





'''simple docstring'''

lowercase_			       =				"""
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowercase_			       =				[{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowercase_			       =				{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}










'''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       a_  :

    '''simple docstring'''
    UpperCamelCase									=				42
    UpperCamelCase									=				42

    # setable values
    UpperCamelCase									=				42
    UpperCamelCase									=				42
    UpperCamelCase									=				None


    @classmethod
    def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
        return cls(
            common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)




@dataclass
class       a_  (       snake_case_ ):

    '''simple docstring'''
    UpperCamelCase									=				42




class       a_  (       snake_case_   ,					snake_case_ ):

    '''simple docstring'''
    UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCamelCase									=				42


    @property
    def     snake_case_(	self )	->     List[Any]:
        return True


    @register_to_config
    def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
        _SCREAMING_SNAKE_CASE             =    dtype


    def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
        if common is None:
            _SCREAMING_SNAKE_CASE             =    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.
        _SCREAMING_SNAKE_CASE             =    (
            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
        _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )

        _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
        return sample


    def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
        _SCREAMING_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
        _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset

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


    def     snake_case_(	self				,	A				,	A				,	A )	->     int:
        _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
        _SCREAMING_SNAKE_CASE             =    jnp.where(
            prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
        _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
        _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev

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

        return variance


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = 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)
        _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps

        _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
        _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod

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

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

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _SCREAMING_SNAKE_CASE             =    (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
        _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
        return add_noise_common(state.common				,	A				,	A				,	A )


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
        return get_velocity_common(state.common				,	A				,	A				,	A )





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



