



"""simple docstring"""







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  UpperCamelCase__  :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	None
      _SCREAMING_SNAKE_CASE    							=	None
      _SCREAMING_SNAKE_CASE    							=	None



      @classmethod
      def 	SCREAMING_SNAKE_CASE__					( cls							: Dict			):
        return cls()







@dataclass
class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None







class  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        return True



      @register_to_config
      def __init__( self							: Any       ,     SCREAMING_SNAKE_CASE_							: int = 2_0_0_0       ,     SCREAMING_SNAKE_CASE_							: float = 0.15       ,     SCREAMING_SNAKE_CASE_							: float = 0.01       ,     SCREAMING_SNAKE_CASE_							: float = 1_3_4_8.0       ,     SCREAMING_SNAKE_CASE_							: float = 1E-5       ,     SCREAMING_SNAKE_CASE_							: int = 1       ,     ):
        pass



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_  :							Tuple								=					ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            UpperCAmelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: ScoreSdeVeSchedulerState       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Tuple = ()       ,     SCREAMING_SNAKE_CASE_							: float = None			):
        lowerCAmelCase_  :							List[str]								=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowerCAmelCase_  :							str								=					jnp.linspace(1       ,     UpperCAmelCase__       ,     UpperCAmelCase__			)
        return state.replace(timesteps=UpperCAmelCase__			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: ScoreSdeVeSchedulerState       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: float = None       ,     SCREAMING_SNAKE_CASE_							: float = None       ,     SCREAMING_SNAKE_CASE_							: float = None       ,     ):
        lowerCAmelCase_  :							Dict								=					sigma_min if sigma_min is not None else self.config.sigma_min
        lowerCAmelCase_  :							Any								=					sigma_max if sigma_max is not None else self.config.sigma_max
        lowerCAmelCase_  :							Optional[int]								=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
          lowerCAmelCase_  :							Union[str, Any]								=					self.set_timesteps(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__			)

        lowerCAmelCase_  :							int								=					jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase__			)       ,     jnp.log(UpperCAmelCase__			)       ,     UpperCAmelCase__			)			)
        lowerCAmelCase_  :							Optional[Any]								=					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

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



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: List[str]			):
        return jnp.where(timesteps == 0       ,     jnp.zeros_like(UpperCAmelCase__			)       ,     state.discrete_sigmas[timesteps - 1]			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any       ,     SCREAMING_SNAKE_CASE_							: ScoreSdeVeSchedulerState       ,     SCREAMING_SNAKE_CASE_							: jnp.ndarray       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: jnp.ndarray       ,     SCREAMING_SNAKE_CASE_							: random.KeyArray       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):
        if state.timesteps is None:
          raise ValueError(
              '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

        lowerCAmelCase_  :							str								=					timestep * jnp.ones(
            sample.shape[0]       ,     )
        lowerCAmelCase_  :							Optional[int]								=					(timestep * (len(state.timesteps			) - 1)).long()

        lowerCAmelCase_  :							List[Any]								=					state.discrete_sigmas[timesteps]
        lowerCAmelCase_  :							List[Any]								=					self.get_adjacent_sigma(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__			)
        lowerCAmelCase_  :							str								=					jnp.zeros_like(UpperCAmelCase__			)
        lowerCAmelCase_  :							Optional[Any]								=					(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
        lowerCAmelCase_  :							str								=					diffusion.flatten()
        lowerCAmelCase_  :							Optional[int]								=					broadcast_to_shape_from_left(UpperCAmelCase__       ,     sample.shape			)
        lowerCAmelCase_  :							List[str]								=					drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowerCAmelCase_  :							Optional[int]								=					random.split(UpperCAmelCase__       ,     num=1			)
        lowerCAmelCase_  :							List[Any]								=					random.normal(key=UpperCAmelCase__       ,     shape=sample.shape			)
        lowerCAmelCase_  :							Optional[int]								=					sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowerCAmelCase_  :							Any								=					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=UpperCAmelCase__       ,     prev_sample_mean=UpperCAmelCase__       ,     state=UpperCAmelCase__			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: ScoreSdeVeSchedulerState       ,     SCREAMING_SNAKE_CASE_							: jnp.ndarray       ,     SCREAMING_SNAKE_CASE_							: jnp.ndarray       ,     SCREAMING_SNAKE_CASE_							: random.KeyArray       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):
        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
        lowerCAmelCase_  :							Union[str, Any]								=					random.split(UpperCAmelCase__       ,     num=1			)
        lowerCAmelCase_  :							Any								=					random.normal(key=UpperCAmelCase__       ,     shape=sample.shape			)

