        self.play(FadeOut(__snake_case						)						)

        snake_case 						=   MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''   ,       font_size=24						)
        step_a.move_to([2, 2, 0]						)

        self.play(Write(__snake_case   ,       run_time=3						)						)

        self.play(
            FadeOut(__snake_case   ,       __snake_case   ,       *__snake_case   ,       *__snake_case						)   ,       )

        self.wait()

