





'''simple docstring'''


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class    a_     :
     __A  				=    42

     # setable values
     __A  				=    42
     __A  				=    42
     __A  				=    42
     __A  				=    None

     # running values
     __A  				=    None





     @classmethod
     def 							lowercase__   (				cls	:      Dict			,   lowercase	:      CommonSchedulerState			,   lowercase	:      jnp.ndarray			,   lowercase	:      jnp.ndarray			,   lowercase	:      jnp.ndarray		):





        """simple docstring"""




        return cls(common=lowercase			,   init_noise_sigma=lowercase			,   timesteps=lowercase			,   sigmas=lowercase		)







@dataclass
class    a_     (       _lowerCAmelCase							):
     __A  				=    42







class    a_     (       _lowerCAmelCase			,		_lowerCAmelCase							):
     __A  				=    [e.name for e in FlaxKarrasDiffusionSchedulers]

     __A  				=    42





     @property
     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        return True





     @register_to_config
     def __init__(				self	:      Union[str, Any]			,   lowercase	:      int = 1_000			,   lowercase	:      float = 0.00_01			,   lowercase	:      float = 0.02			,   lowercase	:      str = "linear"			,   lowercase	:      Optional[jnp.ndarray] = None			,   lowercase	:      str = "epsilon"			,   lowercase	:      jnp.dtype = jnp.floataa			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				dtype





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      Optional[CommonSchedulerState] = None		):





        """simple docstring"""




        if common is None:
           lowercase_    :int					    =				CommonSchedulerState.create(self		)

        lowercase_    :Union[str, Any]					    =				jnp.arange(0			,   self.config.num_train_timesteps		).round()[::-1]
        lowercase_    :List[Any]					    =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        lowercase_    :Optional[Any]					    =				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowercase			,   init_noise_sigma=lowercase			,   timesteps=lowercase			,   sigmas=lowercase			,   )





     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      LMSDiscreteSchedulerState			,   lowercase	:      jnp.ndarray			,   lowercase	:      int		):





        """simple docstring"""




        ((lowercase_)    ,							)    :Any					    =				jnp.where(state.timesteps == timestep			,   size=1		)
        lowercase_    :Any					    =				step_index[0]

        lowercase_    :Union[str, Any]					    =				state.sigmas[step_index]
        lowercase_    :Optional[int]					    =				sample / ((sigma**2 + 1) ** 0.5)
        return sample





     def 							lowercase__   (				self	:      int			,   lowercase	:      LMSDiscreteSchedulerState			,   lowercase	:      Tuple			,   lowercase	:      Dict			,   lowercase	:      int		):





        """simple docstring"""




        def lms_derivative(lowercase	:      Optional[int]		):
           lowercase_    :List[str]					    =				1.0
           for k in range(lowercase		):
              if current_order == k:
                 continue
              prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
           return prod

        lowercase_    :Any					    =				integrate.quad(lowercase			,   state.sigmas[t]			,   state.sigmas[t + 1]			,   epsrel=1e-4		)[0]

        return integrated_coeff





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      LMSDiscreteSchedulerState			,   lowercase	:      int			,   lowercase	:      Tuple = ()		):





        """simple docstring"""




        lowercase_    :Any					    =				jnp.linspace(self.config.num_train_timesteps - 1			,   0			,   lowercase			,   dtype=self.dtype		)

        lowercase_    :List[str]					    =				jnp.floor(lowercase		).astype(jnp.intaa		)
        lowercase_    :str					    =				jnp.ceil(lowercase		).astype(jnp.intaa		)

        lowercase_    :Any					    =				jnp.mod(lowercase			,   1.0		)

        lowercase_    :Optional[int]					    =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        lowercase_    :Optional[int]					    =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        lowercase_    :List[str]					    =				jnp.concatenate([sigmas, jnp.array([0.0]			,   dtype=self.dtype		)]		)

        lowercase_    :int					    =				timesteps.astype(jnp.intaa		)

        # initial running values
        lowercase_    :int					    =				jnp.zeros((0,) + shape			,   dtype=self.dtype		)

        return state.replace(
            timesteps=lowercase			,   sigmas=lowercase			,   num_inference_steps=lowercase			,   derivatives=lowercase			,   )





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      LMSDiscreteSchedulerState			,   lowercase	:      jnp.ndarray			,   lowercase	:      int			,   lowercase	:      jnp.ndarray			,   lowercase	:      int = 4			,   lowercase	:      bool = True			,   ):





        """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"		)

        lowercase_    :Dict					    =				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
           lowercase_    :Optional[Any]					    =				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase_    :Any					    =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'		)

        # 2. Convert to an ODE derivative
        lowercase_    :Optional[int]					    =				(sample - pred_original_sample) / sigma
        lowercase_    :int					    =				state.replace(derivatives=jnp.append(state.derivatives			,   lowercase		)		)
        if len(state.derivatives		) > order:
           lowercase_    :List[Any]					    =				state.replace(derivatives=jnp.delete(state.derivatives			,   0		)		)

        # 3. Compute linear multistep coefficients
        lowercase_    :Optional[int]					    =				min(timestep + 1			,   lowercase		)
        lowercase_    :Any					    =				[self.get_lms_coefficient(lowercase			,   lowercase			,   lowercase			,   lowercase		) for curr_order in range(lowercase		)]

        # 4. Compute previous sample based on the derivatives path
        lowercase_    :List[Any]					    =				sample + sum(
            coeff * derivative for coeff, derivative in zip(lowercase			,   reversed(state.derivatives		)		)		)

        if not return_dict:
           return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowercase			,   state=lowercase		)





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      LMSDiscreteSchedulerState			,   lowercase	:      jnp.ndarray			,   lowercase	:      jnp.ndarray			,   lowercase	:      jnp.ndarray			,   ):





        """simple docstring"""




        lowercase_    :Tuple					    =				state.sigmas[timesteps].flatten()
        lowercase_    :Any					    =				broadcast_to_shape_from_left(lowercase			,   noise.shape		)

        lowercase_    :Optional[Any]					    =				original_samples + noise * sigma

        return noisy_samples







     def __len__(				self	:      Union[str, Any]		):





        """simple docstring"""




        return self.config.num_train_timesteps









'''simple docstring'''


import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]				):
   return EnvironmentCommand()


class    a_     (       _lowerCAmelCase							):





     @staticmethod
     def 							lowercase__   (				lowercase	:      ArgumentParser		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				parser.add_parser("env"			,   help="Print relevant system environment info."		)
        download_parser.set_defaults(func=lowercase		)





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :Tuple					    =				{
            "`datasets` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "PyArrow version": pyarrow.__version__,
            "Pandas version": pandas.__version__,
        }

        print("\nCopy-and-paste the text below in your GitHub issue.\n"		)
        print(self.format_dict(lowercase		)		)

        return info







     @staticmethod
     def 							lowercase__   (				lowercase	:      Dict		):





        """simple docstring"""




        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"