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

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=UpperCAmelCase__       ,     state=UpperCAmelCase__			)





      def __len__( self							: int			):
        return self.config.num_train_timesteps








"""simple docstring"""





import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Tuple  						=      "config.json"


 @register_to_config
 def __init__(     self				:     List[str],   UpperCAmelCase__				:     Dict=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     Dict="for diffusion",   UpperCAmelCase__				:     Optional[int]=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     List[str]=2,   UpperCAmelCase__				:     List[str]=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Union[str, Any]=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     Optional[int]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   UpperCAmelCase__				:     Dict=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Any=2,   UpperCAmelCase__				:     Union[str, Any]=5,   UpperCAmelCase__				:     Optional[Any]=(2, 5),   UpperCAmelCase__				:     List[str]="for diffusion",   UpperCAmelCase__				:     Optional[Any]=[1, 5],   UpperCAmelCase__				:     Dict=[5, 4],   ):
    pass







class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    with self.assertRaises(UpperCAmelCase__      ):
       ConfigMixin.load_config("dummy_path"      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # init ignore private arguments
    __lowercase								=		SampleObject(_name_or_path="lalala"      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can override default
    __lowercase								=		SampleObject(c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can use positional arguments.
    __lowercase								=		SampleObject(1,   c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 1
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config

    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    with tempfile.TemporaryDirectory() as tmpdirname:
       obj.save_config(UpperCAmelCase__      )
       __lowercase								=		SampleObject.from_config(SampleObject.load_config(UpperCAmelCase__      )      )
       __lowercase								=		new_obj.config

    # unfreeze configs
    __lowercase								=		dict(UpperCAmelCase__      )
    __lowercase								=		dict(UpperCAmelCase__      )

    assert config.pop("c"      ) == (2, 5)  # instantiated as tuple
    assert new_config.pop("c"      ) == [2, 5]  # saved & loaded as list because of json
    config.pop("_use_default_values"      )
    assert config == new_config


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDIMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert ddim.__class__ == DDIMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     int      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerAncestralDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerAncestralDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		PNDMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert pndm.__class__ == PNDMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDPMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler",   prediction_type="sample",   beta_end=8,   )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger_a:
       __lowercase								=		DDPMScheduler.from_pretrained("google/ddpm-celebahq-256",   beta_start=8_8      )

    assert ddpm.__class__ == DDPMScheduler
    assert ddpm.config.prediction_type == "sample"
    assert ddpm.config.beta_end == 8
    assert ddpm_a.config.beta_start == 8_8

    # no warning should be thrown
    assert cap_logger.out == ""
    assert cap_logger_a.out == ""


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DPMSolverMultistepScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert dpm.__class__ == DPMSolverMultistepScheduler
    # no warning should be thrown
    assert cap_logger.out == ""



 def    _lowercase					(     self				:     str      ):
    # let's first save a config that should be in the form
    #    a=2,
    #    b=5,
    #    c=(2, 5),
    #    d="for diffusion",
    #    e=[1, 3],

    __lowercase								=		SampleObject()

    __lowercase								=		{k: v for k, v in config.config.items() if not k.startswith("_"      )}

    # make sure that default config has all keys in `_use_default_values`
    assert set(config_dict.keys()      ) == set(config.config._use_default_values      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       config.save_config(UpperCAmelCase__      )

       # now loading it with SampleObject2 should put f into `_use_default_values`
       __lowercase								=		SampleObjecta.from_config(UpperCAmelCase__      )

       assert "f" in config._use_default_values
       assert config.f == [1, 3]

    # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
    # **BECAUSE** it is part of `config._use_default_values`
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config.f == [5, 4]

    config.config._use_default_values.pop()
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config_a.f == [1, 3]

    # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
    assert new_config_a.e == [1, 3]




